2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023)
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2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. It will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. The Conference looks for significant contributions to all major fields of the Natural Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.
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Call for Papers - 2 nd International Conference on NLP, Data Mining and Machi...
2
nd International Conference on NLP, Data Mining and Machine Learning (NLDML
2023) will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of Natural Language Computing, Data Mining and
Machine Learning.
It will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Natural Language Computing, Data Mining and Machine
Learning. The Conference looks for significant contributions to all major fields of the Natural
Language Computing, Data Mining and Machine Learning in theoretical and practical
aspects.
2 nd International Conference on NLP, Data Mining and Machine Learning (NLDML...
2
nd International Conference on NLP, Data Mining and Machine Learning (NLDML
2023) will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of Natural Language Computing, Data Mining and
Machine Learning.
It will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Natural Language Computing, Data Mining and Machine
Learning. The Conference looks for significant contributions to all major fields of the Natural
Language Computing, Data Mining and Machine Learning in theoretical and practical
aspects.
Call for Papers - 2nd International Conference on NLP, Data Mining and Machin...
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
It will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Natural Language Computing, Data Mining and Machine
Learning. The Conference looks for significant contributions to all major fields of the Natural
Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML ...
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
Call for Papers - 2nd International Conference on NLP, Data Mining and Machin...
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
It will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. The Conference looks for significant contributions to all major fields of the Natural Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.
Call for Papers - 2nd International Conference on NLP, Data Mining and Machin...
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
It will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. The Conference looks for significant contributions to all major fields of the Natural Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.
Call for Paper - 2nd International Conference on NLP, Data Mining and Machine...
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML ...
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
Recommended
Call for Papers - 2 nd International Conference on NLP, Data Mining and Machi...
2
nd International Conference on NLP, Data Mining and Machine Learning (NLDML
2023) will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of Natural Language Computing, Data Mining and
Machine Learning.
It will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Natural Language Computing, Data Mining and Machine
Learning. The Conference looks for significant contributions to all major fields of the Natural
Language Computing, Data Mining and Machine Learning in theoretical and practical
aspects.
2 nd International Conference on NLP, Data Mining and Machine Learning (NLDML...
2
nd International Conference on NLP, Data Mining and Machine Learning (NLDML
2023) will provide an excellent international forum for sharing knowledge and results in
theory, methodology and applications of Natural Language Computing, Data Mining and
Machine Learning.
It will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Natural Language Computing, Data Mining and Machine
Learning. The Conference looks for significant contributions to all major fields of the Natural
Language Computing, Data Mining and Machine Learning in theoretical and practical
aspects.
Call for Papers - 2nd International Conference on NLP, Data Mining and Machin...
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
It will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Natural Language Computing, Data Mining and Machine
Learning. The Conference looks for significant contributions to all major fields of the Natural
Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML ...
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
Call for Papers - 2nd International Conference on NLP, Data Mining and Machin...
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
It will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. The Conference looks for significant contributions to all major fields of the Natural Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.
Call for Papers - 2nd International Conference on NLP, Data Mining and Machin...
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
It will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. The Conference looks for significant contributions to all major fields of the Natural Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.
Call for Paper - 2nd International Conference on NLP, Data Mining and Machine...
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML ...
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
Call for Papers - 2nd International Conference on NLP, Data Mining and Machin...
2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
It will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. The Conference looks for significant contributions to all major fields of the Natural Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.
Call for Research Articles - 3rd International Conference on NLP, Data Mining...
3rd International Conference on NLP, Data Mining and Machine Learning (NLDML 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. It will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. The Conference looks for significant contributions to all major fields of the Natural Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.
Call for Research Articles - 3rd International Conference on NLP, Data Mining...
3rd International Conference on NLP, Data Mining and Machine Learning (NLDML 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. It will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. The Conference looks for significant contributions to all major fields of the Natural Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.
Call for Papers - International Conference on NLP, Data Mining and Machine Le...
International Conference on NLP, Data Mining and Machine Learning (NLDML 2022)
will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Natural Language Computing, Data Mining and Machine
Learning.
