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Lay Summarization of Biomedical Documents with Discourse Structure-Based Prompt Tuning 28th

Lay Summarization of Biomedical Documents with Discourse S...

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28th international conference on Technologies and Applications of Artificial Intelligence, TAAI 2023

作者： Wu, Yu-Hsuan Chiu, Chi-Min Kao, Hung-Yu Intelligent Knowledge Management Lab Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9789819717101

Transforming complex biomedical texts into accessible lay summaries is a critical endeavor in natural language Generation (NLG). This study addresses the challenges associated with this task by employing a multi-aspect approach. Firstly, we undertake a comprehensive analysis of discourse structures within a diverse range of biomedical datasets and clarify underlying patterns and structures. Secondly, we designed the power of prompting strategies to integrate training on these varied datasets, thereby reducing the noise introduced by their diversity. This twofold strategy fine-tunes the model’s training and enriches it with the ability to generate coherent and simplified lay summaries of biomedical content. Our experimental results clearly demonstrate the effectiveness of our study, underscoring its potential to make complex medical information more accessible to general readers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： natural language processing systems

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Simple Techniques for Enhancing Sentence Embeddings in Generative language Models 20th

Simple Techniques for Enhancing Sentence Embeddings in Gener...

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20th international conference on Intelligent Computing (ICIC)

作者： Zhang, Bowen Chang, Kehua Li, Chunping Tsinghua Univ Sch Software Beijing Peoples R China

ISBN: (纸本)9789819756681;9789819756698

Sentence Embedding stands as a fundamental task within the realm of natural language processing, finding extensive application in search engines, expert systems, and question-and-answer platforms. With the continuous evolution of large language models such as LLaMA and Mistral, research on sentence embedding has recently achieved notable breakthroughs. However, these advancements mainly pertain to fine-tuning scenarios, leaving explorations into computationally efficient direct inference methods for sentence representation in a nascent stage. This paper endeavors to bridge this research gap. Through comprehensive experimentation, we challenge the widely held belief in the necessity of an Explicit One-word Limitation for deriving sentence embeddings from Pre-trained language Models (PLMs). We demonstrate that this approach, while beneficial for generative models under direct inference scenario, is not imperative for discriminative models or the fine-tuning of generative PLMs. This discovery sheds new light on the design of manual templates in future studies. Building upon this insight, we propose two innovative prompt engineering techniques capable of further enhancing the expressive power of PLMs' raw embeddings: Pretended Chain of Thought and knowledge Enhancement. We confirm their effectiveness across various PLM types and provide a detailed exploration of the underlying factors contributing to their success. Our code has been made publicly available at https://***/ZBWpro/PretCoTandKE.

关键词： Sentence Representation Prompt engineering Large language Models

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Scalable Analysis of English Dictionary Files on HPCC Systems Big Data Platform 9

Scalable Analysis of English Dictionary Files on HPCC System...

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9th international conference on Big Data Analytics, ICBDA 2024

作者： Jayanth, C. Adarsh, U. De Hilster, David Watanuki, Hugo Shobha, G. Shetty, Jyoti Department of Information Science and Engineering Bengaluru India Department of Computer Science and Engineering Bengaluru India Lexis Nexis Risk Solutions Boca Raton United States

ISBN: (纸本)9798350352962

This paper presents a scalable approach to address the challenge of enhancing and scaling English dictionaries, employing a scalable integration of NLP++ VisualText analyzers with the HPCC Systems Big Data platform. The methodology involves parsing English Wiktionary files to generate knowledge bases and dictionary files, deploying large XML files on the HPCC platform via the ECL-NLP++ plugin. This study focuses on utilizing ECL and HPCC System architecture to analyze files. The work demonstrates the effectiveness of HPCC Systems in handling large linguistic datasets and compares it with Hadoop MapReduce, highlighting the effectiveness of HPCC in processing real-time data and its user-friendly functionality The results show extraction of English words and their parts of speech from Wiktionary files. This research contributes to improving natural language processing (NLP) applications and lays the groundwork for future enhancements, such as incorporating semantic information and exploring other linguistic variations. © 2024 IEEE.

关键词： Semantics

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Proceedings of the 8th international conference on Big Data, knowledge and Control Systems engineering, BdKCSE 2023

Proceedings of the 8th International Conference on Big Data,...

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8th international conference on Big Data, knowledge and Control Systems engineering, BdKCSE 2023

ISBN: (纸本)9798350313246

The proceedings contain 30 papers. The topics discussed include: from data to learning: the scientific approach to AI-enhanced online course design;implication and inference rules in multivalued logic with network configuration;modern hot water storage technologies for livestock farms;AIOps-driven enhancement of log anomaly detection in unsupervised scenarios;lean six sigma: integrating knowledge, data, and innovation for organizational excellence;key performance indicators in parking guidance information system;application of machine learning and natural language technologies in building semantic search systems: case study of a virtual legal assistant;an efficient hybrid volleyball premier league and antlion metaheuristic algorithm for feature selection;big data processing for Bulgarian healthcare - smart cards and some simulating decisions;and the role of big data in intelligent educational platform: a functional architecture.

