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Transformers, Tables and Frame Semantics 17

Transformers, Tables and Frame Semantics

引用

17th ieee international conference on Semantic Computing (ICSC)

作者： Ramirez, Mario Bogatu, Alex Paton, Norman W. Freitas, Andre Univ Manchester Dept Comp Sci Manchester Lancs England Idiap Res Inst Martigny Switzerland

ISBN: (纸本)9781665482639

Transformer-based language models are able to capture linguistic patterns at scale by encoding both syntactic and semantic dimensions of natural language representations with the aim of achieving language understanding. While Transformers have been adapted for generating table embeddings, less research effort has been dedicated to investigating the extent to which these models can encode table semantics. To address this limitation, we propose a method to transfer knowledge from pre-trained natural language models to encode schema-level relationships and analyze the resulting model with respect to two schema-related tasks in different data scenarios.

关键词： data lakes deep learning natural language processing semantics structured data table understanding transformers

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A Survey of Pre-Trained language Models Incorporatingknowledge Graphs

A Survey of Pre-Trained Language Models IncorporatingKnowled...

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2023 ieee international conference on Electrical, Automation and Computer engineering, ICEACE 2023

作者： Wang, Xiaoyi Liu, Jie Zhou, Jianshe Wang, Jincheng Capital Normal University China Language Intelligence Research Center Beijing China North China University of Technology Beijing China Beijing Institute of Control and Electronics Technology Beijing China

ISBN: (纸本)9798350309614

Pre-trained models acquire knowledge from vast amounts of unannotated and unstructured data through self-supervised learning. However, they suffer from limitations such as inadequate performance and limited knowledge reasoning capabilities due to the lack of external knowledge guidance. To address these limitations, integrating structured knowledge from knowledge graphs into pretrained models enables them to acquire both general semantic knowledge from free text and real-world knowledge behind the text, thereby effectively addressing downstream knowledge-driven tasks. This paper introduces the concepts of pretrained models and knowledge graphs, discusses research advancements, provides an overview of methods for integrating knowledge into pretrained models, and proposes three classification approaches based on fusion methods. It also outlines the application domains where these approaches can be applied. Finally, the paper summarizes and discusses future research directions for pretrained models Integrated with knowledge. © 2023 ieee.

关键词： knowledge graph natural language processing Pre-trained language model

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Context-Based Question Answering Using Large language BERT Variant Models for Low Resourced Sesotho sa Leboa language 4

Context-Based Question Answering Using Large Language BERT V...

引用

4th international Multidisciplinary Information Technology and engineering conference, IMITEC 2024

作者： Masethe, Mosima Anna Masethe, Hlaudi Daniel Ojo, Sunday O. Sefako Makgatho Health Sciences Universiry Department of Csit South Africa Durban University of Technology Department of Csit South Africa Tshwane University of Technology Department of Computer Science South Africa Durban University of Technology Department of Information Technology South Africa

ISBN: (纸本)9798350387988

Since reading and responding to text needs both a grasp of natural language and awareness of the outside world, it is challenging for machines to do [1]. The most difficult areas of information retrieval and natural language processing are question answering systems (QAS). The goal of the Question Answering System is to use the provided context or knowledge base to provide replies in natural language to the user's questions. Both closed and open domains can produce the answers. A closed domain's responses are limited to a specific situation, whereas open-domain systems can provide answers in a human-readable language from a vast knowledge base. Another issue is coming up with answers to the questions based on certain situations, as each question might have a variety of interpretations and responses based on the context to which it relates [2]. In our comprehension, this research work is the initial effort to extract answers from a context in low resourced Sesotho sa Leboa language. The Bidirectional Encoder Representation from Transformers (BERT) variant model such as Albert, and DistilBERT is used as the language model in this research study. © 2024 ieee.

关键词： Question answering

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Exploring the Impact of Error Type Features Integration on Transformer-based Myanmar Spelling Correction 21

Exploring the Impact of Error Type Features Integration on T...

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21st international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Phyu, Ei Thandar Thu, Ye Kyaw Chanlekha, Hutchatai Funakoshi, Kotaro Supnithi, Thepchai Kasetsart Univ Fac Engn Bangkok Thailand Natl Elect & Comp Technol Ctr Pathum Thani Thailand Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9798350381771;9798350381764

Spelling correction is a fundamental aspect of natural language processing systems, essential for ensuring the accuracy and fluency of text-based applications. In the context of neural machine translation based spelling correction, spelling errors pose significant challenges, particularly in low-resource language like Myanmar language where training data is limited. In this paper, the Myanmar spelling error-correction parallel corpus was developed through the collection of text from social media platforms. By learning the error patterns in social media text data, we categorized the Myanmar spelling mistakes into ten categories. We developed Myanmar spelling correction model using Transformer architecture and also investigated the potential improvement of our Transformer-based spelling correction by incorporating error type features as additional linguistic information. Evaluating the Transformer model with manually annotated error type labels in our developed corpus significantly enhances the F1-score from 75.91% to 86.71%, increases the GLEU score from 0.9648 to 0.9767, and decreases the word error rate from 3.3% to 2.0%. Additionally, we conducted a preliminary study on integrating our proposed correction model with an automatic error type classification model, employing a bidirectional Long Short-Term Memory. The findings demonstrate that integrating error type information into the Transformer architecture enhances the effectiveness of Myanmar spelling correction.

