MORPHOLOGICAL SEGMENTATION WITH LSTM NEURAL NETWORKS FOR TIGRINYAijnlc
Morphological segmentation is a fundamental task in language processing. Some languages, such as
Arabic and Tigrinya,have words packed with very rich morphological information.Therefore, unpacking
this information becomes a necessary taskfor many downstream natural language processing tasks. This
paper presents the first morphological segmentation research forTigrinya. We constructed a new
morphologically segmented corpus with 45,127 manually segmented tokens. Conditional random fields
(CRF) and window-based longshort-term memory (LSTM) neural networkswere employed separately to
develop our boundary detection models. We appliedlanguage-independent character and substring features
for the CRFand character embeddings for the LSTM networks. Experimentswere performed with four
variants of the Begin-Inside-Outside (BIO) chunk annotation scheme. We achieved 94.67% F1 scoreusing
bidirectional LSTMs with fixed-sizewindow approach to morphemeboundary detection.
Setswana Tokenisation and Computational Verb Morphology: Facing the Challenge...Guy De Pauw
This document discusses tokenization and computational verb morphology for Setswana, a Bantu language with a disjunctive orthography. It presents an approach that combines two tokenization transducers and a morphological analyzer to effectively tokenize Setswana text. The approach was tested on a short Setswana text and achieved 93.6% accuracy between the automatically and hand-tokenized texts. While mostly successful, some issues remained around longest matches that were not valid tokens or did not allow morphological analysis. Overall, the approach demonstrated that a precise tokenizer and morphological analyzer can largely resolve the challenges of Setswana's disjunctive writing system.
This document provides an overview of constraint satisfaction problems (CSPs). It defines a CSP as a problem where variables must be assigned values from their domains to satisfy constraints. Examples of CSPs include the n-queens puzzle, map coloring, Boolean satisfiability, and cryptarithmetic problems. A CSP is represented as a constraint graph with nodes as variables and edges as binary constraints. The goal is to assign values to each variable to satisfy all constraints.
This document provides an overview of the Minimalist Program (MP) proposed by Chomsky in 1993. It discusses the redundant and necessary levels of representation, including Logical Form and Phonetic Form. Principles like economy of derivation and economy of representation are explained. The document also covers topics like phrase structure, movements, feature checking, and the Full Interpretation Principle in MP. The conclusion states that MP aims to minimize theoretical concepts in syntax to achieve universality of grammar.
This document discusses propositional logic inference rules and their properties. It introduces several common rules of inference like modus ponens, and introduction, and elimination. It also discusses the relationship between inference and entailment, and defines important properties of soundness, completeness, and decidability for logical systems. Examples are provided to demonstrate proving goals using rules of inference and a truth table is used to check entailment.
This document is a preface to the second edition of "The Z Notation: A Reference Manual" by J.M. Spivey. It explains the motivation for revising and updating the reference manual, including addressing omitted language constructs, expanding the mathematical notation library, and improving explanations. The preface acknowledges contributions from others that helped improve the new edition. It also provides instructions for obtaining supplemental materials to support understanding and using the Z specification language.
This document presents two new approaches for aligning sentences in parallel English-Arabic corpora: mathematical regression (MR) and genetic algorithm (GA) classifiers. Feature vectors containing text features like length, punctuation score, and cognate score are extracted from sentence pairs and used to train the MR and GA models on manually aligned training data. The trained models are then tested on additional sentence pairs, achieving better results than a baseline length-based approach. The methods can be applied to any language pair by modifying the feature vector.
This document presents machine learning techniques to identify verb subcategorization frames from Czech language corpora. It compares three statistical techniques and shows they can discover new subcategorization frames and label dependents as arguments or adjuncts with 88% accuracy. It describes the task, relevant properties of Czech including free word order and case marking, and how the techniques are applied without assuming a predefined frame set.
MORPHOLOGICAL SEGMENTATION WITH LSTM NEURAL NETWORKS FOR TIGRINYAijnlc
Morphological segmentation is a fundamental task in language processing. Some languages, such as
Arabic and Tigrinya,have words packed with very rich morphological information.Therefore, unpacking
this information becomes a necessary taskfor many downstream natural language processing tasks. This
paper presents the first morphological segmentation research forTigrinya. We constructed a new
morphologically segmented corpus with 45,127 manually segmented tokens. Conditional random fields
(CRF) and window-based longshort-term memory (LSTM) neural networkswere employed separately to
develop our boundary detection models. We appliedlanguage-independent character and substring features
for the CRFand character embeddings for the LSTM networks. Experimentswere performed with four
variants of the Begin-Inside-Outside (BIO) chunk annotation scheme. We achieved 94.67% F1 scoreusing
bidirectional LSTMs with fixed-sizewindow approach to morphemeboundary detection.
Setswana Tokenisation and Computational Verb Morphology: Facing the Challenge...Guy De Pauw
This document discusses tokenization and computational verb morphology for Setswana, a Bantu language with a disjunctive orthography. It presents an approach that combines two tokenization transducers and a morphological analyzer to effectively tokenize Setswana text. The approach was tested on a short Setswana text and achieved 93.6% accuracy between the automatically and hand-tokenized texts. While mostly successful, some issues remained around longest matches that were not valid tokens or did not allow morphological analysis. Overall, the approach demonstrated that a precise tokenizer and morphological analyzer can largely resolve the challenges of Setswana's disjunctive writing system.
