This study applies mathematical linguistics to explore how language distance plays an essential role in third language acquisition in terms of a morphosyntactic module. Data were drawn from 3410 essays written in Japanese by low, middle and high levels of learners from 12 first-language (L1) backgrounds who acquire English as a second language (L2)-interlanguage and Japanese as a third language (L3). The findings indicate that (a) mean dependency distance is an efficient indicator for syntactic complexity of writing proficiency. In both elementary and intermediate groups, learners of highly agglutinative languages are likely to show higher dependency distance than learners from isolated-language and fusion-language backgrounds. (b) The frequency and dependency distance are distributed in Power Law Function. Fitting Right truncated Good to the dependency distances indicates that the values of the parameter p ascend as the degree of agglutination of learners’ mother tongue increases. (c) The syntactic complexity in multi-background Japanese learners’ essays highlights that no matter how diverse the learners’ native and target languages are, the syntax is always constrained by universal law, namely, minimising dependency distance. This is in accordance with existing findings in second language acquisition of inflectional languages.
Alderson´s question revisited: Is reading in a foreign language a language pr...B L
A non-experimental correlational research was carried out to study the relationships between reading comprehension of EFL, grammatical knowledge of EFL and reading comprehension of Spanish as a first language of 1.059 freshman engineering and basic sciences students at a South American university.
How lexicon is represented in the mind in the bilinguals still attracts the scholars’ interest. A variety of experiments, in different methodologies under different theoretical framework, were conducted, producing different results. This study used the data from Jiang (1999) to duplicate a masked translation lexical decision task experiment, aiming at examining the asymmetry effect in the proficient Chinese English learners studying in Singapore. The results did not show the existence of L1-L2 priming effect assured in the previous studies but see the L2-L1 priming effect as reported in Jiang (1999).
Alderson´s question revisited: Is reading in a foreign language a language pr...B L
A non-experimental correlational research was carried out to study the relationships between reading comprehension of EFL, grammatical knowledge of EFL and reading comprehension of Spanish as a first language of 1.059 freshman engineering and basic sciences students at a South American university.
How lexicon is represented in the mind in the bilinguals still attracts the scholars’ interest. A variety of experiments, in different methodologies under different theoretical framework, were conducted, producing different results. This study used the data from Jiang (1999) to duplicate a masked translation lexical decision task experiment, aiming at examining the asymmetry effect in the proficient Chinese English learners studying in Singapore. The results did not show the existence of L1-L2 priming effect assured in the previous studies but see the L2-L1 priming effect as reported in Jiang (1999).
A NEW METHOD OF TEACHING FIGURATIVE EXPRESSIONS TOIRANIAN LANGUAGE LEARNERScscpconf
In teaching languages, if we only consider direct relationship between form and meaning in language and leave psycholinguistic aside, this approach is not a successful practice and language learners won't be able to make a successful relation in the real contexts. The present study intends to answer this question: is the teaching method in which salient meaning is taught more successful than the method in which literal or figurative meaning is taught or not? To answer the research question, 30 students were selected. Every ten people are formed as a group and three such groups were formed. Twenty figurative expressions were taught to every group. Group one was taught the figurative meaning of every expression. Group two was taught the literal meaning and group three was taught the salient meaning. Then three groups were tested. After analyzing data, we concluded that there was a significant difference in mean grades between classes and the class trained under graded salience hypothesis was more successful. This shows that traditional teaching methods must be revised.
Language transfer can be classified into negative transfer and positive transfer. The former is caused by the similarities shared by source language and target language, the latter is attributed to the differences between two languages. Linguists abroad and home have put forward that native language can promote students’ understanding of second language (Jarvis and Pavlenko, 2008; Wen, 2010). In the process of second language learning, especially for Junior High School students, the knowledge of native language can help students to complete their tasks. When learning the target language, they will unconsciously use the previous information to think and achieve the purpose of second language learning. Native language plays a fundamental role in second language learning. For students, it can facilitate the study of second language in some extent. Vocabulary is the foundation of language. As the beginning stage, English teaching in Junior high school should give priority to vocabulary learning. Then, whether Chinese plays a facilitate role in the process of students’ vocabulary learning? And if it has, what are the factors that influence language transfer? What teaching methods can teacher employed to students’ vocabulary learning? These are main contents of this study.
The Effects of Two Languages in One Mind: First Language AttritionMattia Zingaretti
An introduction to the phenomenon of first language attrition with interactive materials, literature findings and useful resources for students and researchers. This is an Open Educational Resource (OER) co-created by Roberta Spelorzi and Mattia Zingaretti, PhD Researchers at the University of Edinburgh. This OER is licensed under Creative Commons Attribution-ShareAlike, allowing viewers to download, use and make changes to the materials, providing attribution to the authors and distributing the content under the same license as the original.
The Impact of L1 Interference on Second Language Learning A Case Study of Fan...ijtsrd
This study explores the complex dynamics of first language interference in second language learning, with an emphasis on how it affects Ghanaian Fante students learning English. To address this, the study adopted a thematic analysis through semi structured questions that involved 20 junior high school students in the central region of Ghana. Utilising a word association task, the studys results revealed significant contributing factors such as confidence, lexical knowledge, and translation L1 interference , which substantially strengthened our understanding of its influence on second language learning among Fante speakers. This study significantly advances the subject by offering complex insights into the challenges and implications associated with L1 interference in language learning among Fante students in Ghana. These findings provide educators, stakeholders, management, governments, policymakers, and researchers with fresh insights into second language acquisition in Ghana and the struggles of Fante students in learning the second language, English. Zhang Beizhen | Dowuona Petrina Naa Narkie "The Impact of L1 Interference on Second Language Learning: A Case Study of Fante Second Language Learners of English" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd62385.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/62385/the-impact-of-l1-interference-on-second-language-learning-a-case-study-of-fante-second-language-learners-of-english/zhang-beizhen
A HISTORICAL DEVELOPMENT OF CONTRASTIVE ANALYSIS: A RELEVANT REVIEW IN SECOND...ijejournal
Contrastive analysis (CA) was primarily used in the 1950’s as an effective means to address second or
foreign language teaching and learning. In this context, it was used to compare pairs of languages, identify
similarities and differences in order to predict learning difficulties, with the ultimate goal of addressing
them (Fries, 1943; Lado, 1957). Yet, in the 1980’s and 1990’s the relevance of CA has been disputed.
Many studies have pointed out the limit of CA with respect to its weak and strong versions (Oller and
Ziahosseiny, 1970), (Wardhaugh, 1970) (Brown, 1989), (Hughes, 1980), (Yang, 1992), and (Whitman and
Jackson, 1972). To answer the limits of CA with regards to its weak, strong, and moderate versions, many
language teachers used CA with a new approach. Kupferberg and Olshtain (1996), James (1996), and
Ruzhekova-Rogozherova (2007). Here, salient contrastive linguistic input (CLI) is presented to learners for
an effective noticing. Yet, mere exposition of contrastive linguistic input to learners may not be enough for
effective acquisition to occur. Hence, Djiguimkoudre (2020) proposed structured phonemic awareness
activities to further strengthen such contrastive salient linguistic input when phonetics and phonology are
involved. When grammar is involved, the processing instruction (PI) model of Lee and
VanPatten (2003) is recommended since the types of activities that result in PI are believed to incite
effective noticing for intake.
. . . all human languages do share the same structure. More e.docxadkinspaige22
. . . all human languages do share the same structure. More explicitly: they have
essentially the same primitive elements and rules of composition . . . , although
of course there may be variations, such as the obvious ones derived from the
arbitrary association between sounds and meanings . . .
(Moro, 2016, p. 15)
3.1 Introduction
In this chapter, we start to consider individual theoretical perspectives on
L2 learning in greater detail. Our first topic is the Universal Grammar (UG)
approach (the generative linguistics approach), developed by the American
linguist Noam Chomsky and numerous followers over the last few decades.
This has been the most influential linguistic theory in the field, and has
inspired a wealth of publications (for full-length treatments, see Hawkins,
2001; Herschensohn, 2000; Lardiere, 2007; Leung, 2009; Slabakova, 2016;
Snape & Kupisch, 2016; Thomas, 2004; White, 2003; Whong, Gil, & Mars-
den, 2013).