It will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Natural Language Computing, Data Mining and Machine
Learning. The Conference looks for significant contributions to all major fields of the Natural
Language Computing, Data Mining and Machine Learning in theoretical and practical
aspects.
Call for Paper - International Conference on NLP, Data Mining and Machine Lea...
International Conference on NLP, Data Mining and Machine Learning (NLDML 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
Call for Papers - International Conference on NLP, Data Mining and Machine Le...
International Conference on NLP, Data Mining and Machine Learning (NLDML 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
3rd International Conference on NLP, Data Mining and Machine Learning (NLDML...
3rd International Conference on NLP, Data Mining and Machine Learning (NLDML 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. It will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. The Conference looks for significant contributions to all major fields of the Natural Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.
Call for Paper - International Conference on NLP, Data Mining and Machine Lea...
International Conference on NLP, Data Mining and Machine Learning (NLDML 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
Call for Paper - International Conference on NLP, Data Mining and Machine Lea...
International Conference on NLP, Data Mining and Machine Learning (NLDML 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
Online Articles Submission - 3rd International Conference on NLP, Data Mining...
3rd International Conference on NLP, Data Mining and Machine Learning (NLDML 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. It will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. The Conference looks for significant contributions to all major fields of the Natural Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.
Online Papers Submission - 3rd International Conference on NLP, Data Mining a...
3rd International Conference on NLP, Data Mining and Machine Learning (NLDML 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. It will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. The Conference looks for significant contributions to all major fields of the Natural Language Computing, Data Mining and Machine Learning in theoretical and practical aspects.
International Conference on NLP, Data Mining and Machine Learning (NLDML 2022)
International Conference on NLP, Data Mining and Machine Learning (NLDML 2022)
will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Natural Language Computing, Data Mining and Machine
Learning.
It will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Natural Language Computing, Data Mining and Machine
Learning. The Conference looks for significant contributions to all major fields of the Natural
Language Computing, Data Mining and Machine Learning in theoretical and practical
aspects.
Call for Paper - International Conference on NLP, Data Mining and Machine Lea...
International Conference on NLP, Data Mining and Machine Learning (NLDML 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning.
International Conference on NLP, Data Mining and Machine Learning (NLDML 2022)
International Conference on NLP, Data Mining and Machine Learning (NLDML 2022)
will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Natural Language Computing, Data Mining and Machine
Learning.
It will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Natural Language Computing, Data Mining and Machine
Learning. The Conference looks for significant contributions to all major fields of the Natural
Language Computing, Data Mining and Machine Learning in theoretical and practical
aspects.
Call for Paper - International Conference on Machine Learning, NLP and Data M...
International Conference on Machine Learning, NLP and Data Mining (MLDA 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning, Natural Language Computing and Data Mining. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only.
Call for Papers - International Conference on Machine Learning, NLP and Data ...
International Conference on Machine Learning, NLP and Data Mining (MLDA 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning, Natural Language Computing and Data Mining. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only.
Call for Paper - International Conference on Machine Learning, NLP and Data M...
International Conference on Machine Learning, NLP and Data Mining (MLDA 2022)
will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Machine Learning, Natural Language Computing and Data
Mining. Authors are solicited to contribute to the conference by submitting articles that
illustrate research results, projects, surveying works and industrial experiences that describe
significant advances in the following areas, but are not limited to these topics only.
Call for Paper - International Conference on Machine Learning, NLP and Data M...
International Conference on Machine Learning, NLP and Data Mining (MLDA 2022) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning, Natural Language Computing and Data Mining. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only.
Online Paper Submission - 2nd International Conference on NLP & AI (NLPAI 2024)
2nd International Conference on NLP & AI (NLPAI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Processing and Artificial Intelligence.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to:
Online Paper Submission - 2nd International Conference on NLP & AI (NLPAI 2024)
2nd International Conference on NLP & AI (NLPAI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Processing and Artificial Intelligence.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to:
Effect of Query Formation on Web Search Engine Results
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 Dogri
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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Call for Paper - International Conference on NLP, Data Mining and Machine Lea...