This document provides an overview of constraint satisfaction problems (CSPs). It defines a CSP as a problem where variables must be assigned values from their domains to satisfy constraints. Examples of CSPs include the n-queens puzzle, map coloring, Boolean satisfiability, and cryptarithmetic problems. A CSP is represented as a constraint graph with nodes as variables and edges as binary constraints. The goal is to assign values to each variable to satisfy all constraints.
This document provides an overview of the Minimalist Program (MP) proposed by Chomsky in 1993. It discusses the redundant and necessary levels of representation, including Logical Form and Phonetic Form. Principles like economy of derivation and economy of representation are explained. The document also covers topics like phrase structure, movements, feature checking, and the Full Interpretation Principle in MP. The conclusion states that MP aims to minimize theoretical concepts in syntax to achieve universality of grammar.
This document discusses propositional logic inference rules and their properties. It introduces several common rules of inference like modus ponens, and introduction, and elimination. It also discusses the relationship between inference and entailment, and defines important properties of soundness, completeness, and decidability for logical systems. Examples are provided to demonstrate proving goals using rules of inference and a truth table is used to check entailment.
This document is a preface to the second edition of "The Z Notation: A Reference Manual" by J.M. Spivey. It explains the motivation for revising and updating the reference manual, including addressing omitted language constructs, expanding the mathematical notation library, and improving explanations. The preface acknowledges contributions from others that helped improve the new edition. It also provides instructions for obtaining supplemental materials to support understanding and using the Z specification language.
This document presents two new approaches for aligning sentences in parallel English-Arabic corpora: mathematical regression (MR) and genetic algorithm (GA) classifiers. Feature vectors containing text features like length, punctuation score, and cognate score are extracted from sentence pairs and used to train the MR and GA models on manually aligned training data. The trained models are then tested on additional sentence pairs, achieving better results than a baseline length-based approach. The methods can be applied to any language pair by modifying the feature vector.
This document presents machine learning techniques to identify verb subcategorization frames from Czech language corpora. It compares three statistical techniques and shows they can discover new subcategorization frames and label dependents as arguments or adjuncts with 88% accuracy. It describes the task, relevant properties of Czech including free word order and case marking, and how the techniques are applied without assuming a predefined frame set.
This document discusses knowledge-based logical agents and concepts from propositional logic. It introduces how knowledge bases represent what agents know and can be used to determine actions. Propositional logic syntax and semantics are explained using sentences about pits and breezes in the Wumpus world. Key concepts in logic are defined such as logical equivalence, validity, satisfiability and the relationship between inference, entailment and sound/complete derivation of sentences.
This summarizes a document describing a system used in the Aspect Based Sentiment Analysis (ABSA) task of SemEval 2016. The system uses maximum entropy classifiers for aspect category detection and sentiment polarity tasks. Conditional random fields are used for opinion target extraction. It achieved state-of-the-art results in 9 constrained and 2 unconstrained experiments. The system is described including the features used, such as semantics features from word embeddings, and constrained/unconstrained features for the different subtasks and languages.
This document discusses regular languages and grammars. It begins by defining formal languages and describing two approaches to describing languages: the generative approach using grammars and the recognition approach using automata. It then discusses Noam Chomsky's hierarchy of formal grammars and how this classifies the expressive power of grammars. Regular languages are those described by regular grammars and recognized by finite automata. Regular expressions provide another way to describe regular languages. The document proves the equivalence between regular expressions, regular grammars, and finite automata by showing how to systematically construct automata from regular expressions and vice versa.
This document provides an overview of glue semantics, a theory of the syntax-semantics interface of natural language that uses linear logic for meaning composition. Glue semantics distinguishes between a meaning logic for semantic representations and a glue logic for specifying how chunks of meaning are assembled. It discusses how linear logic is well-suited for modeling linguistic resources and applications of glue semantics, including examples using lexical functional grammar. The document also covers identity criteria for proofs in glue semantics through lambda equivalence and the Curry-Howard isomorphism.
The document discusses the programming language Scheme, defining key elements like expressions, definitions, programs, and introducing an if expression as a new element. It argues that with evaluation rules defined for each grammar rule, any Scheme program's meaning can be determined. While more forms could be helpful, none are necessary to write any possible program.
The document also compares the complexity and page counts of specifications for Scheme, C++, and English, showing Scheme's simplicity. It asks how languages and new ways of thinking are best learned.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
ESR10 Joachim Daiber - EXPERT Summer School - Malaga 2015RIILP
The document discusses using syntactic preordering models to delimit the morphosyntactic search space for machine translation of morphologically rich languages. It explores preordering dependency trees of the source language to reduce word order variations and predicting morphological attributes on the source side to inform target language word selection. Experimental results show that non-local features and jointly learning which attributes to predict can improve translation performance over baselines. The work aims to combine preordering and morphology prediction to better exploit interactions between syntactic structure and inflectional properties.
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
The document discusses recursive definitions of formal languages using regular expressions. It provides examples of recursively defining languages like INTEGER, EVEN, and factorial. Regular expressions can be used to concisely represent languages. The recursive definition of a regular expression is given. Examples are provided of regular expressions for various languages over an alphabet. Regular languages are those generated by regular expressions, and operations on regular expressions correspond to operations on the languages they represent.
This document discusses examples of regular languages and finite automata. It provides regular expressions and finite automata to represent languages over alphabets like {a,b} with certain string properties, such as beginning and ending with the same letter, containing double letters, or having an even number of as and bs. It also discusses equivalent finite automata and finite automata corresponding to finite languages expressed with regular expressions.
