International Journal on Natural Language Computing (IJNLC) (original) (raw)

Papers by International Journal on Natural Language Computing (IJNLC)

Natural Language Processing is a programmed approach to analyze text that is based on both a set ... more 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.

Text analysis has been attracting increasing attention in this data era. Selecting effective feat... more Text analysis has been attracting increasing attention in this data era. Selecting effective
features from datasets is a particular important part in text classification studies. Feature
selection excludes irrelevant features from the classification task, reduces the dimensionality
of a dataset, and improves the accuracy and performance of identification. So far, so many
feature selection methods have been proposed, however, it remains unclear which method is
the most effective in practice. This article focuses on evaluating and comparing the available
feature selection methods in general versatility regarding authorship attribution problems and
tries to identify which method is the most effective. The discussions on general versatility of
feature selection methods and its connection in selecting the appropriate features for varying
data were done. In addition, different languages, different types of features, different systems
for calculating the accuracy of SVM (support vector machine), and different criteria for
determining the rank of feature selection methods were used to measure the general
versatility of these methods together. The analysis results indicate the best feature selection
method is different for each dataset; however, some methods can always extract useful
information to discriminate the classes. The chi-square was proved to be a better method
overall.

Machine translation is the process of translating text from one language to another. In this pape... more Machine translation is the process of translating text from one language to another. In this paper, Statistical Machine Translation is done on Assamese and English language by taking their respective parallel corpus. A statistical phrase based translation toolkit Moses is used here. To develop the language model and to align the words we used two another tools IRSTLM, GIZA respectively. BLEU score is used to check our translation system performance, how good it is. A difference in BLEU scores is obtained while translating sentences from Assamese to English and vice-versa. Since Indian languages are morphologically very rich hence translation is relatively harder from English to Assamese resulting in a low BLEU score. A statistical transliteration system is also introduced with our translation system to deal basically with proper nouns, OOV (out of vocabulary) words which are not present in our corpus.

Computer Science and Information Technology ======================================== The AIRCC's ... more Computer Science and Information Technology
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The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.

Wireless & Mobile Network
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The International Journal of Wireless & Mobile Networks (IJWMN) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Wireless & Mobile Networks. The journal focuses on all technical and practical aspects of Wireless & Mobile Networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced wireless & mobile networking concepts and establishing new collaborations in these areas.

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.

International Journal of Computational Science and Information Technology (IJCSITY) focuses on Co... more International Journal of Computational Science and Information Technology (IJCSITY) focuses on Complex systems, information and computation using mathematics and engineering techniques. This is an open access peer-reviewed journal will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area of Computation theory and applications. It will also serve to facilitate the exchange of information between researchers and industry professionals to discussthelatestissuesandadvancementintheareaofadvancedComputationanditsapplications.

IJNLC, 2022

Natural Language Processing is a programmed approach to analyze text that is based on both a set ... more 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.

IJNLC, 2022

Natural Language Processing is a programmed approach to analyze text that is based on both a set ... more 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.

Machine Transliteration has come out to be an emerging and a very important research area in the ... more 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.

In the era of Artificial Intelligence (AI), significant progress has been made by enabling machin... more In the era of Artificial Intelligence (AI), significant progress has been made by enabling machines to understand and communicate in human languages. Central to this progress are parsers, which play a vital role in syntactic analysis and support various Natural language Processing (NLP) applications, including Machine Translation and sentiment analysis. This paper introduces a robust implementation of an optimized Head-Driven Parser designed to advance NLP capabilities beyond the limitations of traditional Lexicalized Tree Adjoining Grammar (L-TAG) based Parser. Traditional parser, while effective, often struggle with the capturing complexities of natural languages, especially translation between English to Indian languages. By leveraging Bi-directional approach and Head-Driven techniques, this research offers a revolutionary enhancement in parsing frameworks. This method not only improves performance in syntactic analysis but also facilitates complex tasks such as discourse analysis and semantic parsing. This research involves experimentation the Bi-Directional Parser on a dataset of 15,000 sentences, resulting a reduction in derivation variations compared to conventional TAG Parsers. This advancement highlights how Head-Driven Parsing can overcome traditional constraints and provide more reliable linguistic analysis. The paper demonstrates how this new implementation not only builds on the strengths of L-TAG but also addresses its limitations and contributes to expanding the scope of Tree Adjoining Grammarbased methodologies and advancing the field of Machine Translation.

