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Entity linking method based on Longformer and adversarial training

Entity linking method based on Longformer and adversarial tr...

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2024 international conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2024

作者： Liu, Jiaqi Zhao, Zhanfang Shuo, Liangxun College of Information and Engineering Hebei GEO University No.136 East Huai’an Road Shijiazhuang050031 China

ISBN: (纸本)9781510685840

Entity linking, the task of associating named entities mentioned in the text with their corresponding entries in a knowledge graph, addresses the inherent ambiguity in entity mentions. This process enhances semantic comprehension in both knowledge graph integration and natural language processing tasks. To address inefficiencies in processing lengthy texts and understanding complex contexts in existing models, this study proposes an innovative model, AT-LOM. The model integrates Longformer pre-training techniques and adversarial training mechanisms into text encoding, employs Extrapolatable Position Embedding (xPos) in the entity detection module and utilizes beam search algorithms in the linking module to rank text sequences generated by Long Short-Term Memory (LSTM). The AT-LOM model demonstrates significant performance enhancements on the standard AIDA-CoNLL dataset, achieving a 1.0% to 13.1% increase in Micro-F1 values compared to mainstream methods in recent years. © 2024 SPIE.

关键词： knowledge graph

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Enhancing Telugu Abusive language Detection Using Word Embeddings and BERT Models 2

Enhancing Telugu Abusive Language Detection Using Word Embed...

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2nd international conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Battula, Venkataramana Nune, Nikhil Teja Guptha, Vivek Rampuri, Shiva Sai Chacham, Pradyumna Pyarasani, Koushik Bojji, Anirudh MVSR Engineering College Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350366570

In the digital era, the surge of abusive content online underscores the necessity for robust content moderation. This study targets the challenges posed by abusive content in Telugu, a language with limited digital resources. The goal is an advanced framework to classify Telugu sentences as abusive or not, using FastText and IndicFast for word embedding. Various machine learning techniques like Decision Trees, SVM, KNN, Random Forest, RNN, LSTM, BiLSTM, GRU, and transfer learning with pre-trained BERT models are explored. Each method is evaluated for its ability to capture abusive language nuances. The study employs diverse word vector dimensions for comprehensive coverage, systematically evaluating techniques to identify optimal classification combinations. It also investigates transfer learning via pre-trained BERT models, leveraging knowledge from other datasets to enhance Telugu abusive language understanding. This research enriches natural language processing and content moderation knowledge. It offers insights into effective word embedding techniques and ML algorithms for low-resource languages, aiding in the development of sensitive content moderation tools for Telugu speakers, fostering a safer online space. © 2024 IEEE.

关键词： Random forests

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An Effective Search Algorithm for Analyzing and Extracting Indian Legal Judgments using NER and Document Summarization 7

An Effective Search Algorithm for Analyzing and Extracting I...

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2023 7th international conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Naik, Varsha Kannan, Rajeswari Agarwal, Sanket Sable, Aryan Chaudhari, Himanshu Mit World Peace University Computer Science and Engineering Pune India Pimpri Chinchwad College of Engineering Computer Engineering Pune India

ISBN: (纸本)9798350304268

With the ever-growing population, the number of legal offenses is also increasing yearly. Going through all the legal documentation can be a very exhausting and time demanding process. Today, NLP (natural language processing) is quite capable of processing documents and extracting key information. Owing to daily advancements in technology it's the perfect time to couple data analytic tools with historical data and create a search algorithm. The searching algorithm can discover an analogous historical case to the one we're working on and give the user access to all the similar legal cases. In this project, the primary attempt is to leverage knowledge graph and NLP (natural language processing) to construct a legal search algorithm that assists in searching it across a big data corpus created using multiple *** study aims to design a semantic search model for the legal domain to facilitate searching of similar judgments by leveraging the power of knowledge graph. Designing a legal system is a very nation-specific task to be carried out because legal terminology shows great variations when it comes to judgment and other legal documents. This study is supposed to produce a smart enough search mechanism that can find similar cases and assist in the faster proceeding of the trial. © 2023 IEEE.

关键词： Abstractive summarization knowledge graph Name Entity Relation (NER) NLP(natural language processing) semantic relation

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A Critical Evaluation of the Application of natural language processing (NLP) For Enhancing Text and Speech Mining 4

A Critical Evaluation of the Application of Natural Language...