8. Qun Liu (DCU) Hybrid Solutions for TranslationRIILP
The document provides an overview of hybrid machine translation approaches. It discusses selective machine translation which selects the best translation from multiple systems. Pipelined machine translation uses one system for pre-processing or post-processing of another system. Statistical post-editing uses statistical machine translation as a post-editor for rule-based machine translation outputs to improve the translation quality.
This document provides an introduction to the basics of formal language theory for the course CIS511 Introduction to the Theory of Computation. It discusses key concepts such as alphabets, strings, languages, operations on languages, and models of computation including finite automata, pushdown automata, and Turing machines. The document outlines the Chomsky hierarchy of formal grammars and their corresponding families of languages. It aims to provide students with an understanding of formal languages and how they are defined and manipulated.
Context free grammars (CFGs) are formal grammars used to describe the syntax of some natural languages. A CFG provides a simple and mathematically precise way to describe how phrases in a natural language are built from smaller blocks, capturing the recursive structure of sentences. Formally, a CFG is defined as a 4-tuple consisting of a set of nonterminal symbols, a set of terminal symbols, a set of production rules that replace nonterminals with strings of terminals and nonterminals, and a starting symbol. A context-free language is one that can be generated by a CFG.
The document summarizes Word2vec, a neural network model that produces word embeddings from large text corpora. Word2vec takes as input a large text corpus and outputs a vector space with each word assigned a corresponding vector. The model uses a skip-gram architecture that predicts words based on surrounding context words to learn embeddings. It also introduces negative sampling to approximate the training objective. The document then provides an overview of StarSpace, a model that extends Word2vec to learn embeddings for various entities from multiple tasks.
Corpus-based part-of-speech disambiguation of PersianIDES Editor
In this paper we introduce a method for part-ofspeech
disambiguation of Persian texts, which uses word class
probabilities in a relatively small training corpus in order to
automatically tag unrestricted Persian texts. The experiment
has been carried out in two levels as unigram and bi-gram
genotypes disambiguation. Comparing the results gained from
the two levels, we show that using immediate right context to
which a given word belongs can increase the accuracy rate of
the system to a high degree
GSCL2013.A Study of Chinese Word Segmentation Based on the Characteristics of...Lifeng (Aaron) Han
Language Processing and Knowledge in the Web - Proceedings of the International Conference of the German Society for Computational Linguistics and Language Technology, (GSCL 2013), Darmstadt, Germany, on September 25–27, 2013. LNCS Vol. 8105, Volume Editors: Iryna Gurevych, Chris Biemann and Torsten Zesch. (EI)
The document defines definite integrals and area functions. A definite integral from a to b, written as ( ) b a f x , represents the area under the curve y=f(x) between x=a and x=b. The area function A(x) is defined as the area of the shaded region under the curve from a to x, and its derivative is equal to the function f(x). One theorem states that if f is continuous on [a,b] and F is an antiderivative of f, then the definite integral is equal to F(b)-F(a).
The document discusses the relationship between context-free grammars (CFGs) and pushdown automata (PDAs) in describing context-free languages (CFLs). It notes that while CFGs and PDAs are both useful for dealing with CFL properties, PDAs are often easier to use when arguing that a language is context-free. It then presents an algorithm for constructing a PDA from a given CFG that simulates leftmost derivations in the grammar using nondeterminism.
Finite state automata (deterministic and nondeterministic finite automata) provide decisions regarding the acceptance and rejection of a string while transducers provide some output for a given input. Thus, the two machines are quite useful in language processing tasks.
This document discusses three approaches to contrastive analysis for comparing English and French:
1) The structuralist approach compares surface structures but lacks distinction between deep and surface phenomena.
2) Chomsky's approach compares deep grammars but the notion of universals becomes incoherent.
3) The notional approach reflects identity at the deep structure level but requires more reflection on semantic categories. Overall, an adequate approach needs to distinguish between deep and surface structures and consider semantic interpretations.
RuleML2015: Similarity-Based Strict Equality in a Fully Integrated Fuzzy Logi...RuleML
The extension of a given similarity relation R between pairs
of symbols of a particular alphabet to terms built with such symbols
can be implemented at a very high abstract level by a set of fuzzy program
rules defining a predicate called sse. This predicate is defined for
incorporating “Similarity-based Strict Equality” into the new fuzzy logic
language FASILL (acronym of “Fuzzy Aggregators and Similarity Into
a Logic Language”) that we have recently developed in our research
group. FASILL aims to cope with implicit/explicit truth degree annotations,
a great variety of connectives and unification by similarity. In
this paper we show the benefits of using this sophisticated notion of
equality which is somehow inspired by the so-called “Strict Equality”
of functional and functional-logic languages with lazy semantics (e.g.:
Haskell and Curry respectively) and the “Similarity-based Equality”
of fuzzy logic languages using weak unification (Bousi∼Prolog, Likelog),
a notion beyond classic syntactic unification.
This document discusses knowledge-based logical agents and concepts from propositional logic. It introduces how knowledge bases represent what agents know and can be used to determine actions. Propositional logic syntax and semantics are explained using sentences about pits and breezes in the Wumpus world. Key concepts in logic are defined such as logical equivalence, validity, satisfiability and the relationship between inference, entailment and sound/complete derivation of sentences.
This summarizes a document describing a system used in the Aspect Based Sentiment Analysis (ABSA) task of SemEval 2016. The system uses maximum entropy classifiers for aspect category detection and sentiment polarity tasks. Conditional random fields are used for opinion target extraction. It achieved state-of-the-art results in 9 constrained and 2 unconstrained experiments. The system is described including the features used, such as semantics features from word embeddings, and constrained/unconstrained features for the different subtasks and languages.