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will provide an excellent international forum for sharing knowledge and results in theory,
methodology and applications of Natural Language Computing, Data Mining and Machine
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methodology and applications of Natural Language Computing, Data Mining and Machine
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Effect of Query Formation on Web Search Engine Results
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
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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 Dogri
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 Computing
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 Perception
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 Models
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 Models
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.
IJNLC 2013 - Ambiguity-Aware Document Similarity
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 Tagging
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 Punjabi
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 Optimization
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 Structure
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 Generation
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...
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 Language
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 OPTIMIZATION
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 Structure
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 GENERATION
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...
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 LANGUAGE
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.
February 2024 - Top 10 cited articles.pdf
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.
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May 2024 - Top10 Cited Articles in Natural Language Computing
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Effect of Singular Value Decomposition Based Processing on Speech Perception
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Genetic Approach For Arabic Part Of Speech Tagging
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Rule Based Transliteration Scheme for English to Punjabi
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Improving Dialogue Management Through Data Optimization
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Document Author Classification using Parsed Language Structure
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Rag-Fusion: A New Take on Retrieval Augmented Generation
Rag-Fusion: A New Take on Retrieval Augmented Generation
Performance, Energy Consumption and Costs: A Comparative Analysis of Automati...
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Document Author Classification Using Parsed Language Structure
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RAG-FUSION: A NEW TAKE ON RETRIEVALAUGMENTED GENERATION
RAG-FUSION: A NEW TAKE ON RETRIEVALAUGMENTED GENERATION
Performance, energy consumption and costs: a comparative analysis of automati...
Performance, energy consumption and costs: a comparative analysis of automati...
EVALUATION OF MEDIUM-SIZED LANGUAGE MODELS IN GERMAN AND ENGLISH LANGUAGE
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https://alandix.com/academic/papers/synergy2024-epistemic/
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Pushing the limits of ePRTC: 100ns holdover for 100 days
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2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023)
- 1. 2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023) January 21 ~ 22, 2023, Virtual Conference https://nldml2023.org/ Scope & Topics 2nd International Conference on NLP, Data Mining and Machine Learning (NLDML 2023) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. It will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing, Data Mining and Machine Learning. The Conference looks for significant contributions to all major fields of the Natural Language Computing, Data Mining and Machine Learning in theoretical and practical aspects. Topics of interest include, but are not limited to, the following Argumentation Mining and Applications Big Data & Business Intelligence Corpus Analysis Data Mining Foundations & Applications Deep Learning, Neural Languages and NLP Information Extraction, Retrieval Knowledge Processing Machine Learning Applications Machine Learning in knowledge-intensive systems Machine Learning Methods and analysis Machine Learning Problems Natural language and Ubiquitous Computing Natural Language in Conceptual Modeling NLP techniques for Ambient Intelligence NLP techniques for Internet of Things (IoT) Question Answering (QA) Semantic Processing & NLP Semantic Web, Open Linked Data, and Ontologies Social Media and Web Analytics Text Mining Paper Submission Authors are invited to submit papers through the conference Submission System by December 31, 2022 (Final Call). Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by International Journal on Cybernetics & Informatics (IJCI) (Confirmed). Selected papers from NLDML 2023, after further revisions, will be published in the special issue of the following journals. International Journal on Natural Language Computing (IJNLC)
- 2. International Journal of Web & Semantic Technology (IJWesT) International Journal of Ubiquitous Computing (IJU) International Journal of Data Mining & Knowledge Management Process (IJDKP) International Journal of Ambient Systems and Applications (IJASA) Machine Learning and Applications: An International Journal (MLAIJ) International Journal on Computational Science & Applications (IJCSA) Advances in Vision Computing: An International Journal (AVC) Important Dates Submission Deadline: December 31, 2022 (Final Call) Authors Notification: January 12, 2023 Registration & Camera - Ready Paper Due: January 15, 2023 Contact Us Here's where you can reach us: nldml@nldml2023.org (or) nldmlconf@yahoo.com For more details, please visit: https://nldml2023.org/ Paper Submission Link: https://nldml2023.org/submission/index.php