The main aim of linguistic theory is twofold: firstly, to characterize what
human languages are like (descriptive adequacy), and, secondly, to explain
why they are that way (explanatory adequacy). In terms of L2 acquisition,
a linguistic approach sets out to describe the evolving language produced
by L2 learners, and to explain its characteristics. UG is therefore a prop-
erty theory (as defined in Chapter 1); that is, it attempts to characterize
the underlying linguistic knowledge in L2 learners’ minds. In contrast, a
detailed examination of the learning process itself (transition theory) will
be the main concern of the cognitive approaches which we describe in
Chapters 4 and 5.
First in this chapter, we will give a broad definition of the aims of the
Chomskyan tradition in linguistic research, in order to identify the aspects
of second language acquisition (SLA) to which this tradition is most relevant.
Secondly, we will examine the concept of UG itself in some detail, and
finally we will consider its application in L2 learning research.
3 Linguistics and Language
Learning
The Universal Grammar
Approach
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EBSCO Publishing : eBook Collection (EBSCOhost) - printed on 4/6/2020 6:13 AM via UNIVERSITY OF ESSEX
AN: 2006630 ; Mitchell, Rosamond, Myles, Florence, Marsden, Emma.; Second Language Learning Theories : Fourth Edition
Account: s9814295.main.ehost
82 Linguistics and Language Learning
3.2 Why a Universal Grammar?
3.2.1 Aims of Linguistic Research
The main goals of linguistic theory, as defined by Chomsky (1986a), are to
answer three basic questions about human language:
1. What constitutes knowledge of language?
2.
. . . all human languages do share the same structure. More e.docxShiraPrater50
. . . all human languages do share the same structure. More explicitly: they have
essentially the same primitive elements and rules of composition . . . , although
of course there may be variations, such as the obvious ones derived from the
arbitrary association between sounds and meanings . . .
(Moro, 2016, p. 15)
3.1 Introduction
In this chapter, we start to consider individual theoretical perspectives on
L2 learning in greater detail. Our first topic is the Universal Grammar (UG)
approach (the generative linguistics approach), developed by the American
linguist Noam Chomsky and numerous followers over the last few decades.
This has been the most influential linguistic theory in the field, and has
inspired a wealth of publications (for full-length treatments, see Hawkins,
2001; Herschensohn, 2000; Lardiere, 2007; Leung, 2009; Slabakova, 2016;
Snape & Kupisch, 2016; Thomas, 2004; White, 2003; Whong, Gil, & Mars-
den, 2013).
The main aim of linguistic theory is twofold: firstly, to characterize what
human languages are like (descriptive adequacy), and, secondly, to explain
why they are that way (explanatory adequacy). In terms of L2 acquisition,
a linguistic approach sets out to describe the evolving language produced
by L2 learners, and to explain its characteristics. UG is therefore a prop-
erty theory (as defined in Chapter 1); that is, it attempts to characterize
the underlying linguistic knowledge in L2 learners’ minds. In contrast, a
detailed examination of the learning process itself (transition theory) will
be the main concern of the cognitive approaches which we describe in
Chapters 4 and 5.
First in this chapter, we will give a broad definition of the aims of the
Chomskyan tradition in linguistic research, in order to identify the aspects
of second language acquisition (SLA) to which this tradition is most relevant.
Secondly, we will examine the concept of UG itself in some detail, and
finally we will consider its application in L2 learning research.
3 Linguistics and Language
Learning
The Universal Grammar
Approach
C
o
p
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r
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EBSCO Publishing : eBook Collection (EBSCOhost) - printed on 4/6/2020 6:13 AM via UNIVERSITY OF ESSEX
AN: 2006630 ; Mitchell, Rosamond, Myles, Florence, Marsden, Emma.; Second Language Learning Theories : Fourth Edition
Account: s9814295.main.ehost
82 Linguistics and Language Learning
3.2 Why a Universal Grammar?
3.2.1 Aims of Linguistic Research
The main goals of linguistic theory, as defined by Chomsky (1986a), are to
answer three basic questions about human language:
1. What constitutes knowledge of language?
2 ...
Effect of Query Formation on Web Search Engine Resultskevig
Query in a search engine is generally based on natural language. A query can be expressed in more than
one way without changing its meaning as it depends on thinking of human being at a particular moment.
Aim of the searcher is to get most relevant results immaterial of how the query has been expressed. In the
present paper, we have examined the results of search engine for change in coverage and similarity of first
few results when a query is entered in two semantically same but in different formats. Searching has been
made through Google search engine. Fifteen pairs of queries have been chosen for the study. The t-test has
been used for the purpose and the results have been checked on the basis of total documents found,
similarity of first five and first ten documents found in the results of a query entered in two different
formats. It has been found that the total coverage is same but first few results are significantly different.
Investigations of the Distributions of Phonemic Durations in Hindi and Dogrikevig
Speech generation is one of the most important areas of research in speech signal processing which is now gaining a serious attention. Speech is a natural form of communication in all living things. Computers with the ability to understand speech and speak with a human like voice are expected to contribute to the development of more natural man-machine interface. However, in order to give those functions that are even closer to those of human beings, we must learn more about the mechanisms by which speech is produced and perceived, and develop speech information processing technologies that can generate a more natural sounding systems. The so described field of stud, also called speech synthesis and more prominently acknowledged as text-to-speech synthesis, originated in the mid eighties because of the emergence of DSP and the rapid advancement of VLSI techniques. To understand this field of speech, it is necessary to understand the basic theory of speech production. Every language has different phonetic alphabets and a different set of possible phonemes and their combinations.
For the analysis of the speech signal, we have carried out the recording of five speakers in Dogri (3 male and 5 females) and eight speakers in Hindi language (4 male and 4 female). For estimating the durational distributions, the mean of mean of ten instances of vowels of each speaker in both the languages has been calculated. Investigations have shown that the two durational distributions differ significantly with respect to mean and standard deviation. The duration of phoneme is speaker dependent. The whole investigation can be concluded with the end result that almost all the Dogri phonemes have shorter duration, in comparison to Hindi phonemes. The period in milli seconds of same phonemes when uttered in Hindi were found to be longer compared to when they were spoken by a person with Dogri as his mother tongue. There are many applications which are directly of indirectly related to the research being carried out. For instance the main application may be for transforming Dogri speech into Hindi and vice versa, and further utilizing this application, we can develop a speech aid to teach Dogri to children. The results may also be useful for synthesizing the phonemes of Dogri using the parameters of the phonemes of Hindi and for building large vocabulary speech recognition systems.
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A NEW METHOD OF TEACHING FIGURATIVE EXPRESSIONS TOIRANIAN LANGUAGE LEARNERScscpconf
In teaching languages, if we only consider direct relationship between form and meaning in language and leave psycholinguistic aside, this approach is not a successful practice and language learners won't be able to make a successful relation in the real contexts. The present study intends to answer this question: is the teaching method in which salient meaning is taught more successful than the method in which literal or figurative meaning is taught or not? To answer the research question, 30 students were selected. Every ten people are formed as a group and three such groups were formed. Twenty figurative expressions were taught to every group. Group one was taught the figurative meaning of every expression. Group two was taught the literal meaning and group three was taught the salient meaning. Then three groups were tested. After analyzing data, we concluded that there was a significant difference in mean grades between classes and the class trained under graded salience hypothesis was more successful. This shows that traditional teaching methods must be revised.
Language transfer can be classified into negative transfer and positive transfer. The former is caused by the similarities shared by source language and target language, the latter is attributed to the differences between two languages. Linguists abroad and home have put forward that native language can promote students’ understanding of second language (Jarvis and Pavlenko, 2008; Wen, 2010). In the process of second language learning, especially for Junior High School students, the knowledge of native language can help students to complete their tasks. When learning the target language, they will unconsciously use the previous information to think and achieve the purpose of second language learning. Native language plays a fundamental role in second language learning. For students, it can facilitate the study of second language in some extent. Vocabulary is the foundation of language. As the beginning stage, English teaching in Junior high school should give priority to vocabulary learning. Then, whether Chinese plays a facilitate role in the process of students’ vocabulary learning? And if it has, what are the factors that influence language transfer? What teaching methods can teacher employed to students’ vocabulary learning? These are main contents of this study.