This document discusses regular languages and grammars. It begins by defining formal languages and describing two approaches to describing languages: the generative approach using grammars and the recognition approach using automata. It then discusses Noam Chomsky's hierarchy of formal grammars and how this classifies the expressive power of grammars. Regular languages are those described by regular grammars and recognized by finite automata. Regular expressions provide another way to describe regular languages. The document proves the equivalence between regular expressions, regular grammars, and finite automata by showing how to systematically construct automata from regular expressions and vice versa.
This document provides an overview of glue semantics, a theory of the syntax-semantics interface of natural language that uses linear logic for meaning composition. Glue semantics distinguishes between a meaning logic for semantic representations and a glue logic for specifying how chunks of meaning are assembled. It discusses how linear logic is well-suited for modeling linguistic resources and applications of glue semantics, including examples using lexical functional grammar. The document also covers identity criteria for proofs in glue semantics through lambda equivalence and the Curry-Howard isomorphism.
The document discusses the programming language Scheme, defining key elements like expressions, definitions, programs, and introducing an if expression as a new element. It argues that with evaluation rules defined for each grammar rule, any Scheme program's meaning can be determined. While more forms could be helpful, none are necessary to write any possible program.
The document also compares the complexity and page counts of specifications for Scheme, C++, and English, showing Scheme's simplicity. It asks how languages and new ways of thinking are best learned.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
ESR10 Joachim Daiber - EXPERT Summer School - Malaga 2015RIILP
The document discusses using syntactic preordering models to delimit the morphosyntactic search space for machine translation of morphologically rich languages. It explores preordering dependency trees of the source language to reduce word order variations and predicting morphological attributes on the source side to inform target language word selection. Experimental results show that non-local features and jointly learning which attributes to predict can improve translation performance over baselines. The work aims to combine preordering and morphology prediction to better exploit interactions between syntactic structure and inflectional properties.
Knowledge Based Reasoning: Agents, Facets of Knowledge. Logic and Inferences: Formal Logic,
Propositional and First Order Logic, Resolution in Propositional and First Order Logic, Deductive
Retrieval, Backward Chaining, Second order Logic. Knowledge Representation: Conceptual
Dependency, Frames, Semantic nets.
The document discusses recursive definitions of formal languages using regular expressions. It provides examples of recursively defining languages like INTEGER, EVEN, and factorial. Regular expressions can be used to concisely represent languages. The recursive definition of a regular expression is given. Examples are provided of regular expressions for various languages over an alphabet. Regular languages are those generated by regular expressions, and operations on regular expressions correspond to operations on the languages they represent.
This document discusses examples of regular languages and finite automata. It provides regular expressions and finite automata to represent languages over alphabets like {a,b} with certain string properties, such as beginning and ending with the same letter, containing double letters, or having an even number of as and bs. It also discusses equivalent finite automata and finite automata corresponding to finite languages expressed with regular expressions.
8. Qun Liu (DCU) Hybrid Solutions for TranslationRIILP
The document provides an overview of hybrid machine translation approaches. It discusses selective machine translation which selects the best translation from multiple systems. Pipelined machine translation uses one system for pre-processing or post-processing of another system. Statistical post-editing uses statistical machine translation as a post-editor for rule-based machine translation outputs to improve the translation quality.
This document provides an introduction to the basics of formal language theory for the course CIS511 Introduction to the Theory of Computation. It discusses key concepts such as alphabets, strings, languages, operations on languages, and models of computation including finite automata, pushdown automata, and Turing machines. The document outlines the Chomsky hierarchy of formal grammars and their corresponding families of languages. It aims to provide students with an understanding of formal languages and how they are defined and manipulated.
Context free grammars (CFGs) are formal grammars used to describe the syntax of some natural languages. A CFG provides a simple and mathematically precise way to describe how phrases in a natural language are built from smaller blocks, capturing the recursive structure of sentences. Formally, a CFG is defined as a 4-tuple consisting of a set of nonterminal symbols, a set of terminal symbols, a set of production rules that replace nonterminals with strings of terminals and nonterminals, and a starting symbol. A context-free language is one that can be generated by a CFG.
The document summarizes Word2vec, a neural network model that produces word embeddings from large text corpora. Word2vec takes as input a large text corpus and outputs a vector space with each word assigned a corresponding vector. The model uses a skip-gram architecture that predicts words based on surrounding context words to learn embeddings. It also introduces negative sampling to approximate the training objective. The document then provides an overview of StarSpace, a model that extends Word2vec to learn embeddings for various entities from multiple tasks.
Corpus-based part-of-speech disambiguation of PersianIDES Editor
In this paper we introduce a method for part-ofspeech
disambiguation of Persian texts, which uses word class
probabilities in a relatively small training corpus in order to
automatically tag unrestricted Persian texts. The experiment
has been carried out in two levels as unigram and bi-gram
genotypes disambiguation. Comparing the results gained from
the two levels, we show that using immediate right context to
which a given word belongs can increase the accuracy rate of
the system to a high degree
GSCL2013.A Study of Chinese Word Segmentation Based on the Characteristics of...Lifeng (Aaron) Han
Language Processing and Knowledge in the Web - Proceedings of the International Conference of the German Society for Computational Linguistics and Language Technology, (GSCL 2013), Darmstadt, Germany, on September 25–27, 2013. LNCS Vol. 8105, Volume Editors: Iryna Gurevych, Chris Biemann and Torsten Zesch. (EI)
The document defines definite integrals and area functions. A definite integral from a to b, written as ( ) b a f x , represents the area under the curve y=f(x) between x=a and x=b. The area function A(x) is defined as the area of the shaded region under the curve from a to x, and its derivative is equal to the function f(x). One theorem states that if f is continuous on [a,b] and F is an antiderivative of f, then the definite integral is equal to F(b)-F(a).
