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An Approach to Bodo Word Sense Disambiguation (WSD) Using Word2Vec

An Approach to Bodo Word Sense Disambiguation (WSD) Using Wo...

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international conference on Modeling, Simulation and Optimization, CoMSO 2022

作者： Basumatary, Subungshri Brahma, Karmabir Barman, Anup Kumar Nag, Amitava Department of Computer Science and Engineering Central Institute of Technology Kokrajhar BTR Assam Kokrajhar India

ISBN: (纸本)9789819968657

natural language processing (NLP) is one of the most popular proliferating research fields nowadays in artificial intelligence and machine learning. NLP deals with numerous applications such as Sentiment Analysis, Speech Recognition, Summarization of Text, Social Media Analytics. However, one of the significant challenges in developing NLP tools is word ambiguity, i.e., a word can have more than one meaning. The process of determining an ambiguous word's precise meaning in a given context is known as word sense disambiguation (WSD). In this research work, we propose a WSD framework for the Bodo language using Word2Vec. Cosine similarity is used to assess how similar the sentence is to the corpora to interpret the ambiguous word. To the best of our knowledge, this is the first work on Bodo WSD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sentiment analysis

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Auto-labelling of Bug Report using natural language processing 8

Auto-labelling of Bug Report using Natural Language Processi...

引用

8th IEEE international conference for Convergence in Technology, I2CT 2023

作者： Patil, Avinash Jadon, Aryan Juniper Networks Inc. Sunnyvale United States San José State University San Jose United States

ISBN: (纸本)9798350334012

The exercise of detecting similar bug reports in bug tracking systems is known as duplicate bug report detection. Having prior knowledge of a bug report's existence reduces efforts put into debugging problems and identifying the root cause. Rule and Query-based solutions recommend a long list of potential similar bug reports with no clear ranking. In addition, triage engineers are less motivated to spend time going through an extensive list. Consequently, this deters the use of duplicate bug report retrieval solutions. In this paper, we have proposed a solution using a combination of NLP techniques. Our approach considers unstructured and structured attributes of a bug report like summary, description and severity, impacted products, platforms, categories, etc. It uses a custom data transformer, a deep neural network, and a non-generalizing machine learning method to retrieve existing identical bug reports. We have performed numerous experiments with significant data sources containing thousands of bug reports and showcased that the proposed solution achieves a high retrieval accuracy of 70% for recall@5. © 2023 IEEE.

关键词： natural language processing systems

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GECTurk: Grammatical Error Correction and Detection Dataset for Turkish 13

GECTurk: Grammatical Error Correction and Detection Dataset ...

引用

13th international Joint conference on natural language processing and the 3rd conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: Findings of the Association for Computational Linguistic, IJCNLP-AACL 2023

作者： Kara, Atakan Safian, Farrin Marouf Bond, Andrew Şahin, Gözde Gül Computer Engineering Department Koç University Istanbul Turkey

ISBN: (纸本)9798891760189

Grammatical Error Detection and Correction (GEC) tools have proven useful for native speakers and second language learners. Developing such tools requires a large amount of parallel, annotated data, which is unavailable for most languages. Synthetic data generation is a common practice to overcome the scarcity of such data. However, it is not straightforward for morphologically rich languages like Turkish due to complex writing rules that require phonological, morphological, and syntactic information. In this work, we present a flexible and extensible synthetic data generation pipeline for Turkish covering more than 20 expert-curated grammar and spelling rules (a.k.a., writing rules) implemented through complex transformation functions. Using this pipeline, we derive 130,000 high-quality parallel sentences from professionally edited articles. Additionally, we create a more realistic test set by manually annotating a set of movie reviews. We implement three baselines formulating the task as i) neural machine translation, ii) sequence tagging, and iii) prefix tuning with a pretrained decoder-only model, achieving strong results. Furthermore, we perform exhaustive experiments on out-of-domain datasets to gain insights on the transferability and robustness of the proposed approaches. Our results suggest that our corpus, GECTurk, is high-quality and allows knowledge transfer for the out-of-domain setting. To encourage further research on Turkish GEC, we release our datasets, baseline models, and the synthetic data generation pipeline at https://***/GGLAB-KU/gecturk. © 2023 Asian Federation of natural language processing.

关键词： Pipelines

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A Comparison of Multinomial Naive Bayes and XG Boost for Sentiment Analysis and Bias Detection in Tweets 9

A Comparison of Multinomial Naive Bayes and XG Boost for Sen...