Text-based comments play a crucial role in providing feedback for various industries. However, ef... more Text-based comments play a crucial role in providing feedback for various industries. However, effectively filtering and categorizing this feedback based on custom context-specific criteria requires sophisticated language modeling techniques. While traditional approaches have shown effectiveness, they often require a substantial amount of data to compensate for their modeling deficiencies. In this work, we focus on highlighting the performance and limitations of prompt-free few-shot text classification using open-source pre-trained sentence transformers. On the one hand, our research includes a comprehensive study across different benchmark datasets, encompassing 9 dimensions such as sentiment analysis, topic modeling, grammatical acceptance, and emotion classification. Also, we worked at making different experiences to test Prompt-Free Few-Shot Text Classification. On the other hand, we underline prompt-free few-shot classification limitations when the targeted criteria are complex. As an alternative approach, prompting an instruction-fine-tuned language model has demonstrated favorable outcomes, as proven by our application in the specific use case of “Identifying and extracting resolution results and actions from explanatory notes”, achieving an accuracy rate of 80%.

Information Retrieval (IR) is a very important and vast area. While searching for context web ret... more Information Retrieval (IR) is a very important and vast area. While searching for context web returns all the results related to the query. Identifying the relevant result is most tedious task for a user. Word Sense Disambiguation (WSD) is the process of identifying the senses of word in textual context, when word has multiple meanings. We have used the approaches of WSD. This paper presents a Proposed Dynamic Page Rank algorithm that is improved version of Page Rank Algorithm. The Proposed Dynamic Page Rank algorithm gives much better results than existing Google’s Page Rank algorithm. To prove this we have calculated Reciprocal Rank for both the algorithms and presented comparative results.

The fundamental techniques used for man-machine communication include Speech synthesis, speech re... more The fundamental techniques used for man-machine communication include Speech synthesis, speech recognition, and speech transformation. Feature extraction techniques provide a compressed representation of the speech signals. The HNM analyses and synthesis provides high quality speech with less number of parameters. Dynamic time warping is well known technique used for aligning two given multidimensional sequences. It locates an optimal match between the given sequences. The improvement in the alignment is estimated from the corresponding distances. The objective of this research is to investigate the effect of dynamic time warping on phrases, words, and phonemes based alignments. The speech signals in the form of twenty five phrases were recorded. The recorded material was segmented manually and aligned at sentence, word, and phoneme level. The Mahalanobis distance (MD) was computed between the aligned frames. The investigation has shown better alignment in case of HNM parametric domain. It has been seen that effective speech alignment can be carried out even at phrase level.

Named Entity Recognition (NER) is considered as one of the key task in the field of Information R... more Named Entity Recognition (NER) is considered as one of the key task in the field of Information Retrieval. NER is the method of recognizing Named Entities (NEs) in a corpus and then organizing these NEs into diverse classes of NEs e.g. Name of Location, Person, Organization, Quantity, Time, Percentage etc. Today, there is a great need to develop a tool for NER, since the existing tools are of limited scope. In this paper, we would discuss the functionality and features of our tool of NER with some experimental results.

Universal Networking Language (UNL) has been used by various researchers as an Interlingua approa... more Universal Networking Language (UNL) has been used by various researchers as an Interlingua approach for AMT (Automatic machine translation). The UNL system consists of two main components/tools, namely, EnConverter-IAN (used for converting the text from a source language to UNL) and DeConverter - EUGENE (used for converting the text from UNL to a target language). This paper highlights the DeConversion generation rules used for the DeConverter and indicates its usage in the generation of Punjabi sentences. This paper also covers the results of implementation of UNL input by using DeConverter-EUGENE and its evaluation on UNL sentences such as Nouns, Pronouns and Prepositions.