The document discusses the relationship between context-free grammars (CFGs) and pushdown automata (PDAs) in describing context-free languages (CFLs). It notes that while CFGs and PDAs are both useful for dealing with CFL properties, PDAs are often easier to use when arguing that a language is context-free. It then presents an algorithm for constructing a PDA from a given CFG that simulates leftmost derivations in the grammar using nondeterminism.
Finite state automata (deterministic and nondeterministic finite automata) provide decisions regarding the acceptance and rejection of a string while transducers provide some output for a given input. Thus, the two machines are quite useful in language processing tasks.
This document discusses three approaches to contrastive analysis for comparing English and French:
1) The structuralist approach compares surface structures but lacks distinction between deep and surface phenomena.
2) Chomsky's approach compares deep grammars but the notion of universals becomes incoherent.
3) The notional approach reflects identity at the deep structure level but requires more reflection on semantic categories. Overall, an adequate approach needs to distinguish between deep and surface structures and consider semantic interpretations.
RuleML2015: Similarity-Based Strict Equality in a Fully Integrated Fuzzy Logi...RuleML
The extension of a given similarity relation R between pairs
of symbols of a particular alphabet to terms built with such symbols
can be implemented at a very high abstract level by a set of fuzzy program
rules defining a predicate called sse. This predicate is defined for
incorporating “Similarity-based Strict Equality” into the new fuzzy logic
language FASILL (acronym of “Fuzzy Aggregators and Similarity Into
a Logic Language”) that we have recently developed in our research
group. FASILL aims to cope with implicit/explicit truth degree annotations,
a great variety of connectives and unification by similarity. In
this paper we show the benefits of using this sophisticated notion of
equality which is somehow inspired by the so-called “Strict Equality”
of functional and functional-logic languages with lazy semantics (e.g.:
Haskell and Curry respectively) and the “Similarity-based Equality”
of fuzzy logic languages using weak unification (Bousi∼Prolog, Likelog),
a notion beyond classic syntactic unification.
Empirical Study Incorporating Linguistic Knowledge on Filled Pausesfor Pers...Yuta Matsunaga
The document summarizes a study on incorporating linguistic knowledge of filled pauses (FPs) for personalized spontaneous speech synthesis. The study developed a speech synthesis model that can insert FPs at predicted positions and with predicted words based on a target speaker's speech. Experiments showed that including predicted FPs led to more natural and individualized speech than random FPs or no FPs. Reproducing a target speaker's actual FP positions and words further improved naturalness and individuality, but speech quality remained inferior to natural speech. The study demonstrated the importance of modeling FPs appropriately for personalized spontaneous speech synthesis.
Arabic morphology encapsulates many valuable features such as word’s root. Arabic roots are beingutilized for many tasks; the process of extracting a word’s root is referred to as stemming. Stemming is anessential part of most Natural Language Processing tasks, especially for derivative languages such asArabic. However, stemming is faced with the problem of ambiguity, where two or more roots could beextracted from the same word. On the other hand, distributional semantics is a powerful co-occurrence
model. It captures the meaning of a word based on its context. In this paper, a distributional semantics
model utilizing Smoothed Pointwise Mutual Information (SPMI) is constructed to investigate itseffectiveness on the stemming analysis task. It showed an accuracy of 81.5%, with a at least 9.4%improvement over other stemmers.
This document discusses Lexical Functional Grammar (LFG) and Generalized Phrase Structure Grammar (GPSG). LFG was developed in the 1970s and emphasizes analyzing phenomena in lexical and functional terms. It uses two levels of structure: c-structure, which is a tree structure, and f-structure, which captures grammatical functions. GPSG was developed in 1985 and is confined to context-free phrase structure rules. It uses immediate dominance and linear precedence rules.
This document discusses Lexical Functional Grammar (LFG) and Generalized Phrase Structure Grammar (GPSG). LFG was developed in the 1970s and emphasizes analyzing phenomena in lexical and functional terms. It uses two levels of structure: c-structure, which is a tree structure, and f-structure, which captures grammatical functions in an attribute-value matrix. GPSG was developed in 1985 and is confined to context-free phrase structure rules. It uses immediate dominance and linear precedence rules.
Math 150 fall 2020 homework 1 due date friday, october 15,MARRY7
This document contains the homework assignment for Math 150 in Fall 2020. It includes 4 problems to solve and submit by October 15, 2021 at 11:59pm. Problem 1 involves determining if logic formulas are tautologies using reasoning rather than truth tables. Problem 2 gives definitions and asks to prove statements about formulas using induction on complexity. Problem 3 involves proving statements about logical consequence between sets of formulas. Problem 4 asks to determine the truth of statements involving logical consequence between sets of formulas, providing proofs or counterexamples as needed. Practice problems are recommended to work through before attempting the assigned problems.
This document describes a Synchronized Alternating Pushdown Automaton (SAPDA) that accepts the language of reduplication with a center marker (RCM). The SAPDA utilizes recursive conjunctive transitions to check that the nth letter before the center marker '$' is the same as the nth letter from the end of the string, for all letters n. This allows the SAPDA to accept strings of the form w$w, where w is any string over the alphabet {a,b}. The construction of the SAPDA involves states that check specific letters at specific positions relative to the center marker.