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4th international conference on Computation, Automation and knowledge Management, ICCAKM 2023

作者： Saxena, Akash Bains, Sahil Sanyal, Shouvik Ravichand, M. Das, Purushottam Jaiswal, Sushma Compucom Institute of Information Technology and Management Jaipur India Guru Nanak Dev University Punjab Amritsar143005 India Dhofar University Department of Marketing and Entrepreneurship Oman Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College India Graphic Era Hill University Department of Cse Dehradun India Bilaspur India

ISBN: (纸本)9798350393248

This research paper has focused on the concept of Text mining and 'natural language processing or NLP' using Deep Learning. Text mining, also known as 'Text Analytics', employs Artificial Intelligence techniques and Machine Learning algorithms to convert uncontrolled corpus into standardized and normalized databases for further research. Text mining and NLP are two scientific topics that are mutually exclusive. These fields both deal with extracting text from unstructured materials, such as data that cannot be categorized or sorted numerically. NLP is more intriguing than text mining, according to Google Trends. Despite this, internet access has resulted in a dramatic increase in the number of documents that contain natural languages. To deal with the influx of documents, text mining and natural language processing (NLP) are required. Early computerized methods to language research centred on automation linguistic analysis method and building foundational technologies like language processing, language processing, and language processing. This research paper aims to evaluate the NLP in enhancing speech and text mining. In order to get information regarding this topic, researchers have used secondary data collection method to gather effective information from different journals and articles. © 2023 IEEE.

关键词： Learning algorithms

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A Comprehensive Survey on Methods Used for Automatic Assessment of Subjective Answers

A Comprehensive Survey on Methods Used for Automatic Assessm...

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2024 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Joshi, Sudarshan Bachkar, Akshay Awaje, Omkar Bhoir, Rhutuj Savitribai Phule Pune University Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350348460

The paper explores various techniques for identifying multilingual text similarities and basic image similarities, covering a wide array of methods and opportunities for comparing and evaluating textual and diagrammatic content. By delving into these categories and studying the pros and cons of various methods, the survey equips researchers, practitioners, and enthusiasts in the field of NLP with a deeper understanding of text similarity *** paper delves into tokenization, vectorization, and similarity metrics, among other approaches. It also discusses modelling techniques, such as LSA and LDA, text alignment algorithms, cosine similarity, BERT, and optimization techniques. The survey categorizes these techniques into corpus-based, knowledge-based, semantic, term-based, and character-based similarities. The survey aims to provide a comprehensive overview of the methods available for checking multilingual text similarity identification and its significance in contemporary text analysis. Index Terms - Similarity Techniques, Machine learning, natural language processing. © 2024 IEEE.

关键词： Image Similarity Multilingualism natural language processing Semantic Analysis Similarity Techniques

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Axolotl: Fairness through Assisted Prompt Rewriting of Large language Model Outputs 15

Axolotl: Fairness through Assisted Prompt Rewriting of Large...

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15th IEEE international conference on knowledge Graph, ICKG 2024

作者： Ebrahimi, Sana Chen, Kaiwen Asudeh, Abolfazl Das, Gautam Koudas, Nick University of Illinois Chicago Department of Computer Science United States University of Toronto Department of Computer Science Canada University of Texas at Arlington Department of Computer Science United States

ISBN: (纸本)9798331508821

Pre-trained Large language Models (LLMs) have significantly advanced natural language processing capabilities but are susceptible to biases present in their training data, leading to unfair outcomes in various applications. While numerous strategies have been proposed to mitigate bias, they often require extensive computational resources and may compromise model *** this work, inspired by the concept of query rewriting, we propose prompt rewriting to minimize the unfairness of LLM outputs. We introduce Axolotl, a novel post-processing framework that operates agnostically across tasks and models, leveraging public APIs to interact with LLMs without direct access to internal parameters. Through a three-step process, Axolotl identifies biases, proposes resolutions, and guides the model to self-debias its outputs. This approach minimizes computational costs and preserves model performance, making Axolotl a promising tool for debiasing LLM outputs with broad applicability and ease of use. © 2024 IEEE.

关键词： Structured Query language

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Comprehensive Survey of Event Extraction Methods in natural language processing 5

Comprehensive Survey of Event Extraction Methods in Natural ...