The process of browsing Search Results is one of the major problems with traditional Web search e... more The process of browsing Search Results is one of the major problems with traditional Web search engines for English, European, and any other languages generally, and for Arabic Language particularly. This process is absolutely time consuming and the browsing style seems to be unattractive. Organizing Web search results into clusters facilitates users quick browsing through search results. Traditional clustering techniques (data-centric clustering algorithms) are inadequate since they don't generate clusters with highly readable names or cluster labels. To solve this problem, Description-centric algorithms such as Suffix Tree Clustering (STC) algorithm have been introduced and used successfully and extensively with different adapted versions for English, European, and Chinese Languages. However, till the day of writing this paper, in our knowledge, STC algorithm has been never applied for Arabic Web Snippets Search Results Clustering. In this paper, we propose first, to study how STC can be applied for Arabic Language? We then illustrate by example that is impossible to apply STC after Arabic Snippets pre-processing (stem or root extraction) because the Merging process yields many redundant clusters. Secondly, to overcome this problem, we propose to integrate STC in a new scheme taking into a count the Arabic language properties in order to get the web more and more adapted to Arabic users. The proposed approach automatically clusters the web search results into high quality, and high significant clusters labels. The obtained clusters not only are coherent, but also can convey the contents to the users concisely and accurately. Therefore the Arabic users can decide at a glance whether the contents of a cluster are of interest. Preliminary experiments and evaluations are conducted and the experimental results show that the proposed approach is effective and promising to facilitate Arabic users quick browsing through Search Results. Finally, a recommended platform for Arabic Web Search Results Clustering is established based on Google search engine API.

Publishing and sharing content through software has become a regular part of Social Computing tod... more Publishing and sharing content through software has become a regular part of Social Computing today [the term Social Computing is used in the sense defined in Wikipedia (Social computing - Wikipedia, defined in [6].)]. This paper shows how we can achieve social cohesion despite varied software pieces working in their unique way and providing their specialized content. It defines a software methodology to design better socially responsive software by representing Intentions in code and using that as an open inter-component communication mechanism with more ownership and responsibility for both publisher and receiver. Intention Space introduces, for the first time, a close knitting of context and intentions at the core of the computation process, which has not been possible until now.

Enabling business users to directly query their data sources is a significant advantage for organ... more Enabling business users to directly query their data sources is a significant advantage for organisations. The majority of enterprise data is housed within databases, requiring extensive procedures that involve intermediary layers for reporting and its related customization. The concept of enabling natural language queries, where a chatbot can interpret user questions into database queries and promptly return results, holds promise for expediting decision-making and enhancing business responsiveness. This approach empowers experienced users to swiftly obtain data-driven insights. The integration of Text-to-SQL and Large Language Model (LLM) capabilities represents a solution to this challenge, offering businesses a powerful tool for query automation. However, security concerns prevent organizations from granting direct database access akin to platforms like OpenAI. To address this limitation, this Paper proposes developing fine-tuned small/medium LLMs tailored to specific domains like retail and supply chain. These models would be trained on domain-specific questions and Queries that answer these questions based on the database table structures to ensure efficacy and security. A pilot study is undertaken to bridge this gap by fine-tuning selected LLMs to handle business-related queries and associated database structures, focusing on sales and supply chain domains. The research endeavours to experiment with zero-shot and fine-tuning techniques to identify the optimal model. Notably, a new dataset is curated for fine-tuning, comprising business-specific questions pertinent to the sales and supply chain sectors. This experimental framework aims to evaluate the readiness of LLMs to meet the demands for business query automation within these specific domains. The study contributes to the progression of natural language query processing and database interaction within the realm of business intelligence applications.

This paper presents some generalities about the VNSCalendar system, a tool able to understand use... more This paper presents some generalities about the VNSCalendar system, a tool able to understand users’ voice commands, would help users with managing and querying their personal calendar by Vietnamese speech. The main feature of this system consists in the fact that it is equipped with a mechanism of analyzing syntax and semantics of Vietnamese commands and questions. The syntactic and semantic processing of Vietnamese sentences is solved by using DCG (Definite Clause Grammar) and the methods of formal semantics. This is the first system in this field of voice application, which is equipped an effective semantic processing mechanism of Vietnamese language. Having been built and tested in PC environment, our system proves its accuracy attaining more than 91%.

Simulation of human vocal tract model is necessary to understand the effect of different conditio... more Simulation of human vocal tract model is necessary to understand the effect of different conditions on speech generation. In natural calamities such as earthquakes person may swallow soil due to large amount of dust around. The effect of soil is investigated on the sound pressure in the human vocal tract. The generation of speech starts from the glottis, so the soil obstacle is positioned near it. The whole model is designed and analyzed using FEM. As the speech signal requires a bandwidth near about 4 kHz, the investigation carried out to obtained sound pressure in the human vocal tract from 100-5000 Hz sound signals.

The process of identification of Named Entities (NEs) in a given document and then there classifi... more The process of identification of Named Entities (NEs) in a given document and then there classification into different categories of NEs is referred to as Named Entity Recognition (NER). We need to do a great effort in order to perform NER in Indian languages and achieve the same or higher accuracy as that obtained by English and the European languages. In this paper, we have presented the results that we have achieved by performing NER in Hindi, Bengali and Telugu using Hidden Markov Model (HMM) and Performance Metrics.