The Effects of Two Languages in One Mind: First Language AttritionMattia Zingaretti
An introduction to the phenomenon of first language attrition with interactive materials, literature findings and useful resources for students and researchers. This is an Open Educational Resource (OER) co-created by Roberta Spelorzi and Mattia Zingaretti, PhD Researchers at the University of Edinburgh. This OER is licensed under Creative Commons Attribution-ShareAlike, allowing viewers to download, use and make changes to the materials, providing attribution to the authors and distributing the content under the same license as the original.
The Impact of L1 Interference on Second Language Learning A Case Study of Fan...ijtsrd
This study explores the complex dynamics of first language interference in second language learning, with an emphasis on how it affects Ghanaian Fante students learning English. To address this, the study adopted a thematic analysis through semi structured questions that involved 20 junior high school students in the central region of Ghana. Utilising a word association task, the studys results revealed significant contributing factors such as confidence, lexical knowledge, and translation L1 interference , which substantially strengthened our understanding of its influence on second language learning among Fante speakers. This study significantly advances the subject by offering complex insights into the challenges and implications associated with L1 interference in language learning among Fante students in Ghana. These findings provide educators, stakeholders, management, governments, policymakers, and researchers with fresh insights into second language acquisition in Ghana and the struggles of Fante students in learning the second language, English. Zhang Beizhen | Dowuona Petrina Naa Narkie "The Impact of L1 Interference on Second Language Learning: A Case Study of Fante Second Language Learners of English" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-1 , February 2024, URL: https://www.ijtsrd.com/papers/ijtsrd62385.pdf Paper Url: https://www.ijtsrd.com/humanities-and-the-arts/education/62385/the-impact-of-l1-interference-on-second-language-learning-a-case-study-of-fante-second-language-learners-of-english/zhang-beizhen
A HISTORICAL DEVELOPMENT OF CONTRASTIVE ANALYSIS: A RELEVANT REVIEW IN SECOND...ijejournal
Contrastive analysis (CA) was primarily used in the 1950’s as an effective means to address second or
foreign language teaching and learning. In this context, it was used to compare pairs of languages, identify
similarities and differences in order to predict learning difficulties, with the ultimate goal of addressing
them (Fries, 1943; Lado, 1957). Yet, in the 1980’s and 1990’s the relevance of CA has been disputed.
Many studies have pointed out the limit of CA with respect to its weak and strong versions (Oller and
Ziahosseiny, 1970), (Wardhaugh, 1970) (Brown, 1989), (Hughes, 1980), (Yang, 1992), and (Whitman and
Jackson, 1972). To answer the limits of CA with regards to its weak, strong, and moderate versions, many
language teachers used CA with a new approach. Kupferberg and Olshtain (1996), James (1996), and
Ruzhekova-Rogozherova (2007). Here, salient contrastive linguistic input (CLI) is presented to learners for
an effective noticing. Yet, mere exposition of contrastive linguistic input to learners may not be enough for
effective acquisition to occur. Hence, Djiguimkoudre (2020) proposed structured phonemic awareness
activities to further strengthen such contrastive salient linguistic input when phonetics and phonology are
involved. When grammar is involved, the processing instruction (PI) model of Lee and
VanPatten (2003) is recommended since the types of activities that result in PI are believed to incite
effective noticing for intake.
. . . all human languages do share the same structure. More e.docxadkinspaige22
. . . all human languages do share the same structure. More explicitly: they have
essentially the same primitive elements and rules of composition . . . , although
of course there may be variations, such as the obvious ones derived from the
arbitrary association between sounds and meanings . . .
(Moro, 2016, p. 15)
3.1 Introduction
In this chapter, we start to consider individual theoretical perspectives on
L2 learning in greater detail. Our first topic is the Universal Grammar (UG)
approach (the generative linguistics approach), developed by the American
linguist Noam Chomsky and numerous followers over the last few decades.
This has been the most influential linguistic theory in the field, and has
inspired a wealth of publications (for full-length treatments, see Hawkins,
2001; Herschensohn, 2000; Lardiere, 2007; Leung, 2009; Slabakova, 2016;
Snape & Kupisch, 2016; Thomas, 2004; White, 2003; Whong, Gil, & Mars-
den, 2013).
The main aim of linguistic theory is twofold: firstly, to characterize what
human languages are like (descriptive adequacy), and, secondly, to explain
why they are that way (explanatory adequacy). In terms of L2 acquisition,
a linguistic approach sets out to describe the evolving language produced
by L2 learners, and to explain its characteristics. UG is therefore a prop-
erty theory (as defined in Chapter 1); that is, it attempts to characterize
the underlying linguistic knowledge in L2 learners’ minds. In contrast, a
detailed examination of the learning process itself (transition theory) will
be the main concern of the cognitive approaches which we describe in
Chapters 4 and 5.
First in this chapter, we will give a broad definition of the aims of the
Chomskyan tradition in linguistic research, in order to identify the aspects
of second language acquisition (SLA) to which this tradition is most relevant.
Secondly, we will examine the concept of UG itself in some detail, and
finally we will consider its application in L2 learning research.
3 Linguistics and Language
Learning
The Universal Grammar
Approach
C
o
p
y
r
i
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h
t
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0
1
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.
R
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EBSCO Publishing : eBook Collection (EBSCOhost) - printed on 4/6/2020 6:13 AM via UNIVERSITY OF ESSEX
AN: 2006630 ; Mitchell, Rosamond, Myles, Florence, Marsden, Emma.; Second Language Learning Theories : Fourth Edition
Account: s9814295.main.ehost
82 Linguistics and Language Learning
3.2 Why a Universal Grammar?
3.2.1 Aims of Linguistic Research
The main goals of linguistic theory, as defined by Chomsky (1986a), are to
answer three basic questions about human language:
1. What constitutes knowledge of language?
2.
. . . all human languages do share the same structure. More e.docxShiraPrater50
. . . all human languages do share the same structure. More explicitly: they have
essentially the same primitive elements and rules of composition . . . , although
of course there may be variations, such as the obvious ones derived from the
arbitrary association between sounds and meanings . . .
(Moro, 2016, p. 15)
3.1 Introduction
In this chapter, we start to consider individual theoretical perspectives on
L2 learning in greater detail. Our first topic is the Universal Grammar (UG)
approach (the generative linguistics approach), developed by the American
linguist Noam Chomsky and numerous followers over the last few decades.
This has been the most influential linguistic theory in the field, and has
inspired a wealth of publications (for full-length treatments, see Hawkins,
2001; Herschensohn, 2000; Lardiere, 2007; Leung, 2009; Slabakova, 2016;
Snape & Kupisch, 2016; Thomas, 2004; White, 2003; Whong, Gil, & Mars-
den, 2013).
The main aim of linguistic theory is twofold: firstly, to characterize what
human languages are like (descriptive adequacy), and, secondly, to explain
why they are that way (explanatory adequacy). In terms of L2 acquisition,
a linguistic approach sets out to describe the evolving language produced
by L2 learners, and to explain its characteristics. UG is therefore a prop-
erty theory (as defined in Chapter 1); that is, it attempts to characterize
the underlying linguistic knowledge in L2 learners’ minds. In contrast, a
detailed examination of the learning process itself (transition theory) will
be the main concern of the cognitive approaches which we describe in
Chapters 4 and 5.
First in this chapter, we will give a broad definition of the aims of the
Chomskyan tradition in linguistic research, in order to identify the aspects
of second language acquisition (SLA) to which this tradition is most relevant.
Secondly, we will examine the concept of UG itself in some detail, and
finally we will consider its application in L2 learning research.
3 Linguistics and Language
Learning
The Universal Grammar
Approach
C
o
p
y
r
i
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EBSCO Publishing : eBook Collection (EBSCOhost) - printed on 4/6/2020 6:13 AM via UNIVERSITY OF ESSEX
AN: 2006630 ; Mitchell, Rosamond, Myles, Florence, Marsden, Emma.; Second Language Learning Theories : Fourth Edition
Account: s9814295.main.ehost
82 Linguistics and Language Learning
3.2 Why a Universal Grammar?
3.2.1 Aims of Linguistic Research
The main goals of linguistic theory, as defined by Chomsky (1986a), are to
answer three basic questions about human language:
1. What constitutes knowledge of language?
2 ...