引用

9th international conference on Wireless Communications, Signal processing and Networking, WiSPNET 2024

作者： Devadath, R. Alex, Emeric C Sreeja, K. Abhishek, S. Anjali, T. Engineering Amrita School of Computing Department of Computer Science Amritapuri India Rolls-Royce Inc Bangalore India

ISBN: (纸本)9798350350845

In the context of social platforms, this study explores Twitter sentiment analysis, providing practical insights and directions for future research. In previous days, people conveyed their feelings directly, but now platforms like Twitter have gained popularity for expressing individual and organizational opinions. Understanding these expressions can be challenging. This research focuses on sentiment analysis techniques for Twitter data, a subfield of natural language processing (NLP) where machine learning plays a key role in detecting public emotions. The paper covers methods, tools, pre-processing, sentiment classification techniques, evaluation matrixes, and machine learning algorithms like Naive Bayes. The analysis, employing various keywords and hashtags, evaluates using metrics such as confusion matrix, F1 score, precision, and recall. The study emphasizes the responsible and ethical use of sentiment analysis tools for informed decision-making, promoting inclusivity and empathy online. Through systematic analysis, a million tweets were categorized into positive and negative sentiments, contributing to the growing body of knowledge in sentiment analysis. © 2024 IEEE.

关键词： Sentiment analysis

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Exploring the Role of Automated Reasoning in Big Data Analysis 15

Exploring the Role of Automated Reasoning in Big Data Analys...

引用

15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Neetha, S.S. Sahoo, Girija Shankar Karthigaikuar, P. Jayasudha, S. Shinde, Gitanjali R. Lone, Zeeshan Ahmad Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore641032 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350370249

In large-scale data analysis, computer reasoning is an important role. This era can pull significant files from substantial statistics and in a well-timed manner. In this way, through automatic feel, facts scientists and researchers can pick styles out of your statistics or even locate anomalies that could not have been detected in records units. Additionally, it speeds up constructing decision-making models and stimulates artificial intelligence. Artificial intelligence permits herculean records mining and semantic analysis methods, including device learning, ontologiesystem gaining knowledge and natural language processing. Likewise, it offers a platform for creating and monitoring valuable findings, trends or patterns. This also helped to enhance the accuracy of analytic results and provides a more efficient way for statistics scientists & researcher s to analyze large amounts of data. This generation poses a chance to enhance state-of-the-art strategies by making the correct picks. © 2024 IEEE.

关键词： Aberrations Discriminations Electronic Mechanical Mindset

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Machine Learning-Enhanced Requirements engineering: A Systematic Literature Review 19

Machine Learning-Enhanced Requirements Engineering: A System...

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19th international conference on Evaluation of Novel Approaches to Software engineering, ENASE 2024

作者： Núñez, Ana-Gabriela Granda, Maria Fernanda Saquicela, Victor Parra, Otto Department of Computer Science Universidad de Cuenca Cuenca Ecuador

ISBN: (纸本)9789897586965

In the software lifecycle, requirements are often subjective and ambiguous, challenging developers to comprehend and implement them accurately and thoroughly. Nevertheless, using techniques and knowledge can help analysts simplify and improve requirements comprehensibility, ensuring that the final product meets the client’s expectations and needs. The Requirements engineering domain and its relationship to Machine Learning have gained momentum recently. Machine Learning algorithms have shown significant progress and superior performance when dealing with functional and non-functional requirements, natural language processing, text-mining, data-mining, and requirements extraction, validation, prioritisation, and classification. This paper presents a Systematic Literature Review identifying novel contributions and advancements from January 2012 to June 2023 related to strategies, technology and tools that use Machine Learning techniques in Requirements engineering. This process included selecting studies from five databases (Scopus, WoS, IEEE, ACM, and Proquest), from which 74 out of 1219 were selected. Although some successful applications were found, there are still topics to explore, such as analysing requirements using different techniques, combining algorithms to improve strategies, considering other requirements specification formats, extending techniques to larger datasets and other application domains and paying attention to the efficiency of the approaches. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Requirements engineering

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OTE: An Optimized Chinese Short Text Matching Algorithm Based on External knowledge 1

引用

15th international conference on knowledge Science, engineering, and Management (KSEM)

作者： Ma, Haoyang Ding, Zhaoyun Li, Zeyu Guo, Hongyu North China Inst Comp Technol Beijing Peoples R China Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha Peoples R China Commun Univ China Beijing Peoples R China

ISBN: (数字)9783031109836

ISBN: (纸本)9783031109836;9783031109829

Short text matching is a key problem in natural language processing (NLP), which can be applied in journalism, military, and other fields. In this paper, we propose an optimized Chinese short text matching algorithm based on external knowledge (OTE). OTE can effectively eliminate semantic ambiguity in Chinese text by integrating the HowNet external knowledge base. We use SoftLexicon to optimize the word lattice graph to provide more comprehensive multi-granularity information and integrate the LaserTagger model and EDA for data augmentation. Experimental results show that OTE has an average accuracy improvement of 1.5% in three datasets compared with existing models.