Abstract Symbolic Automata: Mixed syntactic/semantic similarity analysis of e...FACE
We introduce a model for mixed syntactic/semantic approximation of programs based on symbolic finite automata (SFA). The edges of SFA are labeled by predicates whose semantics specifies the de- notations that are allowed by the edge. We introduce the notion of abstract symbolic finite automaton (ASFA) where approximation is made by abstract interpretation of symbolic finite automata, act- ing both at syntactic (predicate) and semantic (denotation) level. We investigate in the details how the syntactic and semantic abstractions of SFA relate to each other and contribute to the determination of the recognized language. Then we introduce a family of transformations for simplifying ASFA. We apply this model to prove properties of commonly used tools for similarity analysis of binary executables. Following the structure of their control flow graphs, disassembled binary executables are represented as (con- crete) SFA, where states are program points and predicates repre- sent the (possibly infinite) I/O semantics of each basic block in a constraint form. Known tools for binary code analysis are viewed as specific choices of symbolic and semantic abstractions in our framework, making symbolic finite automata and their abstract interpretations a unifying model for comparing and reasoning about soundness and completeness of analyses of low-level code.
This document discusses methods for aligning word senses between languages using probabilistic sense distributions. It proposes two approaches: 1) Using only monolingual corpora and aligning senses based on similar sense distributions between closely related languages. 2) Leveraging parallel corpora to estimate sense distribution alignments and the most probable translation for each source sense. The approaches are tested on the Europarl corpus, first ignoring and then exploiting sentence alignments. Several examples are examined to validate the sense alignments. Key aspects include using word sense disambiguation to annotate corpora, estimating sense assignment distributions, and assigning translation weights between language pairs based on relative sense frequencies.
SYNTACTIC ANALYSIS BASED ON MORPHOLOGICAL CHARACTERISTIC FEATURES OF THE ROMA...kevig
This paper gives complete guidelines for authors submitting papers for the AIRCC Journals.
This paper refers to the syntactic analysis of phrases in Romanian, as an important process of natural
language processing. We will suggest a real-time solution, based on the idea of using some words or
groups of words that indicate grammatical category; and some specific endings of some parts of sentence.
Our idea is based on some characteristics of the Romanian language, where some prepositions, adverbs or
some specific endings can provide a lot of information about the structure of a complex sentence. Such
characteristics can be found in other languages, too, such as French. Using a special grammar, we
developed a system (DIASEXP) that can perform a dialogue in natural language with assertive and
interogative sentences about a “story” (a set of sentences describing some events from the real life).
Syntactic Analysis Based on Morphological characteristic Features of the Roma...kevig
This paper gives complete guidelines for authors submitting papers for the AIRCC Journals.
This paper refers to the syntactic analysis of phrases in Romanian, as an important process of natural
language processing. We will suggest a real-time solution, based on the idea of using some words or
groups of words that indicate grammatical category; and some specific endings of some parts of sentence.
Our idea is based on some characteristics of the Romanian language, where some prepositions, adverbs or
some specific endings can provide a lot of information about the structure of a complex sentence. Such
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KEYWORDS
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http://airccse.org/journal/ijnlc/papers/1412ijnlc01.pdf
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Why parsing is a part of Language Faculty Science (by Daisuke Bekki)
1. Why parsing is a part of Language Faculty
Science∗
Daisuke Bekki
Ochanomizu University
Faculty of Core Research, Natural Science Division
LFS workshop (online)
December 19, 2020
∗
This work is supported by JST CREST, and JSPS KAKENHI Grant Number
JP18H03284, Japan.
2. 1 Language Faculty Science: Guess, Compute and Compare paradigm
1 Language Faculty Science: Guess, Compute
and Compare paradigm
Language Faculty Science (LFS) is a research program, advocated in
(Hoji, 2015), that aims to discover the properties of the language faculty
(Chomsky, 1965) by adopting the methodology of exact science, which
is also stated as the Guess-Compute-Compare method (Feynman, 1965).
Definition 1.1 Language faculty is that part of the human
mind/brain that is hypothesized to be responsible for our ability
to relate meaning to linguistic sounds/signs. (Hoji, 2015, p.332)
Definition 1.2 Guess-Compute-Compare method emphasizes
the deduction of definite predictions and the pursuit of rigorous testa-
bility of the definite predictions. (Hoji, 2015, p.331)
LFS workshop, December 20, 2020. 2
3. 1.1 Guess: Weak Crossover (WCO)
1.1 Guess: Weak Crossover (WCO)
(1) Hoji (2015, p.73):
a. Every boy praised his father. (under BVA(every boy, his))
b. * His father praised every boy. (under BVA(every boy, his))
Definition 1.3 BVA(A,B) is the dependency interpretation de-
tectable by the informant such that the reference invoked by singular-
denoting expression B co-varies with what is invoked by non-singular-
denoting expression A. (Hoji, 2015, p.327)
The contrast between (1a) and (1b) implies the existence of some
condition on the relation between meaning and linguistic sounds/signs
regarding the availability of BVA readings.
Here are two examples of the hypotheses that we may come up with
by guessing what had caused the contrast in (1):
Hypothesis 1.4 BVA(A,B) is possible only if A c-commands B in LF.1
Hypothesis 1.5 BVA(A,B) is possible only if A precedes B in PF.
Definition 1.6 A c-commands B if and only if A is merged with
what contains B where we understand that the containment relation
is reflexive.
1
Hypothesis 1.4 is a combination of what Hoji (2015) calls Universal hypothesis
and Bridging hypothesis, which have the different status in LFS.