Effect of Query Formation on Web Search Engine Resultskevig
Query in a search engine is generally based on natural language. A query can be expressed in more than
one way without changing its meaning as it depends on thinking of human being at a particular moment.
Aim of the searcher is to get most relevant results immaterial of how the query has been expressed. In the
present paper, we have examined the results of search engine for change in coverage and similarity of first
few results when a query is entered in two semantically same but in different formats. Searching has been
made through Google search engine. Fifteen pairs of queries have been chosen for the study. The t-test has
been used for the purpose and the results have been checked on the basis of total documents found,
similarity of first five and first ten documents found in the results of a query entered in two different
formats. It has been found that the total coverage is same but first few results are significantly different.
Investigations of the Distributions of Phonemic Durations in Hindi and Dogrikevig
Speech generation is one of the most important areas of research in speech signal processing which is now gaining a serious attention. Speech is a natural form of communication in all living things. Computers with the ability to understand speech and speak with a human like voice are expected to contribute to the development of more natural man-machine interface. However, in order to give those functions that are even closer to those of human beings, we must learn more about the mechanisms by which speech is produced and perceived, and develop speech information processing technologies that can generate a more natural sounding systems. The so described field of stud, also called speech synthesis and more prominently acknowledged as text-to-speech synthesis, originated in the mid eighties because of the emergence of DSP and the rapid advancement of VLSI techniques. To understand this field of speech, it is necessary to understand the basic theory of speech production. Every language has different phonetic alphabets and a different set of possible phonemes and their combinations.
For the analysis of the speech signal, we have carried out the recording of five speakers in Dogri (3 male and 5 females) and eight speakers in Hindi language (4 male and 4 female). For estimating the durational distributions, the mean of mean of ten instances of vowels of each speaker in both the languages has been calculated. Investigations have shown that the two durational distributions differ significantly with respect to mean and standard deviation. The duration of phoneme is speaker dependent. The whole investigation can be concluded with the end result that almost all the Dogri phonemes have shorter duration, in comparison to Hindi phonemes. The period in milli seconds of same phonemes when uttered in Hindi were found to be longer compared to when they were spoken by a person with Dogri as his mother tongue. There are many applications which are directly of indirectly related to the research being carried out. For instance the main application may be for transforming Dogri speech into Hindi and vice versa, and further utilizing this application, we can develop a speech aid to teach Dogri to children. The results may also be useful for synthesizing the phonemes of Dogri using the parameters of the phonemes of Hindi and for building large vocabulary speech recognition systems.
May 2024 - Top10 Cited Articles in Natural Language Computingkevig
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
Effect of Singular Value Decomposition Based Processing on Speech Perceptionkevig
Speech is an important biological signal for primary mode of communication among human being and also the most natural and efficient form of exchanging information among human in speech. Speech processing is the most important aspect in signal processing. In this paper the theory of linear algebra called singular value decomposition (SVD) is applied to the speech signal. SVD is a technique for deriving important parameters of a signal. The parameters derived using SVD may further be reduced by perceptual evaluation of the synthesized speech using only perceptually important parameters, where the speech signal can be compressed so that the information can be transformed into compressed form without losing its quality. This technique finds wide applications in speech compression, speech recognition, and speech synthesis. The objective of this paper is to investigate the effect of SVD based feature selection of the input speech on the perception of the processed speech signal. The speech signal which is in the form of vowels \a\, \e\, \u\ were recorded from each of the six speakers (3 males and 3 females). The vowels for the six speakers were analyzed using SVD based processing and the effect of the reduction in singular values was investigated on the perception of the resynthesized vowels using reduced singular values. Investigations have shown that the number of singular values can be drastically reduced without significantly affecting the perception of the vowels.
Identifying Key Terms in Prompts for Relevance Evaluation with GPT Modelskevig
Relevance evaluation of a query and a passage is essential in Information Retrieval (IR). Recently, numerous studies have been conducted on tasks related to relevance judgment using Large Language Models (LLMs) such as GPT-4,
demonstrating significant improvements. However, the efficacy of LLMs is considerably influenced by the design of the prompt. The purpose of this paper is to
identify which specific terms in prompts positively or negatively impact relevance
evaluation with LLMs. We employed two types of prompts: those used in previous
research and generated automatically by LLMs. By comparing the performance of
these prompts in both few-shot and zero-shot settings, we analyze the influence of
specific terms in the prompts. We have observed two main findings from our study.
First, we discovered that prompts using the term ‘answer’ lead to more effective
relevance evaluations than those using ‘relevant.’ This indicates that a more direct
approach, focusing on answering the query, tends to enhance performance. Second,
we noted the importance of appropriately balancing the scope of ‘relevance.’ While
the term ‘relevant’ can extend the scope too broadly, resulting in less precise evaluations, an optimal balance in defining relevance is crucial for accurate assessments.
The inclusion of few-shot examples helps in more precisely defining this balance.
By providing clearer contexts for the term ‘relevance,’ few-shot examples contribute
to refine relevance criteria. In conclusion, our study highlights the significance of
carefully selecting terms in prompts for relevance evaluation with LLMs.
Identifying Key Terms in Prompts for Relevance Evaluation with GPT Modelskevig
Relevance evaluation of a query and a passage is essential in Information Retrieval (IR). Recently, numerous studies have been conducted on tasks related to relevance judgment using Large Language Models (LLMs) such as GPT-4, demonstrating significant improvements. However, the efficacy of LLMs is considerably influenced by the design of the prompt. The purpose of this paper is to identify which specific terms in prompts positively or negatively impact relevance evaluation with LLMs. We employed two types of prompts: those used in previous research and generated automatically by LLMs. By comparing the performance of these prompts in both few-shot and zero-shot settings, we analyze the influence of specific terms in the prompts. We have observed two main findings from our study. First, we discovered that prompts using the term ‘answer’ lead to more effective relevance evaluations than those using ‘relevant.’ This indicates that a more direct approach, focusing on answering the query, tends to enhance performance. Second, we noted the importance of appropriately balancing the scope of ‘relevance.’ While the term ‘relevant’ can extend the scope too broadly, resulting in less precise evaluations, an optimal balance in defining relevance is crucial for accurate assessments. The inclusion of few-shot examples helps in more precisely defining this balance. By providing clearer contexts for the term ‘relevance,’ few-shot examples contribute to refine relevance criteria. In conclusion, our study highlights the significance of carefully selecting terms in prompts for relevance evaluation with LLMs.
In recent years, great advances have been made in the speed, accuracy, and coverage of automatic word
sense disambiguator systems that, given a word appearing in a certain context, can identify the sense of
that word. In this paper we consider the problem of deciding whether same words contained in different
documents are related to the same meaning or are homonyms. Our goal is to improve the estimate of the
similarity of documents in which some words may be used with different meanings. We present three new
strategies for solving this problem, which are used to filter out homonyms from the similarity computation.
Two of them are intrinsically non-semantic, whereas the other one has a semantic flavor and can also be
applied to word sense disambiguation. The three strategies have been embedded in an article document
recommendation system that one of the most important Italian ad-serving companies offers to its customers.
Genetic Approach For Arabic Part Of Speech Taggingkevig
With the growing number of textual resources available, the ability to understand them becomes critical.
An essential first step in understanding these sources is the ability to identify the parts-of-speech in each
sentence. Arabic is a morphologically rich language, which presents a challenge for part of speech
tagging. In this paper, our goal is to propose, improve, and implement a part-of-speech tagger based on a
genetic algorithm. The accuracy obtained with this method is comparable to that of other probabilistic
approaches.
Rule Based Transliteration Scheme for English to Punjabikevig
Machine Transliteration has come out to be an emerging and a very important research area in the field of
machine translation. Transliteration basically aims to preserve the phonological structure of words. Proper
transliteration of name entities plays a very significant role in improving the quality of machine translation.