引用

5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kulkarni, Amit Dogra, Varun School of Computer Applications Lovely Professional University Punjab Phagwara144411 India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara144411 India

ISBN: (纸本)9798331530013

Event Extraction (Event Extraction) plays an essential role in NLP (natural language processing) by automating the identification and extraction of events from unstructured textual data. This paper provides a comprehensive overview of event extraction, highlighting its significance in various domains such as security, law, and financial analysis. The paper categorizes event extraction into closed-domain and open-domain approaches. It also talks about various techniques like argument identification, event trigger identification, as well as event type classification. It explores methodologies including machine learning, pattern matching, & deep learning, emphasizing their application in different domains. Evaluation metrics to evaluate the effectiveness of EE systems are explored, including precision, recall, as well as the F1 score. Through a systematic review of the literature, the paper underscores the advancements, challenges, and future directions in EE, positioning it as a critical component in extracting actionable knowledge from textualsources. © 2024 IEEE.

关键词： Deep learning

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Create, Explore and Analyse traceability knowledge graphs 31

Create, Explore and Analyse traceability knowledge graphs

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31st IEEE international Requirements engineering conference (RE)

作者： Radhakrishnan, Sruthi Cecchettini, Ario Giancarlo Wendler, Svenja Graf, Andreas itemis AG CC Adv Engn Lunen Germany

ISBN: (纸本)9798350326918

"Glad you introduced a traceability process in your company. Now you are able to relate the various types of artifacts in order to build up a large traceability graph" - what are the next steps? How do you deal with the enormous amount of information? We provide use cases to create, analyse and explore traceability graphs, based on our experiences from itemis ANALYZE, our professional traceability management system, that connect your whole development toolchain, including modelling tools and code. Furthermore, we fine-grain the 'requirements' in traceability by emerging Machine learning (ML) techniques that aid in analysing their similarities thereby proposing a tailored solution which forms the basis for requirements -specific knowledge graph. Finally, we use graph-based algorithms to analyse different use-case of created knowledge graphs.

关键词： Traceability Graphs natural language processing Machine learning Graph analysis

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LSTM Sequence to Sequence Model for Dynamic Title Generation 7

LSTM Sequence to Sequence Model for Dynamic Title Generation

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7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Shilaskar, Swati Saitwal, Prerna Raundal, Pranav Rathi, Manas Vishwakarma Institute of Technology Dept. of Electronics & Telecommunication Engineering Pune India

ISBN: (纸本)9798350359299

To make research communication more efficient, this research work introduces a smart way to create titles for research papers automatically from their corresponding titles. Leveraging a diverse dataset of research papers, the study employs a comprehensive pipeline involving data preprocessing, neural network modeling, and evaluation. Our methodology employs advanced natural language processing techniques, including LSTM-based sequence-to-sequence models and word embeddings, to capture the relationship between abstracts and titles. The trained model is rigorously optimized, and its performance is evaluated using established metrics. An accuracy of 78.82% and perplexity of 1.005 is achieved. This means that the model is able to generate titles that were very similar to the human-generated titles. The results demonstrate the feasibility of automated title generation, providing titles that closely resemble human-generated counterparts. This research offers valuable insights into enhancing scholarly publishing and knowledge dissemination across academic domains. © 2024 IEEE.

关键词： natural language processing systems

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Multi-table Question Answering Method Based on Correlation Evaluation and Precomputed Cube 17th

Multi-table Question Answering Method Based on Correlation E...

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17th international conference on knowledge Science, engineering and Management (KSEM)

作者： Wang, Bing Wang, Chunhao Zhang, Xingpeng Zhao, Chunlan Yang, Xiaoling Guo, Kuan Southwest Petr Univ Sch Comp Sci & Software Engn Chengdu 610500 Peoples R China Southwest Petr Univ Sch Sci Chengdu 610500 Peoples R China

ISBN: (纸本)9789819754915;9789819754922

The goal of multi-table question answering (Multi-TableQA) is to enable intelligent extraction and understanding of structured table data via natural language queries. The most commonly used autoregressive pretraining language models (PLMs) encounter problems such as noise sequence information, sequence length and numerical computation limitation. To address these issues, we propose two preprocessing steps: a correlation evaluator and a precomputed cube (TaCube). The correlation evaluator assesses the relevance between questions and table columns, discarding irrelevant columns to minimize information noise and reduce sequence length. TaCube precomputes aggregation and arithmetic operations based on the query, thereby equipping the model with rapid access to numerical computations and enhancing the PLM's numerical reasoning capabilities. Experimental results demonstrate that these preprocessing steps increase the exact match accuracy on the Spider and GeoQuery multi-table question answering datasets to 33.99% and 61.43%, respectively, outperforming existing methods.

关键词： Multi-Table Question Answering natural language processing Encoder-Decoder Table-Question Alignment

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