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language processing with Recursive Neural Networks

Language Processing with Recursive Neural Networks

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2023 IEEE international conference on Research Methodologies in knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Sangeetha, R. Srivastava, Durgesh Vats, Satvik Logeshwaran, J. Vishwakarma, Pramod Department of CSE Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India

ISBN: (纸本)9798350305708

Recursive Neural Networks (RNNs) are a powerful tool for natural language processing (NLP) that can easily learn and represent recursive structures in sentences. Artificial Neural Networks (ANNs) have been widely used in various NLP tasks, such as language modeling, text classification, and sentiment analysis. With the development of deep learning, RNNs have emerged as a powerful tool to represent structural and syntactic information of sentences. RNNs have attracted a lot of attention due to their ability to learn complex language structures and representation. Consequently, RNNs have achieved great performance in a variety of NLP applications, such as text summarization, machine translation, and question answering. In this paper, we discuss the importance of RNNs in NLP and present different smart performance optimization strategies for RNNs. Firstly, we consider the use of pre-trained language models such as GloVe and word2vec to enhance the representation of words and improve the overall accuracy. Secondly, we implement a variety of regularization techniques, such as dropout and batch normalization, to regularize the learning process of RNNs. Finally, we discuss the approaches used to reduce the complexity and overfitting of RNNs. We compare the performance of different RNNs on the Penn Treebank dataset with different optimization strategies. Experimental results show that our proposed approaches can significantly improve the performance of RNNs and provide a more effective solution for NLP tasks. © 2023 IEEE.

关键词： accuracy complexity Consequently overfitting significantly

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Extending Token Computation for LLM Reasoning 24

Extending Token Computation for LLM Reasoning

引用

8th international conference on Computer Science and Artificial Intelligence, CSAI 2024

作者： Li, Bingli Vargas, Danilo Vasconcellos Laboratory of Intelligent Systems The Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Fukuoka Japan

ISBN: (纸本)9798400718182

Large language Models (LLMs) are pivotal in advancing natural language processing but often struggle with complex reasoning tasks due to inefficient attention distributions. In this paper, we explore the effect of increased computed tokens on LLM performance and introduce a novel method for extending computed tokens in the Chain-of-Thought (CoT) process, utilizing attention mechanism optimization. By fine-tuning an LLM on a domain-specific, highly structured dataset, we analyze attention patterns across layers, identifying inefficiencies caused by non-semantic tokens with outlier high attention scores. To address this, we propose an algorithm that emulates early layer attention patterns across downstream layers to re-balance skewed attention distributions and enhance knowledge abstraction. Our findings demonstrate that our approach not only facilitates a deeper understanding of the internal dynamics of LLMs but also significantly improves their reasoning capabilities, particularly in non-STEM domains. Our study lays the groundwork for further innovations in LLM design, aiming to create more powerful, versatile, and responsible models capable of tackling a broad range of real-world applications. © © 2024 Copyright held by the owner/author(s).

关键词： Semantics

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Exploratory prompting of large language models to act as co-pilots for augmenting business process work in document classification

Exploratory prompting of large language models to act as co-...

引用

2023 international conference on Industry Sciences and Computer Science Innovation, iSCSi 2023

作者： Ilagan, Jose Ramon Ilagan, Joseph Benjamin Basallo, Claire Louisse Alabastro, Zachary Matthew Department of Quantitative Methods and Information Technology Ateneo de Manila University Philippines

Businesses deal with different types of documents containing unstructured documents. The data in these documents must be converted into digital forms other automated systems could only process. One generic use case is document classification, which usually involves manual transformation due to human understanding needed in the process. These documents go beyond those generated through regular business transactions and operations and also include web-based content such as online news, blogs, e-mails, and various digital libraries. Recent developments in robotic process automation (RPA) and artificial intelligence (AI) aim to automate the otherwise expensive, time-consuming, and repetitive manual steps. Through more powerful natural language processing (NLP) and natural language understanding (NLU) capabilities, large language models (LLMs) may come as a big boost in applying AI to RPA initiatives. This study proposes a general approach to using LLMs as document classifier co-pilots for knowledge workers in charge of classifying documents to be useful. The manner of prompt engineering and refinement involving labeled health insurance documents to achieve better results is discussed and evaluated through early, iterative classification attempts. However, early tests with a complex sample use case show unsatisfactory results. The study ends with recommendations for future work to improve precision and recall performance. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： natural language processing systems

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knowledge graphs to an analysis and visualization of texts from scientific articles 27

Knowledge graphs to an analysis and visualization of texts f...