In this paper we are doing machine transliteration for English-Punjabi language pair using rule based
approach. We have constructed some rules for syllabification. Syllabification is the process to extract or
separate the syllable from the words. In this we are calculating the probabilities for name entities (Proper
names and location). For those words which do not come under the category of name entities, separate
probabilities are being calculated by using relative frequency through a statistical machine translation
toolkit known as MOSES. Using these probabilities we are transliterating our input text from English to
Punjabi.
Improving Dialogue Management Through Data Optimizationkevig
In task-oriented dialogue systems, the ability for users to effortlessly communicate with machines and computers through natural language stands as a critical advancement. Central to these systems is the dialogue manager, a pivotal component tasked with navigating the conversation to effectively meet user goals by selecting the most appropriate response. Traditionally, the development of sophisticated dialogue management has embraced a variety of methodologies, including rule-based systems, reinforcement learning, and supervised learning, all aimed at optimizing response selection in light of user inputs. This research casts a spotlight on the pivotal role of data quality in enhancing the performance of dialogue managers. Through a detailed examination of prevalent errors within acclaimed datasets, such as Multiwoz 2.1 and SGD, we introduce an innovative synthetic dialogue generator designed to control the introduction of errors precisely. Our comprehensive analysis underscores the critical impact of dataset imperfections, especially mislabeling, on the challenges inherent in refining dialogue management processes.
Document Author Classification using Parsed Language Structurekevig
Over the years there has been ongoing interest in detecting authorship of a text based on statistical properties of the text, such as by using occurrence rates of noncontextual words. In previous work, these techniques have been used, for example, to determine authorship of all of The Federalist Papers. Such methods may be useful in more modern times to detect fake or AI authorship. Progress in statistical natural language parsers introduces the possibility of using grammatical structure to detect authorship. In this paper we explore a new possibility for detecting authorship using grammatical structural information extracted using a statistical natural language parser. This paper provides a proof of concept, testing author classification based on grammatical structure on a set of “proof texts,” The Federalist Papers and Sanditon which have been as test cases in previous authorship detection studies. Several features extracted from the statisticalnaturallanguage parserwere explored: all subtrees of some depth from any level; rooted subtrees of some depth, part of speech, and part of speech by level in the parse tree. It was found to be helpful to project the features into a lower dimensional space. Statistical experiments on these documents demonstrate that information from a statistical parser can, in fact, assist in distinguishing authors.
Rag-Fusion: A New Take on Retrieval Augmented Generationkevig
Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots, but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal scores and fusing the documents and scores. Through manually evaluating answers on accuracy, relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and comprehensive answers due to the generated queries contextualizing the original query from various perspectives. However, some answers strayed off topic when the generated queries' relevance to the original query is insufficient. This research marks significant progress in artificial intelligence (AI) and natural language processing (NLP) applications and demonstrates transformations in a global and multi-industry context.
Performance, Energy Consumption and Costs: A Comparative Analysis of Automati...kevig
The common practice in Machine Learning research is to evaluate the top-performing models based on their performance. However, this often leads to overlooking other crucial aspects that should be given careful consideration. In some cases, the performance differences between various approaches may be insignificant, whereas factors like production costs, energy consumption, and carbon footprint should be taken into account. Large Language Models (LLMs) are widely used in academia and industry to address NLP problems. In this study, we present a comprehensive quantitative comparison between traditional approaches (SVM-based) and more recent approaches such as LLM (BERT family models) and generative models (GPT2 and LLAMA2), using the LexGLUE benchmark. Our evaluation takes into account not only performance parameters (standard indices), but also alternative measures such as timing, energy consumption and costs, which collectively contribute to the carbon footprint. To ensure a complete analysis, we separately considered the prototyping phase (which involves model selection through training-validation-test iterations) and the in-production phases. These phases follow distinct implementation procedures and require different resources. The results indicate that simpler algorithms often achieve performance levels similar to those of complex models (LLM and generative models), consuming much less energy and requiring fewer resources. These findings suggest that companies should consider additional considerations when choosing machine learning (ML) solutions. The analysis also demonstrates that it is increasingly necessary for the scientific world to also begin to consider aspects of energy consumption in model evaluations, in order to be able to give real meaning to the results obtained using standard metrics (Precision, Recall, F1 and so on).
Evaluation of Medium-Sized Language Models in German and English Languagekevig
Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative question answering, which requires models to provide elaborate answers without external document retrieval. The paper introduces an own test dataset and presents results from human evaluation. Results show that combining the best answers from different MLMs yielded an overall correct answer rate of 82.7% which is better than the 60.9% of ChatGPT. The best MLM achieved 71.8% and has 33B parameters, which highlights the importance of using appropriate training data for fine-tuning rather than solely relying on the number of parameters. More fine-grained feedback should be used to further improve the quality of answers. The open source community is quickly closing the gap to the best commercial models.
IMPROVING DIALOGUE MANAGEMENT THROUGH DATA OPTIMIZATIONkevig
In task-oriented dialogue systems, the ability for users to effortlessly communicate with machines and
computers through natural language stands as a critical advancement. Central to these systems is the
dialogue manager, a pivotal component tasked with navigating the conversation to effectively meet user
goals by selecting the most appropriate response. Traditionally, the development of sophisticated dialogue
management has embraced a variety of methodologies, including rule-based systems, reinforcement
learning, and supervised learning, all aimed at optimizing response selection in light of user inputs. This
research casts a spotlight on the pivotal role of data quality in enhancing the performance of dialogue
managers. Through a detailed examination of prevalent errors within acclaimed datasets, such as
Multiwoz 2.1 and SGD, we introduce an innovative synthetic dialogue generator designed to control the
introduction of errors precisely. Our comprehensive analysis underscores the critical impact of dataset
imperfections, especially mislabeling, on the challenges inherent in refining dialogue management
processes.
Document Author Classification Using Parsed Language Structurekevig
Over the years there has been ongoing interest in detecting authorship of a text based on statistical properties of the
text, such as by using occurrence rates of noncontextual words. In previous work, these techniques have been used,
for example, to determine authorship of all of The Federalist Papers. Such methods may be useful in more modern
times to detect fake or AI authorship. Progress in statistical natural language parsers introduces the possibility of
using grammatical structure to detect authorship. In this paper we explore a new possibility for detecting authorship
using grammatical structural information extracted using a statistical natural language parser. This paper provides a
proof of concept, testing author classification based on grammatical structure on a set of “proof texts,” The Federalist
Papers and Sanditon which have been as test cases in previous authorship detection studies. Several features extracted
of some depth, part of speech, and part of speech by level in the parse tree. It was found to be helpful to project the
features into a lower dimensional space. Statistical experiments on these documents demonstrate that information
from a statistical parser can, in fact, assist in distinguishing authors.
RAG-FUSION: A NEW TAKE ON RETRIEVALAUGMENTED GENERATIONkevig
Infineon has identified a need for engineers, account managers, and customers to rapidly obtain product
information. This problem is traditionally addressed with retrieval-augmented generation (RAG) chatbots,
but in this study, I evaluated the use of the newly popularized RAG-Fusion method. RAG-Fusion combines
RAG and reciprocal rank fusion (RRF) by generating multiple queries, reranking them with reciprocal
scores and fusing the documents and scores. Through manually evaluating answers on accuracy,
relevance, and comprehensiveness, I found that RAG-Fusion was able to provide accurate and
comprehensive answers due to the generated queries contextualizing the original query from various
perspectives. However, some answers strayed off topic when the generated queries' relevance to the
original query is insufficient. This research marks significant progress in artificial intelligence (AI) and
natural language processing (NLP) applications and demonstrates transformations in a global and multiindustry context
Performance, energy consumption and costs: a comparative analysis of automati...kevig
The common practice in Machine Learning research is to evaluate the top-performing models based on their
performance. However, this often leads to overlooking other crucial aspects that should be given careful
consideration. In some cases, the performance differences between various approaches may be insignificant, whereas factors like production costs, energy consumption, and carbon footprint should be taken into
account. Large Language Models (LLMs) are widely used in academia and industry to address NLP problems. In this study, we present a comprehensive quantitative comparison between traditional approaches
(SVM-based) and more recent approaches such as LLM (BERT family models) and generative models (GPT2 and LLAMA2), using the LexGLUE benchmark. Our evaluation takes into account not only performance
parameters (standard indices), but also alternative measures such as timing, energy consumption and costs,
which collectively contribute to the carbon footprint. To ensure a complete analysis, we separately considered the prototyping phase (which involves model selection through training-validation-test iterations) and
the in-production phases. These phases follow distinct implementation procedures and require different resources. The results indicate that simpler algorithms often achieve performance levels similar to those of
complex models (LLM and generative models), consuming much less energy and requiring fewer resources.