LFS workshop, December 20, 2020. 3
4. 1.2 Compute
1.2 Compute
In order to compute the empirical predictions of Hypothesis 1.4, we put
it together with our general assumptions on language faculty, one of
which is the following Language-particular structural hypothesis about
English.
LE1 S V O in English corresponds to an LF representation where S
assymmetrically c-commands O. (Hoji, 2015, p.33)
Thus the following predictions are born out (but we will come back
to this step later).
(2) A predicted schematic asymmetry (Hoji, 2015, p.34,69)
a. okSchema
NP V [. . . B . . . ] (Under BVA(NP,B))
b. ∗Schema
[. . . B . . . ] V NP (Under BVA(NP,B))
Definition 1.7 ∗Schema is such that any Example that instantiates
it is completely unacceptable with the specified dependency inter-
pretation. (Hoji, 2015, p.336)
In words:
1. Every instantiation of okSchema (=okExample) may be acceptable
under the BVA(NP,B) reading.
LFS workshop, December 20, 2020. 4
5. 1.2 Compute
2. Every instantiation of ∗Schema (=∗Example) is unacceptable un-
der the BVA(NP,B) reading.
From (2), we further predict that an instantiation of (2a) as (3a) can
be ok, and an instantiation of (2b) as (3b) is out:
(3) a. An okExample instantiating the okSchema:
Every boy praised his father. (under BVA(every boy, his))
b. An ∗Example instantiating the ∗Schema:
* His father praised every boy. (under BVA(every boy, his))
LFS workshop, December 20, 2020. 5
6. 1.3 Compare
1.3 Compare
The status of okSchema and ∗Schema are asymmetrical:
1. An okJudgment on ∗Example disconfirms the hypothesis.
2. A ∗Judgment on okExample does not disconfirm, the hypothesis.
though a ∗Judgment on okExample suggests that the experiment is not
designed well.
Best Result: Next-best Result:
Judgment
∗Example *
okExample ok
Judgment
∗Example *
okExample *
Bad Result: Worst Result:
Judgment
∗Example ok
okExample ok
Judgment
∗Example ok
okExample *
LFS workshop, December 20, 2020. 6
7. 2 A missing link in Compute
2 A missing link in Compute
Let us repeat here the okSchema and ∗Schema from Hypothesis 1.4.
(4) a. okSchema
NP V [. . . B . . . ] (Under BVA(NP,B))
b. ∗Schema
[. . . B . . . ] V NP (Under BVA(NP,B))
together with the predictions:
1. Every instantiation of okSchema (=okExample) may be acceptable
under the BVA(NP,B) reading.
2. Every instantiation of ∗Schema (=∗Example) is unacceptable un-
der the BVA(NP,B) reading.
The deduction process of Compute, from the Hypothesis 1.4 to the
prediction above, factors through the following propositions.
(5) a. In every instantiation of okSchema, NP c-commands B in LF.
b. In every instantiation of ∗Schema, NP does not c-command
B in LF.
The proposition (5a) is deduced from LE1, repeated below.
LE1 S V O in English corresponds to an LF representation where S
assymmetrically c-commands O. (Hoji, 2015, p.33)
LFS workshop, December 20, 2020. 7
8. 2 A missing link in Compute
However, to deduce the proposition (5b), we need something like the
following:
Proposition 2.1 S V O in English never corresponds to an LF repre-
sentation where some node in O assymmetrically c-commands S.
This is a missing link in the duduction process of Compute in Hoji
(2015)’s version of LFS.
Note that, it is not enough to remedy this missing link by asserting
that NP does not c-command B in the structure specified in (4b). First,
what is presented to an informant is a string without any specification
on its syntactic structure. Thus, we have to assume the following.
An instantiation of a Schema is a string of linguistic sign/sound, not
a syntactic structure.
We should also assume the following proposition:
Most sentences (as strings) are syntactically and/or lexically am-
biguous, i.e., one or more LF representations correspond to a given
sentence.
Therefore, we cannot logically deduce (5b). Actually, (5b) does
not hold in general, since an instantiation of ∗Schema may correspond
to several syntactic structures, some of which may be those specified
by ∗Schema, but the others may be different structures where NP c-
commands B.
LFS workshop, December 20, 2020. 8
9. 2 A missing link in Compute
How crutial is the missing link? Suppose that there exists an LF
where NP c-commands B in ∗Example. Consider first the case where
some informant’s judgment is okJudgment:
Judgment
∗Example ok
This is supposed to disconfirm Hypothesis 1.4 in LFS, but acutu-
ally it does not. It is completely reasonable judgment when both our
grammar and Hypothesis 1.4 are correct. On the other hand, there
also remain the possibility that either our grammar or Hypothesis 1.4 is
wrong,
Consider second the case where some informant’s judgment is ∗Judgment:
Judgment
∗Example *
This happens when the informant lacks resorcefullness enough to
find out a LF representation where A c-commans B. This is supposed to
support Hypothesis 1.4 in LFS, however, we also obtain this result when
our gramamr is wrong, or Hypothesis 1.4 is wrong.
In summary, the missing link not only is problematic for maintain-
ing the strong deducibility of predictions in LFS, but is problematic as
well from the perspective of learning from the errors (Hoji, 2015, p.61),
since the missing link obscures the factors behind the judgment for the
∗Example.