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27th international conference on knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Probierz, Barbara Kozak, Jan Department of Machine Learning University of Economics in Katowice 1 Maja 50 Katowice40-287 Poland Lukasiewicz Research Network Institute of Innovative Technologies EMAG Leopolda 31 Katowice40-189 Poland

Reviewing the literature is one of the key elements of scientific research that allows you to identify existing solutions and research niches. However, it can be difficult for researchers to find relevant scientific articles related to the research topic. A limited number of available sources of information, diverse ways of describing them, and a multitude of scientific publications mean that scientists often have to spend a lot of time and effort to find the information they need. For this reason, we propose a solution for text analysis and its presentation in the form of a graph visualization. In our research, we used natural language processing (NLP) methods and word weighting measures such as TF and TF-IDF. In addition, knowledge graphs were used to present the results visually. The conducted research was based on the analysis of the content of scientific articles, which allowed to draw important conclusions related to the presentation of texts in graphic form. Experimental results also identified potential methods and suggestions for literature reviews in specific fields of science. The analysis of the methods used and the results obtained allowed for a better understanding of the potential of natural language processing and graph text representation in the analysis of scientific articles. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： analysis of texts knowledge graphs natural language processing scientific articles

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NLP Based Question Answering System 7

NLP Based Question Answering System

引用

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

作者： Patil, Rahul Patil, Promod D. Joshi, Yadneya Khandelwal, Shivam Nalawade, Sarthak Palve, Bhagwat Pimpri Chinchwad College of Engineering Pune India D y Patil Institute of Technology Pune India

ISBN: (纸本)9798350304268

The proliferation of data in various domains has increased the significance of Question Answering Systems (QAS), which utilize natural language processing (NLP) techniques to comprehend and respond to natural language queries. This review paper provides a comprehensive overview of recent advancements in QAS that employ NLP techniques. However, this thesis concentrates on specific QA scenarios where the answer is explicitly present in the text source. The objective is to build a QA system that can extract answers from plain text sources quickly, precisely, and accurately, using only a single sentence or a fragment of a sentence. The paper discusses the obstacles involved in creating QAS and how NLP techniques can overcome them. The paper proposes a question answering system that combines extractive and abstractive summarization methods to produce brief and accurate answers to user questions. The system uses keyword extraction and classification to identify pertinent information from a collection of documents and then combines extractive and abstractive summarization approaches to create a comprehensive and concise response. The research findings indicate that the proposed system has significant potential to enhance the effectiveness and efficiency of question answering systems in a range of applications. © 2023 IEEE.

关键词： API knowledge graphs natural language processing Summarization

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Proceedings - 2023 international conference on Advanced Enterprise Information System, AEIS 2023

Proceedings - 2023 International Conference on Advanced Ente...

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3rd international conference on Advanced Enterprise Information System, AEIS 2023

ISBN: (纸本)9798350359268

The proceedings contain 22 papers. The topics discuss include: the adoption and usage of SAFe in financial institutions;automated black-box testing of RESTful APIs using enhanced artificial bee colony;feature engineering procedure for information enrichment;multi-feature classification enhancement;approach to reuse of unity game scenes;see-shop system: an assistive outdoor navigation system for the visual impaired based on deep learning methods;OMIT: an instrument to aid implementation of a data ownership model in data governance;architecture to productize semantic transformation by utilizing natural language processing with microservices;distributed data addressed in natural language;and knowledge-driven analysis framework of anomaly propagation in manufacturing workshop.

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Topic and Speaker-aware Hierarchical Encoder-Decoder Model for Dialogue Generation 35

Topic and Speaker-aware Hierarchical Encoder-Decoder Model f...

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35th international conference on Software engineering and knowledge engineering, SEKE 2023

作者： Hu, Qitao Wei, Xiao School of Computer Engineering and Science Shanghai University China

As one of the most common social behavior in human society, communication in multi-turn conversation or dialogue system has always been a research focuses of natural language processing (NLP). The quality of downstream tasks in multi-turn dialogue is often determined by the result of dialogue context modeling. For dialogue generation, the context information will determine the consistency and diversity of the generated responses. However, the current research on dialogue generation increasingly relies on external information rather than mining from the dialogue content itself. In this paper, we propose a topic and speaker-aware hierarchical encoder-decoder (TSHED) model to capture the topic and speaker information flow in the context for response generation with the hierarchical transformer-based framework. Specifically, we obtain semantic information of each utterance at word-level and then apply topic and speaker-aware attention to model context at utterance-level. Experimental results on two open-domain datasets show that TSHED significantly improves the quality of responses and outperforms strong baselines. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Semantics

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Ontology-driven knowledge base for digital humanities: Restructuring knowledge organization at the library of the Folkwang University of the Arts

Ontology-driven knowledge base for digital humanities: Restr...