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Unveiling Insights: A Machine Learning Approach to Decipher Student Sentiments in Computer University of Myanmar 2

Unveiling Insights: A Machine Learning Approach to Decipher ...

引用

2nd international conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Aung, Zu Zu Shinha, G.R. International Institute of Infomration Technology Indian Knowledge System Lab Bangalore India

ISBN: (纸本)9798350328202

This propose system addresses the crucial aspect of student feedback analysis in computer science courses at the Computer University of Myanmar. Acknowledging the significance of genuine feedback for educational improvement, the study integrates machine learning techniques and natural language processing for a comprehensive analysis. Three Google forms, tailored for curriculum, assessment, and learning outcomes, are utilized to collect both quantitative and qualitative data from students. The analysis pipeline involves preprocessing, text normalization, Bag of Words model construction, sentiment labeling, and TF-IDF feature extraction. Classification employs Support Vector Machine (SVM) and Naive Bayes classifiers, with varied strengths demonstrated across questionnaire forms. The results, presented the importance of selecting classifiers based on data context. SVM excels in positive class accuracy, particularly in assessment and learning outcomes, while Naive Bayes performs well in the curriculum. The study concludes by emphasizing the value of a combined SVM and Naive Bayes approach for a robust analysis and offers insights and methodologies for universities to enhance educational programs through meaningful feedback interpretation. © 2024 IEEE.

关键词： Support vector machines

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On The Cross-Modal Transfer from natural language to Code through Adapter Modules 30

On The Cross-Modal Transfer from Natural Language to Code th...

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30th IEEE/ACM international conference on Program Comprehension (ICPC)

作者： Goel, Divyam Grover, Ramansh Fard, Fatemeh H. Indian Inst Technol Roorkee Uttar Pradesh India Delhi Technol Univ Delhi India Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9781450392983

Pre-trained neural language Models (PTLM), such as CodeBERT, are recently used in software engineering as models pre-trained on large source code corpora. Their knowledge is transferred to downstream tasks (e.g. code clone detection) via fine-tuning. In natural language processing (NLP), other alternatives for transferring the knowledge of PTLMs are explored through using adapters, compact, parameter efficient modules inserted in the layers of the PTLM. Although adapters are known to facilitate adapting to many downstream tasks compared to fine-tuning the model that require retraining all of the models' parameters- which owes to the adapters' plug and play nature and being parameter efficient-their usage in software engineering is not explored. Here, we explore the knowledge transfer using adapters and based on the naturalness Hypothesis proposed by Hindle et. al [12]. Thus, studying the bimodality of adapters for two tasks of cloze test and code clone detection, compared to their benchmarks from the CodeXGLUE platform. These adapters are trained using programming languages and are inserted in a PTLM that is pre-trained on English corpora (N-PTLM). Three programming languages, C/C++, Python, and Java, are studied along with extensive experiments on the best setup used for adapters. Improving the results of the N-PTLM confirms the success of the adapters in knowledge transfer to software engineering, which sometimes are in par with or exceed the results of a PTLM trained on source code;while being more efficient in terms of the number of parameters, memory usage, and inference time. Our results can open new directions to build smaller models for more software engineering tasks. We open source all the scripts and the trained adapters.

关键词： Pre-trained language Models Transfer learning Adapters Parameter Efficient Models

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Handwritten language Detection using Deep Learning 15

Handwritten Language Detection using Deep Learning

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15th international conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Mahamuni, Sakshi D. Gawade, Sumant D. Pednekar, Prithviraj P. Vaghela, Karan H. Patil, Sachin Sambhaji D Y Patil University School of Engineering and Technology Ambi Pune India

ISBN: (纸本)9798331300579

Handwritten language detection plays a crucial role in various applications, such as document analysis, translation services, and forensic document examination. This research focuses on the development and evaluation of machine learning models for the automated identification of the language in handwritten texts. The proposed approach leverages advanced techniques in image processing and natural language processing to extract relevant features from handwritten documents. The first step involves preprocessing the handwritten images to enhance clarity and reduce noise. Subsequently, a combination of traditional and deep learning-based feature extraction methods is employed to capture the distinctive characteristics of different languages. The extracted features are then fed into a machine learning classifier, such as a support vector machine or a neural network, for training and validation. To assess the performance of the proposed system, a diverse dataset comprising handwritten samples from multiple languages is used. The evaluation metrics include accuracy, precision, recall, and F1 score. The experimental results demonstrate the effectiveness of the developed model in accurately identifying the language of handwritten text across various scripts and writing styles. Furthermore, the research explores the impact of dataset size, variability, and the transferability of the trained model to unseen data. The findings contribute to the advancement of handwritten language detection systems, paving the way for improved accuracy and applicability in real-world scenarios. This research has implications for document digitization, multilingual information retrieval, and linguistic analysis in forensic investigations. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Effective Use of knowledge Graphs in a language Representation Model 14th

Effective Use of Knowledge Graphs in a Language Representati...