These findings suggest that companies should consider additional considerations when choosing machine
learning (ML) solutions. The analysis also demonstrates that it is increasingly necessary for the scientific
world to also begin to consider aspects of energy consumption in model evaluations, in order to be able to
give real meaning to the results obtained using standard metrics (Precision, Recall, F1 and so on).
EVALUATION OF MEDIUM-SIZED LANGUAGE MODELS IN GERMAN AND ENGLISH LANGUAGEkevig
Large language models (LLMs) have garnered significant attention, but the definition of “large” lacks
clarity. This paper focuses on medium-sized language models (MLMs), defined as having at least six
billion parameters but less than 100 billion. The study evaluates MLMs regarding zero-shot generative
question answering, which requires models to provide elaborate answers without external document
retrieval. The paper introduces an own test dataset and presents results from human evaluation. Results
show that combining the best answers from different MLMs yielded an overall correct answer rate of
82.7% which is better than the 60.9% of ChatGPT. The best MLM achieved 71.8% and has 33B
parameters, which highlights the importance of using appropriate training data for fine-tuning rather than
solely relying on the number of parameters. More fine-grained feedback should be used to further improve
the quality of answers. The open source community is quickly closing the gap to the best commercial
models.
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Search and Society: Reimagining Information Access for Radical Futures
Language Distance and L3 Japanese Acquisition in Morphosyntactic Module
1. International Journal on Natural Language Computing (IJNLC) Vol.12, No.4, August 2023
DOI: 10.5121/ijnlc.2023.12402 13
LANGUAGE DISTANCE AND L3 JAPANESE
ACQUISITION IN MORPHOSYNTACTIC MODULE
Wenchao Li
Department of Japanese Studies, Zhejiang University, Hangzhou City, China
ABSTRACT
This study applies mathematical linguistics to explore how language distance plays an essential role in
third language acquisition in terms of a morphosyntactic module. Data were drawn from 3410 essays
written in Japanese by low, middle and high levels of learners from 12 first-language (L1) backgrounds
who acquire English as a second language (L2)-interlanguage and Japanese as a third language (L3). The
findings indicate that (a) mean dependency distance is an efficient indicator for syntactic complexity of
writing proficiency. In both elementary and intermediate groups, learners of highly agglutinative
languages are likely to show higher dependency distance than learners from isolated-language and fusion-
language backgrounds. (b) The frequency and dependency distance are distributed in Power Law
Function. Fitting Right truncated Good to the dependency distances indicates that the values of the
parameter p ascend as the degree of agglutination of learners’ mother tongue increases. (c) The syntactic
complexity in multi-background Japanese learners’ essays highlights that no matter how diverse the
learners’ native and target languages are, the syntax is always constrained by universal law, namely,
minimising dependency distance. This is in accordance with existing findings in second language
acquisition of inflectional languages.
KEYWORDS
language distance, writing proficiency, mathematical linguistics, L3 acquisition, mean dependency
distance
1. INTRODUCTION
Typological distance and learning order have been deemed the most essential factors in language
acquisition. Many scholars from different camps have attempted to determine which factor is
more important and to what extent transfer from previously acquired languages happens
(wholesale or piecemeal). Existing assumptions fall into three groups: (a) the second language
(L2) has a significant influence at the early stage regarding phonological and syntactic acquisition
(Hammarberg and Hammarberg 1993; Marx 2002; Tremblay 2007; Williams and Hammarberg
1998; Onishi 2016; Bardel and Falk 2007; Falk and Bardel 2011; Forsyth 2014; Archibald 2019;
Dziubalska-Kołaczyk and Wrembel 2017; Onishi 2016; Wrembel 2015), (b) typological distance
plays an essential role in lexis learning (Cenoz 2001; Rossi et al. 2006; Rothman 2010; Singleton
1987), and (c) the Cumulative-Enhancement Model (Flynn et al. 2004), Scalpel Model
(Slabakova 2016) and Linguistic Proximity Model (Westergaard et al. 2017) show that language
transfer is selective.
Owing to the development of natural language processing, another line of notable research
contributes a great deal to the field: the quantitative approach (Hunt 1965; Lu 2011; Jiang et al.
2019). Quyang and Jiang (2018) examined the syntactic complexity of Chinese L2 English
writing and arrived at regularity: with increased learners’ grades, writing quality improves. Jiang
et al. (2019) explored writing development across beginner and intermediate L2 English learners,
2. International Journal on Natural Language Computing (IJNLC) Vol.12, No.4, August 2023
14
finding that the mean T-unit length, mean sentence length, and dependent clauses per clause are
effective measures of writing quality. In line with the research on the quantitative approach,
however, there remains room for further investigation. First, the target language of previous work
seems to be limited to English, a (relatively) morphologically isolated language for which writing
proficiency appears to be better captured at the syntactic level (e.g., relative, coordinate, and
subordinate clauses). Furthermore, existing studies tend to focus on second language acquisition
(SLA) more than third language acquisition (TLA). To our knowledge, the only study to explore
third-language (L3) Japanese writing quality is that of Komori et al. (2019), which deemed the
mean hierarchical distance an efficient index of Japanese learners’ writing. Based on Quyang and
Jiang’s (2018) insights, Li and Yan (2021) examined three levels of Japanese English learners
and demonstrated that interlanguage follows certain linguistic laws regardless of the learner’s
native language. This inspires us to consider the role a learner’s first language plays in learning
Japanese as a third language. Moreover, thus far, the metrics in measuring learning proficiency
include the mean sentence length, mean clause length (Hunt 1965), T-unit length (Hunt 1970),
noun phrases (Biber et al. 2011), number of coordinate/subordinate clauses (Bulte and Housen
2012), degree of sophistication (Ai and Lu 2013), mean dependency distance (MDD) (e.g., Liu
2008, 2017; Quyang and Jiang 2018), and mean hierarchical distance (Jing and Liu 2015; Komori
et al. 2019).
Given that Japanese is morphologically agglutinative, one or more suffixes (causative, negation,
voice, tense, or honorification) are added to a verb/adjective stem to create complex predicates.
The dependency direction at the lexical level is thus head-initial, contrary to syntactic structure
(head-final). Incorporating this, it would be necessary to consider lexical complexity when
indexing the writing proficiency of agglutinative languages (e.g., Altaic, Korean, Indonesian,
Hungarian). For instance, the verb 返 す [kaesu.plain form], its honorific form okaeshininaru
[kaesu.honorific form], and its humble form okaeshisuru [kaesu.humble form] have the same semantic
meaning “to return sth” but different pragmatic functions. Word type-token ratio has a great deal
to do with the degree of honorification and, thus, should be considered a crucial candidate for
indexing spoken and written quality.
The present study investigates 360 essays written by L3 Japanese learners from 12 L1 language
backgrounds (Vietnamese, Thai, Chinese, English, Russian, Indonesian, French, Turkish, German,
Spanish, Hungarian, and Korean). By calculating the differences in lexical and syntactic
complexity between writings at different levels and learners’ mother tongues, this study aims to
understand the association between L1-L3 language distance and L3 acquisition proficiency. The
following questions are addressed:
Question 1: Can mean dynamic mean dependency distance, type-token ratio, and the distribution
of long dependency relationship types indicate L3 writing proficiency?
Question 2: While Japanese is agglutinative and has an SOV word order, the research target in
this study (i.e., Japanese learners from 12 L1 backgrounds) bears different morphosyntactic
features (see Table 1). Is there an indicator in the probability distribution of L3 proficiency? If so,
is it the morphological distance (distance between mother tongue and L3 language:
agglutination/fusion/isolation) that suggests a trend in learning level?
Question 3: It has been explicated that the power law function may reflect the probability
distribution of the dependency distance of second language learners’ language proficiency (e.g.,
Quyang et al. 2018). Does L3 Japanese acquisition across diverse L1 backgrounds follow a
similar regularity? Are there parameters suggesting a trend in learning quality?