引用

2024 IEEE international conference on Big Data, BigData 2024

作者： Linxen, Andrea Schmidt, Vera-Maria Klinke, Harald Beecks, Christian FernUniversität in Hagen Hagen Germany Folkwang Universität der Künste Essen Germany Ludwig-Maximilians-Universität München München Germany

ISBN: (纸本)9798350362480

Academic libraries are increasingly challenged by the need to efficiently manage and analyse vast collections of data and knowledge. The divers formats and organisation methods of these collections, ranging from traditional print media to digital archives and multimedia assets, can hinder researchers' ability to easily access and retrieve relevant information. This paper introduces an ontology-driven knowledge base to address this issue by enabling the efficient access to knowledge in the application domain and enhancing the semantic search capabilities in the field of Digital Humanities. Our approach focuses on the development of an ontology-drive knowledge base for semantic search in academic libraries by the example of the library of the Folkwang University of Arts that captures the knowledge concepts present in the library's archival collections. The resulting ontology framework provides a structured representation of domain knowledge, facilitating the integration of diverse data sources, including structured, semi-structured, and unstructured data from the application domain into a triple store knowledge base. By leveraging SPARQL queries generated from Large language Model (LLM) prompts, we aim to facilitate more intuitive and effective knowledge retrieval. This approach allows users to express their information needs in a more natural and flexible way, leading to more accurate and relevant search results. We evaluate the proposed ontology-driven knowledge base in terms of its integrity, consistency, flexibility, relevance, and scalability. Our evaluation methodology includes a combination of verification and validation techniques, including automated reasoners and query results based on competence questions. Our findings demonstrate the potential of ontology engineering to enhance complex information retrieval in academic libraries. However, we also identify limitations related to processing speed for complex queries and the quality of search results. This re

关键词： Structured Query language

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Multi-Label Classification of CS Papers Using natural language processing Models 22

Multi-Label Classification of CS Papers Using Natural Langua...

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22nd IEEE international conference on Machine Learning and Applications, ICMLA 2023

作者： Zhang, Zheng Das, Amit Rahgouy, Mostafa Bao, Yida Baskiyar, Sanjeev Auburn University Dept. of Computer Science & Software Engineering Dept. of Mathematics & Statistics AuburnAL United States

ISBN: (纸本)9798350345346

Computer science (CS) is a rapidly evolving field of knowledge with a significant volume and significance of scientific papers. Consequently, meticulous management and categorization of CS papers are crucial to ensure efficient access and retrieval of relevant information. This study aims to evaluate the effectiveness of eight traditional machine learning models, as well as Recurrent Neural Network (RNN) models, such as LSTM and BiLSTM, and state-of-the-art pre-trained self-attention-based natural language processing (NLP) models, including BERT, XLNet, RoBERTa, and DistilBERT, for the task of multi-label classification of arXiv CS paper classification. The results demonstrate that pre-trained self-attention-based models consistently outperform the other models regarding classification performance. Moreover, self-attention-based models exhibited superior performance and achieved a new state-of-the-art result. To the best of our knowledge, our paper represents the first endeavor to evaluate machine learning models, specifically on computer science-related documents, as part of a multi-label classification task. © 2023 IEEE.

关键词： BERT Deep Learning LSTM Machine learning multi-label classification

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SEMANTIC SEARCH WITH SENTENCE-BERT FOR DESIGN INFORMATION RETRIEVAL

SEMANTIC SEARCH WITH SENTENCE-BERT FOR DESIGN INFORMATION RE...

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ASME international Design engineering Technical conferences / 42nd Annual Computers and Information in engineering conference (IDETC-CIE)

作者： Walsh, Hannah S. Andrade, Sequoia R. NASA Ames Res Ctr Intelligent Syst Div Moffett Field CA 94035 USA HX5 LLC Moffett Field CA USA

ISBN: (纸本)9780791886212

Managing and referencing design knowledge is a critical activity in the design process. However, reliably retrieving useful knowledge can be a frustrating experience for users of knowledge management systems due to inherent limitations of standard keyword-based searches. In this research, we consider the task of retrieving relevant lessons learned from the NASA Lessons Learned Information System (LLIS). To this end, we apply a state-of-the-art natural language processing (NLP) technique for information retrieval (IR): semantic search with sentence-BERT, which is a modification of a Bidirectional Encoder Representations from Transformers (BERT) model that uses siamese and triplet network architectures to obtain semantically meaningful sentence embeddings. While the pre-trained sBERT model performs well out-of-the-box, we further fine-tune the model on data from the LLIS so that it learns on design engineering-relevant vocabulary. We quantify the improvement in query results using both standard sBERT and fine-tuned sBERT over a keyword search. Our use case throughout the paper is to use queries related to specific requirements from a NASA project. Fine tuning the sBERT model on LLIS data yields a mean average precision (MAP) of 0.807 on queries based on information needs from a real NASA project. Results indicate that applying state-of-the-art natural language processing techniques, especially when fine-tuned using engineering data, to design information retrieval tasks shows significant promise in modernizing design knowledge management systems.

关键词： Design Information retrieval Semantics NASA natural language processing knowledge management Architecture

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