关键词： Spelling Correction Transformer Linguistic Features Neural Machine Translation natural language processing Myanmar language

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Construction of a Biomedical knowledge Graph using natural language processing

Construction of a Biomedical Knowledge Graph using Natural L...

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2024 ieee international conference on Emerging Technologies in Computer Science for Interdisciplinary Applications, ICETCS 2024

作者： Balachandra, Anumaneni Venkat Deepamala, N. Shobha, G. R.V. College of Engineering Department of CSE Bengaluru India

ISBN: (纸本)9798350372502

Biomedical Named Entity Linking (NEL) plays a crucial role in extracting valuable information from text for downstream tasks in bioinformatics. However, challenges like entity ambiguity and complex relationships hinder accurate entity linking. We demonstrate the effectiveness of BERN2, a novel neural network architecture designed to address these challenges and improve NEL performance, by linking entities to a knowledge graph stored in a Neo4j graph database. This combination allows for efficient exploration of intricate relationships between entities, facilitating advanced biomedical research. Furthermore, we showcase the utility of leveraging node embedding techniques on the knowledge graph for various machine-learning applications in the biomedical domain. Our findings suggest that BERN2, coupled with knowledge graphs and node embeddings, can significantly enhance biomedical information extraction and empower researchers with powerful tools for collaborative healthcare research and accelerated discoveries. © 2024 ieee.

关键词： knowledge graph

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Towards Suicide Prevention: A natural language processing and Machine Learning Approach Integrated with Chatbot

Towards Suicide Prevention: A Natural Language Processing an...

引用

2024 ieee international conference on Automation and Computation, AUTOCOM 2024

作者： Maini, Manit Srivastava, Pranjal Soni, Himanshu Hemprasanna Pillai, Anju S. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering Coimbatore India

ISBN: (纸本)9798350382723

The paper presents a prediction model for identifying suicide intent in social media messages, especially on Twitter and Reddit. A huge dataset of postings, including symptoms of suicide ideation or behaviour, was analyzed using natural language processing (NLP) techniques, and characteristics were collected to build a machine learning (ML) model. The prediction algorithm was subsequently incorporated into a functioning mental health chatbot, which will give users at risk of suicide relevant resources and help. The initiative intends to improve mental health and suicide prevention efforts while also considering critical ethical factors, such as respecting user privacy and ensuring that the chatbot does not replace professional mental health treatment. © 2024 ieee.

关键词： Learning algorithms

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Designing prompts and creating cleaned scientific text for Retrieval Augmented Generation for more precise responses from generative large language models 3

Designing prompts and creating cleaned scientific text for R...

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3rd conference on Information Technology and Data Science

作者： Lakatos, Robert Urban, Eszter Klara Szabo, Zoltan Janos Pozsga, Janos Csernai, Eszter Hajdu, Andras Univ Debrecen Fac Informat Data Sci & Visualizat Debrecen Hungary GE HealthCare Chicago IL USA

ISBN: (纸本)9798350387896;9798350387889

This paper presents a comprehensive methodology for extracting and processing data from the scientific literature to improve the performance of generative language models in the case of the application of Retrieval Augmented Generation. We show how a knowledge-based system can be created to extract information from scientific literature using generative language models. The methodology involves a two-phase approach, utilizing the GROBID PDF processing system for initial data extraction, followed by refinement through a custom text cleaning module. The processed data is formatted into JSON for integration into a semantic search engine, facilitating efficient searching and retrieval. Additionally, the setup of prompts and management of generative language models are meticulously detailed to optimize response quality. Evaluation of response performance using metrics such as BLEU, ROUGE, METEOR, and cosine similarity demonstrates the efficiency of the proposed methodology. As a result, the efficiency of generative language models using data embedded and cleaned in our knowledge-based system improves by 9% in terms of cosine similarity and by 27%, 6%, and 2% in the case of BLEU, ROUGE, METEOR scores, in contrast to the direct usage of the data extracted by GROBID. Overall, this work showcases the methodology's effectiveness in improving the quality of responses generated by language models and lays the groundwork for further advancements in natural language processing and semantic search systems within the scientific literature domain.

关键词： large language models prompt engineering retrieval augmented generation text-cleaning knowledge-based systems semantic search natural language processing

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LLM4Workflow: An LLM-based Automated Workflow Model Generation Tool 24

LLM4Workflow: An LLM-based Automated Workflow Model Generati...