Query in a search engine is generally based on natural language. A query can be expressed in more... more 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.

Speech generation is one of the most important areas of research in speech signal processing whic... more 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.

Speech is an important biological signal for primary mode of communication among human being and ... more 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.

Relevance evaluation of a query and a passage is essential in Information Retrieval (IR). Recentl... more Relevance evaluation of a query and a passage is essential in Information Retrieval (IR). Recently, numerous studies have been conducted on tasks related to relevance judgment using Large Language Models (LLMs) such as GPT-4, demonstrating significant improvements. However, the efficacy of LLMs is considerably influenced by the design of the prompt. The purpose of this paper is to identify which specific terms in prompts positively or negatively impact relevance evaluation with LLMs. We employed two types of prompts: those used in previous research and generated automatically by LLMs. By comparing the performance of these prompts in both few-shot and zero-shot settings, we analyze the influence of specific terms in the prompts. We have observed two main findings from our study. First, we discovered that prompts using the term ‘answer’ lead to more effective relevance evaluations than those using ‘relevant.’ This indicates that a more direct approach, focusing on answering the query, tends to enhance performance. Second, we noted the importance of appropriately balancing the scope of ‘relevance.’ While the term ‘relevant’ can extend the scope too broadly, resulting in less precise evaluations, an optimal balance in defining relevance is crucial for accurate assessments. The inclusion of few-shot examples helps in more precisely defining this balance. By providing clearer contexts for the term ‘relevance,’ few-shot examples contribute to refine relevance criteria. In conclusion, our study highlights the significance of carefully selecting terms in prompts for relevance evaluation with LLMs.

In recent years, great advances have been made in the speed, accuracy, and coverage of automatic ... more 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.

With the growing number of textual resources available, the ability to understand them becomes cr... more 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.

IJNLC 2024, 2024

International Conference on NLP, AI, Computer Science & Engineering (NLAICSE 2024) will provide a... more International Conference on NLP, AI, Computer Science & Engineering (NLAICSE 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of NLP, Artificial Intelligence, Computer Science & Engineering Techniques. The Conference looks for significant contributions to all major fields of NLP, Artificial Intelligence and Computer Science & Engineering in theoretical and practical aspects.The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

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:

5th International Conference on NLP & Artificial Intelligence Techniques (NLAI 2024) will provide... more 5th International Conference on NLP & Artificial Intelligence Techniques (NLAI 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of NLP & Artificial Intelligence Techniques. The Conference looks for significant contributions to all major fields of NLP and Artificial Intelligence in theoretical and practical aspects. The aim of the Conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

13th International Conference on Information Technology Convergence and Services (ITCSE 2024) wil... more 13th International Conference on Information Technology Convergence and Services (ITCSE 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Information Technology Convergence and Services. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

13th International Conference on Cryptography and Information Security (CRYPIS 2024) focuses on c... more 13th International Conference on Cryptography and Information Security (CRYPIS 2024) focuses on cutting-edge results in Applied Cryptography and Information security. It aims to bring together scientists, researchers and students to exchange novel ideas and results in all aspects of Cryptography, Coding and Information security.

13th International Conference on Digital Image Processing and Vision (ICDIPV 2024) is a forum for... more 13th International Conference on Digital Image Processing and Vision (ICDIPV 2024) is a forum for presenting new advances and research results in the fields of Digital Image Processing and vision. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world.

5th International Conference on Machine Learning Techniques and NLP (MLNLP 2024) will provide an ... more 5th International Conference on Machine Learning Techniques and NLP (MLNLP 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning Techniques and NLP.

3rd International Conference on NLP and Machine Learning Trends (NLMLT 2024) will provide an exce... more 3rd International Conference on NLP and Machine Learning Trends (NLMLT 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of NLP and Machine Learning. .

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 areas of NLP and Machine Learning Trends.

5th International Conference on Natural Language Processing and Computational Linguistics (NLPCL ... more 5th International Conference on Natural Language Processing and Computational Linguistics (NLPCL 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing and Computational Linguistics. The Conference looks for significant contributions to all major fields of the Natural Language processing and Computational Linguistics in theoretical and practical aspects.

5th International Conference on Natural Language Computing Advances (NLCA 2024) will provide an e... more 5th International Conference on Natural Language Computing Advances (NLCA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Natural Language Computing and its advances.