3. International Journal on Natural Language Computing (IJNLC) Vol.12, No.4, August 2023
15
Table 1. Japanese learners from multiple backgrounds
L1 Family Languag
es
Morphology Word
order
Adjective
Noun
Adposition
HHG Uralic Hungaria
n
Highly
agglutinative
SVO Adj-Noun Postposition
TTR Altaic Turkish Highly
agglutinative
SOV Adj-Noun Postposition
RRS Slavic Russian Inflectional SVO Adj-Noun Preposition
IID Austronesian Indonesi
an
Agglutinative SVO Noun-Adj Preposition
TTH Kra-Dai language Thai Isolating SVO Noun-Adj Preposition
VVN Austroasiatic Vietnam
ese
Isolating SVO Noun-Adj Preposition
CCH Sino-Tibetan Chinese Isolating SVO Adj-Noun Preposition
EUS Indo-European
(Germanic)
English Relatively
isolating
SVO Adj-Noun Preposition
GAT Indo-European
(Germanic)
German Inflectional SVO Adj-Noun Preposition
FFR Indo-European
(Italic)
French Inflectional SVO Noun-Adj Preposition
SES Indo-European
(Italic)
Spanish Inflectional SVO Noun-Adj Preposition
KKR unknown Korean Agglutinative SOV Adj-Noun Postposition
JJJ unknown Japanese Agglutinative SOV Adj-Noun Postposition
2. DATA AND METHODS
2.1. Data
Data were drawn from the International Cross-Sectional Corpus of Japanese as a Second
Language1
. Following the scores of Japanese Computerized Adaptive Test2
, this study extracts
1705 low, middle and high learners from Hungary, Russia, Indonesia, Thai, Vietnam, Chinese,
England, Germany, France, Spain Korea and Turk. A totally of 3410 compositions written in
Japanese titled “Our Eating Life: Fast Food and Home-Made Food” was extracted (1705
learners × 2 tasks). The tokens of these essays amount to 633,000 words. 100 essays by native
Japanese were collected for reference.
2.2. Procedures
The present study is designed to examine third language acquisition proficiency with a focus on
the morphosyntactic module. To this end, writing proficiency is measured via syntactic
complexity. Essays written by Japanese learners across different L1 backgrounds are classified
into two levels: primary and intermediate. The classification is made by the “Learner Text
Evaluation System”. By looking into the writing proficiency of different levels, we may
understand whether language distance plays a role in learning quality. Moreover, considering
conjunctions might be missing in compositions, leading to an undetected dependency relationship
(e.g., the run-on sentence “ringo ga suki, hoshii” [apple-NOM-like, want]; I like apples, so I want
[apples])—sentence length alone may not be sufficient to represent syntactic complexities. This
1
Sakoda (2020:10) points out that SLA refers to learning a language other than the first language.
2
https://j-cat.jalesa.org/?page_id=168
4. International Journal on Natural Language Computing (IJNLC) Vol.12, No.4, August 2023
16
study thus delves into the distribution of MDD for determining the level of writing proficiency.
The following procedures were carried out:
Step 1: Draw raw data from the corpora
Step 2: Classify the writings into two levels: elementary and intermediate via the Learner Text
Evaluator
Step 3: Parse each sentence via the GiNZA v4 Parser
Step 4: Produce a computer program to calculate the dynamic MDD from the parsed outputs
Step 5: Explore the probability distribution and the parameters that index writing proficiency
Step 6: Produce a computer program to verify the Spearman correlation and
Euclidean distance clustering between the essays written by Japanese natives and learners from
different L1 backgrounds
2.3. Data Analysis
As previously noted, this study employs dependency distance to indicate the syntactic complexity
of essays written in Japanese. The framework is Dependency Grammar (Tesnière 1959; Hudson
2007; Liu 2009b), which contains three concepts: dependency relationship, dependency direction,
and dependency distance. A dependency relationship is characterized by being binary and
asymmetrical. Among syntactic structures, the verb is the GOVERNOR, and all elements are
connected via a ‘governor-dependent’ relationship. For instance, in Taroo ga Jiroo ni ringo o
ageta [Taroo-NOM-Jiroo-DAT-apple-ACC-give.PAST], the verb ageta ‘gave’ behaves as the
governor on which the subject Taroo, the direction object ringo ‘apple,’ and the indirect object
Jiroo depend. The straight arrow is directed from the governor to the dependent, as shown in (1).
(1) Dependency structure of ‘Taroo ga Jiroo ni ringo o ageta.’
Dependency direction is a means of word-order typology (Liu 2010). Regarding the research
targets of the present study, as Liu (2010) suggests, Chinese, English, Hungarian, Japanese, and
Turkish tend to favor a head-final structure, while German and Spanish generally prefer a head-
initial structure. Dependency distance refers to the linear distance between governor and
dependent. This concept was initially proposed by Yngve (1960) and developed by Hudson
(1995). Liu, Hudson, and Feng (2009) proposed measuring the mean dependency distance for a
sentence in terms of dependency direction. The calculation procedure is as follows: subtract the
position numbers of the governor and the dependent, and assume words in a sentence are
assigned in a string, that is, W1 …Wi …Wn. In any dependency relationship between words Wa
and Wb, Wa is a governor, and Wb is its dependent. When Wa and Wb are adjacent, the distance
between them is |1|. To put it another way, the dependency distance (DD) between the two words
is |governor – dependent| (the absolute value), and the MDD of the whole sentence would be
In this formula, n is the number of words in the sentence, and DDi is the dependency distance of
the i th dependency relationship of a sentence. Building on this, the annotation of sentence (1)
was:
5. International Journal on Natural Language Computing (IJNLC) Vol.12, No.4, August 2023
17
Sentence
number
Dependent Governor Dependency
type
Order
number
Word POS Order
number
Word POS
S1 1 Taroo NOUN 7 age VERB nsubj
S1 2 ga CASE 1 Taroo NOUN case
S1 3 Jiroo NOUN 7 age VERB obl
S1 4 ni CASE 3 Jiroo NOUN case
S1 5 ringo NOUN 7 age VERB obj
S1 6 o CASE 5 ringo NOUN case
S1 7 ageta VERB 0 / / root
S1 8 ta TENSE 7 age VERB aux
Incorporating this, the MDD of sentence (1) would be 2. Theoretical and empirical studies from
different camps have confirmed that dependency distance is a metric of language production and
comprehension (Gibson 1998, 2000; Temperley 2007; Liu 2008; Gibson et al. 2013; Scontras et
al. 2015; Rispens and De Amesti 2017; Fang and Liu 2018; Wang and Liu 2019): the longer a
dependency distance is, the harder this dependency is to access (input and output). Inspired by
these contributions, a general law of human language is proposed: minimising dependency
distance (Liu 2008; Temperley 2007; Futrell et al. 2015). Against this background, this study
wishes to determine whether there is a trend for Japanese learners with a subject-object-verb
(SOV) language background to create a longer dependency distance while learners from a
subject-verb-object (SVO) language background are likely to produce a shorter dependency
distance. There are about 14 levels of dependency relationships in Japanese (Table 2). The
following long-distance relationships will be targeted: ccomp (object subclause), advcl (adverbial
clause), conj (conjunction), and acl (adjectival clause).
Table 2. Selected dependency relationships in Japanese
Dependency relations Grammatical structures
Clause level nsubj Subject of noun phrase
ccomp Complementizer of object clause
obl Oblique element
advcl Adverbial clause
advmod Adverbibal modifier
cop Copular
conj Conjunction
Noun phrase level
Others
nmod Noun modifier
acl Adjectival clause
3. RESULTS AND DISCUSSION
3.1. The Reliability of MDD
This section examines whether MDD could index the syntactic complexity of writing quality. A
Spearman rank correlation test between MDD and score of J-CAT score was conducted. A
scatterplot with a regression line was plotted in Figure 1.
6. International Journal on Natural Language Computing (IJNLC) Vol.12, No.4, August 2023
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Figure 1. Spearman rank correlation coefficient between J-CAT scores and proficiency indicator MDD
As indicated, MDD and learners’ acquisition proficiency well fitted the regression line. The low,
middle and high proficiency groups show extremely harmonic correlations, with ρ = 0.9992, p =
0.024. Given this, we contend that MDD is efficient for indexing Japanese writing proficiency.
Research question 1 was, thus, answered.