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14th international conference on Computer Science and its Applications, CSA 2022 and the 16th KIPS international conference on Ubiquitous Information Technologies and Applications, CUTE 2022

作者： Min, Chanwook Na, Hyungsun Song, Yeji Ahn, Jinhyun Im, Dong-Hyuk Kwangwoon University Seoul Korea Republic of Jeju National University Jeju Korea Republic of

ISBN: (纸本)9789819912513

Methods such as knowledge-enabled language representation model (K-BERT) that help train models using external information, such as knowledge graphs, have recently been proposed in the field of natural language processing. However, adding knowledge that does not match the topic when external information is used may hinder training. Hence, a method is required that adds only knowledge matching the topic of the input data by partitioning the external information by topic. Topic-based knowledge graph BERT (TK-BERT), which uses the existing latent Dirichlet allocation (LDA) model to partition the knowledge, does not consider the contextual information. To compensate for this drawback and effectively use external information, this study uses the BERTopic technique to partition the knowledge graph. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： knowledge graph

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Software System Testing Assisted by Large language Models: An Exploratory Study 36th

Software System Testing Assisted by Large Language Models: A...

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36th international conference on Testing Software and Systems

作者： Augusto, Cristian Moran, Jesus Bertolino, Antonia de la Riva, Claudio Tuya, Javier Univ Oviedo Comp Sci Dept Gijon Spain ISTI CNR Consiglio Nazl Ric Pisa Italy

ISBN: (纸本)9783031808883;9783031808890

Large language models (LLMs) based on transformer architecture have revolutionized natural language processing (NLP), demonstrating excellent capabilities in understanding and generating human-like text. In Software engineering, LLMs have been applied in code generation, documentation, and report writing tasks, to support the developer and reduce the amount of manual work. In Software Testing, one of the cornerstones of Software engineering, LLMs have been explored for generating test code, test inputs, automating the oracle process or generating test scenarios. However, their application to high-level testing stages such as system testing, in which a deep knowledge of the business and the technological stack is needed, remains largely unexplored. This paper presents an exploratory study about how LLMs can support system test development. Given that LLM performance depends on input data quality, the study focuses on how to query general purpose LLMs to first obtain test scenarios and then derive test cases from them. The study evaluates two popular LLMs (GPT-4o and GPT-4o-mini), using as a benchmark a European project demonstrator. The study compares two different prompt strategies and employs well-established prompt patterns, showing promising results as well as room for improvement in the application of LLMs to support system testing.

关键词： Large language Model Software Testing System Testing Test Cases Test Scenarios

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Sound Wave Scribe: Bridging Spoken language and Written Text 1

Sound Wave Scribe: Bridging Spoken Language and Written Text

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1st international conference on Pioneering Developments in Computer Science and Digital Technologies, IC2SDT 2024

作者： Shaik, Mohammed Ali Kethireddy, Achyuthreddy Nerella, Sanjay Pinninti, Samadarshini Kathare, Vasanth Pitta, Pavan SR University Telangana Warangal506371 India

ISBN: (纸本)9798350365016

The Sound Wave Scribe Voice Assistant paper is, about how people interact with computers. It uses the advancements in natural language processing (NLP) and machine learning (ML) to incorporate voice commands. Inorder to rely on the intelligence when a user talks to the system, the assistant picks up the sound through a microphone or other input tools. The raw sound goes through steps like reducing noise breaking it into segments and extracting features. Then the system changes the sound into text using speech recognition (ASR). Different NLP components like tokenization part of speech tagging and named entity recognition break down the text into parts. After that the system figures out what users want by analyzing the text. natural language understanding (NLU) figures out what users are saying while context management ensures transitions in conversations. Depending on what users want the system creates responses that sound human like using natural language generation (NLG) techniques. Dialog management keeps track of context and knowledge graphs offer data for answers. Finally computer generated speech-made using text to speech (TTS) libraries-turns the responses back, into sounding spoken words. The systems flexibility goes beyond setting reminders and scraping the web. You can do it all with voice commands. With connections, to databases and online platforms the Sound Wave Scribe Voice Assistant steps up user ease becoming a resource, for voice activated tech. © 2024 IEEE.

关键词： Speech recognition

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