LFS workshop, December 20, 2020. 9
10. 3 Parsing as a part of LFS
3 Parsing as a part of LFS
3.1 The main thesis
LFS must ensure that each instantiation of ∗Schema does not have other
syntactic structures with A c-commanding B. For this purpose, we need
a way to know every syntactic structure that corresponds to ∗Example,
or more generally, LFS needs a theoretical component that tells us all
possible syntactic structures of a given sentence (as a string). This is
exactly what parser does.
Claim 3.1 Parser is a part of LFS, which ensures that each okExample and
each ∗Example is associated only with syntactic structures that are in-
tended in okSchema and ∗Schema.
3.2 Competence versus performance
Since Chomsky (1965), parsing is a task classified as belonging to per-
formance of language faculty, thus is regarded as a target whose in-
vestigation should be postponed until the investigation of competence
gets matured. However, parsing has an aspect which purely belongs to
linguistic competence, which we call n¨aive parsing/parser.
Definition 3.2 (Grammar) Suppose that Σ is a set of all strings
(or linearized PF-forms) and Λ is a set of all LF-(tree-)structures. A
grammar is a subset of Σ×Λ, or equally, an element of Pow(Σ×Λ).
LFS workshop, December 20, 2020. 10
11. 3.2 Competence versus performance
Definition 3.3 (N¨aive parser) A n¨aive parser for a grammar G is
a function that takes a string (or linearized PF-form) σ and returns
{λ | (σ, λ) ∈ G }. In words, a n¨aive parser returns a set of all LF-
(tree-)structures each of which is associated with σ in G.
The same argument applies to generator/generation.
Definition 3.4 (N¨aive generator) A n¨aive generator for a gram-
mar G is a function that takes an LF-(tree-)structure λ and returns
{σ | (σ, λ) ∈ G }. In words, a n¨aive generator returns a set of all
strings (or linearized PF-forms) each of which is associated with λ
in G.
As read off from the above definitions, the notions of n¨aive parsing
and n¨aive generator are solely defined by a given grammar. Thus, if we
consider the notion of grammar as a part of linguistic competence, so
are the notions of n¨aive parser and generator.
N¨aive parser is not computationally efficient to compute all the pos-
sible LF-structures for a given string, and it is commonly believed that
human parsing employs some language model that enables us to guess
what is the most plausible structure for a given string. Most computa-
tional parsers that have been developed in the research field of compu-
tational linguistics pose the same assumption.
These non-n¨aive parsers, or at least the computational parsers, are
developed as a combination of the knowledge of competence grammar
LFS workshop, December 20, 2020. 11
12. 3.3 Parsing versus structural hypothesis
and some mechanisms concerning linguistic performance, such as lan-
guage models and heuristic search. The adjective n¨aive is used to make
a clear distinction between the parser/generator that concerns linguistic
competence only and the parser/generator for linguistic performance.
3.3 Parsing versus structural hypothesis
Language-particular structural hypothesis about English
LE1 S V O in English correspons to an LF representation where S
assymmetrically c-commands O. (Hoji, 2015, p.33)
LE2 O S V in English can correspond to an LF representation where S
c-commands O. (Hoji, 2015, p.35)
• Hard to pose a hypothesis for every sentence pattern.
• These hypothesis are derived by parsers.
4 LFS with a parser
4.1 Schemata in CCG
(6) okSchema
AT/(TNP) [. . . BNP/N . . . ]SNP (Under BVA(A,B))
(7) ∗Schema
[. . . BNP/N . . . ]T/(TNP) [. . . AT/(TNP) . . . ]SNP (Under BVA(A,B))
LFS workshop, December 20, 2020. 12
13. 4.2 Demonostration
Lexical items for A in BVA(A,B):
every T/(TNP)/N
no T/(TNP)/N
Lexical items for B in BVA(A,B):
his NP/N
Other lexical items:
boy N
praised SNP/NP
father N
4.2 Demonostration
(8) Mary saw a boy with a telescope.
a. Mary [saw a [boy with a telescope]].
b. Mary [[saw a boy] with a telescope].
(9) a. Mary saw every astronomer with his telescope.
(under the reading BVA(every astronomer, his))
b. Mary saw his owner with every telescope.
(under the reading BVA(every telescope, his))
LFS workshop, December 20, 2020. 13
14. 4.2 Demonostration
Suppose that an LFS researcher claims Hypothesis 1.4, and set up
an experiment with the following Schema.
(10) a. okSchema
A [VP [...B...]]
b. ∗Schema
NP [[TV A] [...B...]]
Then each of the following is an instantiation of (4.2).
(11) a. Every astronomer [[saw Mary] [with his telescope]].
b. Mary saw every astronomer with his telescope.
the latter of which is based on the following structure:
(12) Mary [[saw [every astronomer]] [with [his telescope]]]
But (my guess is that) most informants will judge (11b) as ok, based
on the following syntactic strucuture:
(13) Mary [saw [every [astronomer [with [his telescope]]]]]
Exercise 4.1 Conduct the Guess/Compute/Compare method on the
examples shown in (9), assume Hypothesis 1.4 again, and design a set
of experiment to test it.
LFS workshop, December 20, 2020. 14
15. REFERENCES REFERENCES
References
Chomsky, N. (1965) Aspects of the Theory of Syntax. The MIT Press.
Feynman, R. (1965) The character of physical law, Beitr¨age zur Philoso-
phie des deutschen Idealismus. New York, The Modern Library.
Hoji, H. (2015) Language Faculty Science. Cambridge.
戸次 大介.(2010) 『日本語文法の形式理論—活用体系・統語構造・意味
合成—』,くろしお出版.
LFS workshop, December 20, 2020. 15