3.2. Associations of Language Distance and Acquisition Proficiency
With the metrics MDD in place, now we are in the position of exploring how L1’s morphological
features might affect Japanese acquisition proficiency. Figures 2 present the boxplots of MDDs
of proficiency low, middle and high.
7. International Journal on Natural Language Computing (IJNLC) Vol.12, No.4, August 2023
19
Figure 2. Boxplots of MDDs in different learning levels
Figure 2 indicates that (a). in early acquisition stage, agglutinative-L1 learners’ essay (Turkish,
Indonesian, Korean, Hungarian) presents a higher MDD value than inflectional-and isolating-L1
learners’ essay (Chinese, Vietnamese, Thai, French, German, Russian, Spanish, English). (b). this
advantage retains in middle learning level. By high learning level, learners’ essay achieved
similar MDD. Since MDD is an index of discriminating writing proficiency, we deduce that non-
native Japanese learning quality at the early learning stage was influenced by the mother tongues.
Euclidean-distance clustering based on MDD of two levels (elementary and intermediate) was
carried out. The results further suggested variations within inflectional languages. The Slavic-
and Romance-L1 learners produce a higher MDD than Germanic-L1 learners; Germanic-L1
learners are more close to insolating-L1 learners, as seen in low learning level, the essays written
by French, Spanish, Turkish and Russian learners are clustered together; the essays written by
Indonesian and Hungarian are clustered together; the essays written by Vietnamese, Thai,
English, Chinese, German learners are clustered together.
(2) Euclidean-distance clustering
8. International Journal on Natural Language Computing (IJNLC) Vol.12, No.4, August 2023
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Figure 3. A cluster tree of essays written by Japanese learners from 12 L1 backgrounds based on MDD
Altmann-Fitter is further employed to fit the distribution of MDD, and it was found that the data
of the isolating, agglutinative, and inflectional groups were fitted to Right truncated Good, as
shown in Table 3.
Table 3. Fitting the Right truncated Good to the dependency distances of Japanese learners from different
L1 backgrounds
L1 a p X2
P (X2
) DF C R2
Isolating group -0.6229 0.8404 0.1380 0.9869 3 0.0097 0.8462
Inflectional group 0.0013 1.0237 0.0735 1.0000 24 0.0012 0.9584
Agglutinative
group
-0.0279 1.0346 0.0834 1.0000 19 0.0015 0.9778
The parameters in Right truncated Good are a and p. The values of the parameter p increase as
the degree of agglutination of learners’ mother tongue increases, that is, from isolating to
inflectional to agglutinative. The MDD results further clarify that the minimizing dependency
distance shown in the Japanese writings (Liu 2008; Temperley 2007; Futrell et al. 2015) reflects
that of the learner’s mother tongue. Research question 3 is, thus, answered.
3.3. The Probability Distribution of L3 Learning Proficiency
Previous sections have shown that MDD is a good indicator of writing proficiency. A further step
forward in the relationship between MDD and frequency is attempted. A Python program is
produced to fit the MDD-frequency by learners across different L1 backgrounds to the Power
Law Function (y = ae−bx
). The finding suggests that apart from essays written by Hungarian
Japanese learners, the MDD-frequency of essays written by the rest 11 L1-language learners fit
appear to fit the power law function, with 0.7992 as the lowest value of the determination
coefficient R2
and 0.9621 as the highest (R2
> 0.90, very good; R2
> 0.80, good; R2
> 0.75,
acceptable; R2
< 0.75, unacceptable). Figure 3 demonstrates the fitting outcomes of MDD-
frequency.
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Figure 3. Fitting Power Law Function to MDD-frequency relationship of essays
10. International Journal on Natural Language Computing (IJNLC) Vol.12, No.4, August 2023
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Table 4 presents the variation of the parameters that contribute to the power law function. As
suggested by the fitting results, the learning proficiency is predictable via (y = ae−bx
).
Table 4. Parameters that contribute to the power law function
L1 a b R2
Fitting results
French 275.41 -0.53 0..9086 y =275.41e-0.53 x
Vietnamese 172.62 -0.29 0.8204 y =172.62e-0.29 x
Indonesian 251044.02 -0.89 0.9621 y =25104.02e-0.89 x
Chinese 1654.49 -0.63 0.8312 y =1654.49e-0.63 x
Korean 14733.02 -1.14 0.9246 y =14733.02e-1.14 x
Thai 6488.04 -1.51 0.7992 y =6488.04e-1.51 x
Russian 5423.72 -0.63 0.8902 y =5423.72e-0.63 x
Spanish 29845.57 -0.97 0.9267 y =29845.57e-0.97 x
Turkish 24274.01 -0.91 0.9095 y =24274.01e-0.91 x
German 18811.50 -1.69 0.9561 y =18811.50e-01.69 x
4. DISCUSSION AND CONCLUSION
This study explores how the language distance between L1 and L3 influences L3’s
writing proficiency. The data comprised 3410 essays produced by learners from 12
mother tongues. Mathematical linguistic approach is incorporated. Statistical analysis revealed
that mean dependency distance is an efficient indicator for syntactic complexity. The finding also
revealed that learners of highly agglutinative languages are likely to show higher MDD values
than learners from isolated-language and fusion-language backgrounds at initial learning stages.
Syntactic distance, that is, word order, also facilities a distinction in MDD: the learners from
SOV language backgrounds show longer MDD and more dependency relationship types than the
learners from SVO language backgrounds. A Python program was used for fitting the probability
distribution of writing proficiency in Japanese learners from different L1 backgrounds. The
results indicated that the frequency and dependency distance is distributed in the Power Law
function. Building on this, we employed the Altmann-Fitter software to verify the indicator in the
probability distribution of L3 proficiency. Fitting Right truncated Good to the dependency
distances of Japanese learners from different L1 backgrounds shows that the values of the
parameter p increase as the degree of agglutination of learners’ mother tongue increases. That is,
L3 Japanese writing proficiency is influenced by language distance. Specifically, the
typologically closer the learner’s previously known languages are to the third language, the better
the learner can perform in L3 acquisition. This is in accordance with the Zipf Principle of Least
Effort (Zipf, 1949), which suggests that any human action aims to lighten the processing load. In
this study, with the degree of agglutination of native language being higher, accessibility to the
learner’s knowledge of their previously known language is quicker, leading to a strong transfer
from native language to subsequent language. The syntactic complexity in multi-background
Japanese learners’ essays highlights that no matter how diverse the learners’ native and target
languages are, the syntax is always constrained by universal law, minimizing dependency
distance. This is in accordance with existing findings (i.e., SLA in English (Quyang et al. 2018;
Lu and Liu 2016)).
While our findings could provide deeper insight into Japanese education, much remains to be
explored in the future. First, the present study only examined narrative essays. To draw a more
unified picture of how language distance may influence learning proficiency, other writing genres,
such as emails, should be examined in more detail. Second, a further look into the role of
interlanguage English in L3 Japanese acquisition is necessary (positive/negative transfer). Finally,
11. International Journal on Natural Language Computing (IJNLC) Vol.12, No.4, August 2023
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the speaking proficiency of learners across diverse L1 backgrounds could shed more light on the
link between L1–L3 distance and acquiring proficiency.
ACKNOWLEDGEMENTS
This paper is based on work that was supported by the National Foundation of Social Sciences of
China (22BYY186).
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AUTHOR
Wenchao Li is Associate professor, Deputy director of Department of Japanese Studies, University of
Zhejiang, and an executive director of Japanese Education Research Association (China). She received her
M.Phil. and Ph.D. in Linguistics at the University of Tohoku (Japan). She also holds a M.St. in Japanese
Studies (linguistic track) from the University of Oxford (College: Hertford). Her main research interests are
mathematic linguistics. She has published 40 papers in English in international linguistic journals, and has
served as a member of editorial board of The European Journal of Theoretical and Applied Sciences;
reviewer for Journal of International Linguistics Research and International Journal of English Linguistics.
Her three books were published in English by LINCOM Press (Germany).