关键词： Text matching Multi-granularity information Data augmentation External knowledge Pre-training natural language processing

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Optimizing Question-Answering Framework Through Integration of Text Summarization Model and Third-Generation Generative Pre-Trained Transformer 14

Optimizing Question-Answering Framework Through Integration ...

引用

14th international conference on Computer and knowledge engineering, ICCKE 2024

作者： Moung, Ervin Gubin Tong, Toh Sin Sufian, Maisarah Mohd Liaw, Valentino Farzamnia, Ali Yahya, Farashazillah Faculty of Computing and Informatics University Malaysia Sabah Kota Kinabalu Sabah Malaysia School of Computing and Engineering University of Huddersfield West YorkshireHD1 3DH United Kingdom

ISBN: (纸本)9798331511272

This research project addresses the growing demand for efficient data access amidst the surge in digital information. Conventional keyword-based search engines face limitations, driving the exploration of advanced natural language processing (NLP) approaches. The study introduces an algorithm that autonomously extracts data from summary reports, utilizing NLP and information retrieval as a question-answering API. Evaluated using Recall-Oriented Understudy for Gisting Evaluation (ROUGE)-1, ROUGE-2, and ROUGE-L scores, PEGASUS achieved the highest average ROUGE score (0.432) with a single sample, while BART attained the highest multi-sample score (0.302) with 1000 samples. The research emphasized optimal hyperparameters in pre-trained models, specifically the impact of batch size on completion time and the relationship between maximum sequence length and ROUGE scores. The study enhances question-answering systems for efficient information retrieval, with practical applications in sectors like legal analysis, healthcare, and business intelligence. This study not only improves the efficiency and accuracy of QA systems but also offers valuable insights for future advancements in NLP-driven information extraction. The refined methodologies and enhanced performance metrics provide a promising avenue for transforming how organizations handle large-scale data, driving innovation in both computational efficiency and user experience. ©2024 IEEE.

关键词： Question answering

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A Survey of Pretrained language Models 1

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15th international conference on knowledge Science, engineering, and Management (KSEM)

作者： Sun, Kaili Luo, Xudong Luo, Michael Y. Guangxi Normal Univ Sch Comp Sci & Engn Guangxi Key Lab Multisource Informat Min & Secur Guilin 541001 Peoples R China Univ Cambridge Emmanuel Coll Cambridge CB2 3AP England

ISBN: (数字)9783031109867

ISBN: (纸本)9783031109867;9783031109850

With the emergence of Pretrained language Models (PLMs) and the success of large-scale PLMs such as BERT and GPT, the field of natural language processing (NLP) has achieved tremendous development. Therefore, nowadays, PLMs have become an indispensable technique for solving problems in NLP. In this paper, we survey PLMs to help researchers quickly understand various PLMs and determine the appropriate ones for their specific NLP projects. Specifically, first, we brief on the main machine learning methods used by PLMs. Second, we explore early PLMs and discuss the main state-of-art PLMs. Third, we review several Chinese PLMs. Fourth, we compare the performance of some mainstream PLMs. Fifth, we outline the applications of PLMs. Finally, we give an outlook on the future development of PLMs.

关键词： Machine learning natural language processing Pretrained language Models BERT GTP

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A multilingual clause boundary detection approach for Assamese and Bishnupriya Manipuri 4

A multilingual clause boundary detection approach for Assame...

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4th international conference on Computing and Communication Systems, I3CS 2023

作者： Das, Bhubneswar Sinha, Smriti Kumar Tezpur University Department of Computer Science and Engineering Tezpur India

ISBN: (纸本)9798350323771

Clause boundary detection is an important step in various natural language processing tasks like dependency parsing, machine translation etc. A complex sentence in any language may contain more than one clause. The boundaries should be identified for capturing various dependency relations and for identifying the arguments of the verbal components. We present here, the task of identification and classification of clauses in Assamese and Bishnupriya Manipuri text. To the best of our knowledge, not much work has been done on clause boundary identification for these two languages, which makes this task more important. We have built a rule-based system using linguistic cues such as coordinating conjunct, subordinating conjunct etc. We additionally considered POS tags of neighbouring words as features into the existing conjunction-based approach, which can also be applied to the languages that belongs to the same language family and have certain similarities. Experimental results show that our approach achieves a satisfactory result. © 2023 IEEE.

关键词： Syntactics

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