引用

39th ACM/ieee international conference on Automated Software engineering (ASE)

作者： Xu, Jia Du, Weilin Liu, Xiao Li, Xuejun Anhui Univ Anhui Prov Int Joint Res Ctr Adv Technol Med Imag Sch Comp Sci & Technol Hefei Peoples R China Anhui Univ Sch Internet Hefei Peoples R China Deakin Univ Sch Informat Technol Geelong Vic Australia

ISBN: (数字)9798400712487

ISBN: (纸本)9798400712487

Workflows are pervasive in software systems where business processes and scientific methods are implemented as workflow models to achieve automated process execution. However, despite the benefit of no/low-code workflow automation, creating workflow models requires in-depth domain knowledge and non-trivial workflow modeling skills, which becomes a hurdle for the proliferation of workflow applications. Recently, Large language models (LLMs) have been widely applied in software code generation given their outstanding ability to understand complex instructions and generate accurate, context-aware code. Inspired by the success of LLMs in code generation, this paper aims to investigate how to use LLMs to automate workflow model generation. We present LLM4Workflow, an LLM-based automated workflow model generation tool. Using workflow descriptions as the input, LLM4Workflow can automatically embed relevant API knowledge and leverage LLM's powerful contextual learning abilities to generate correct and executable workflow models. Its effectiveness was validated through functional verification and simulation tests on a real-world workflow system. LLM4Workflow is open sourced at https://***/ISEC-AHU/LLM4Workflow, and the demo video is provided at https://***/XRQ0saKkuxY.

关键词： Automated Workflow Model Generation Large language Models Low Code Development

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natural language processing in Strategic Planning Analysis 2

Natural Language Processing in Strategic Planning Analysis

引用

2nd international conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Shetty, Keerthi Tyagi, Swati Jha, Anupama Rao, D N Murali Krishna Arunkumar, J.R. Lakshmikanth, R. Nmam Institute of Technology Department of Mca Udupi India University of Delaware DE United States Vivekananda Institute of Professional Studies Guru Gobind Singh Ip University Vsit Delhi India Jntuh Electrical and Electronics Engineering India Modern Institute of Technology and Research Centre Department of Computer Science and Engineering Rajasthan Alwar India Adithya Institute of Technology Department of Management Studies Coimbatore India

ISBN: (纸本)9798350364699

natural language processing (NLP) has emerged as a game-changing technique in the field of strategic planning analysis. This development represents a significant step forward. Improved methods for obtaining meaningful insights from massive amounts of textual data are made available by natural language processing (NLP). According to the findings of this research project, the application of natural language processing (NLP) technology to enhance the strategic planning processes that are carried out within enterprises is being investigated. natural language processing (NLP) approaches, such as semantic analysis, sentiment identification, and topic modeling, can be utilized to acquire a more in-depth grasp of market tendencies, consumer feedback, and internal communications. This can be accomplished by utilizing these techniques. The study demonstrates how natural language processing (NLP) can facilitate the discovery of emergent patterns, simplify the understanding of data, and encourage decision-making that is driven by data. There are a great number of case studies that demonstrate the incorporation of natural language processing (NLP) into strategic frameworks. These case studies show improvements in the accuracy of forecasting, risk management, and competitive analysis processes. The findings of this study demonstrate the potential for natural language processing (NLP) to revolutionize strategic planning by providing a more nuanced and comprehensive evaluation of qualitative data. This, in turn, will ultimately result in strategic decisions that are more informed and agile. © 2024 ieee.

关键词： Risk management

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Program Synthesis and natural language processing: A Systematic Literature Review 11

Program Synthesis and Natural Language Processing: A Systema...

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11th international conference in Software engineering Research and Innovation (CONISOFT)

作者： Ramirez-Rueda, Rolando Benitez-Guerrero, Edgard Mezura-Godoy, Carmen Barcenas, Everardo Univ Veracruzana Fac Estadist & Informat Xalapa Mexico Univ Nacl Autonoma Mexico Fac Ingn Mexico City Mexico

ISBN: (纸本)9798350328837;9798350328844

Program Synthesis is the process of automatically generating software from a requirement specification. This paper presents a systematic literature review focused on program synthesis from specifications expressed in natural language. The study aims to examine recent works published within the last five years, identifying emerging trends and potential areas for further exploration. Four major data sources (ieee, ACM, Springer, and Elsevier) were selected to gather a total of eight papers from various journals and conference proceedings. These papers propose solutions that target end-users or inexperienced programmers by leveraging natural language queries as inputs and generating expressions in boolean logic, DSL, or Java. The methods employed for program synthesis range from rule-based approaches that encode explicit translation rules from natural language to code, to more advanced techniques that learn these rules from input-output pairs.

关键词： Program Synthesis natural language processing

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