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Search if you don't know! knowledge-Augmented Korean Grammatical Error Correction with Large language Models

Search if you don't know! Knowledge-Augmented Korean Grammat...

引用

2024 conference on Empirical Methods in natural language processing, EMNLP 2024

作者： Koo, Seonmin Kim, Jinsung Park, Chanjun Lim, Heuiseok Department of Computer science and Engineering Korea University Korea Republic of Upstage AI Korea Republic of

ISBN: (纸本)9798891761681

Grammatical error correction (GEC) system is a practical task used in the real world, showing high achievements alongside the development of large language models (LLMs). However, these achievements have been primarily obtained in English, and there is a relative lack of performance for non-English data, such as Korean. We hypothesize that this insufficiency occurs because relying solely on the parametric knowledge of LLMs makes it difficult to thoroughly understand the given context in the Korean GEC. Therefore, we propose a knowledge-Augmented GEC (KAGEC) framework that incorporates evidential information from external sources into the prompt for the GEC task. KAGEC first extracts salient phrases from the given source and retrieves non-parametric knowledge based on these phrases, aiming to enhance the context-aware generation capabilities of LLMs. Furthermore, we conduct validations for fine-grained error types to identify those requiring a retrieval-augmented manner when LLMs perform Korean GEC. According to experimental results, most LLMs, including ChatGPT, demonstrate significant performance improvements when applying KAGEC. © 2024 Association for Computational Linguistics.

关键词： Modeling languages

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Disease Prediction Using Symptoms based on Machine Learning Algorithms and natural language processing

Disease Prediction Using Symptoms based on Machine Learning ...

引用

2023 international conference on Artificial Intelligence and knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Hema, Pothana Darbha, Arunarkavalli Sunny, N. Naganjani, Raavi Venkata V R Siddhartha Engineering College Dept. of Cse Vijayawada India

ISBN: (纸本)9798350334357

Health became an important aspect in today's world. People are suffering from various viruses, infections etc. Citizens are getting terrified even if they have some symptoms. This stress is just because of not knowing the disease and its seriousness. Sometimes it becomes too late for a disease to be diagnosed and treated which may become a life threat. Additionally, in some situations patients may not be in a position to visit a doctor at times it may become a serious issue. If a system predicts the disease of a patient, then he/she might get out of the anxiety. This can be done by using some machine learning algorithms like Naïve Bayes, Random Forest and Decision Trees. The ultimate result will be the result of the model with high accuracy. Since communicating will be a difficult, a chatbot is implemented. This is done by using natural language processing (NLP). The final result will be shown on the interface using all three machine learning techniques, and feature extraction will be done depending on their symptoms. Five modules make up the proposed methodology. Preprocessing will be done on the dataset in the first module. The decision tree classifier is used to generate a prediction model in the second module. The Random forest method is used for forecast the illness in the third module, the Naive Bayes technique is utilized in the fourth model, natural language processing is used for chatbot mechanism is the fifth module, with the outputs from all the three models taken into account to find best model. © 2023 IEEE.

关键词： Decision trees

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Exploring Social Media Sentiment Polarity: A Case Study Utilizing language Models 4

Exploring Social Media Sentiment Polarity: A Case Study Util...

引用

4th IEEE international conference on Software engineering and Artificial Intelligence (SEAI)

作者： Yang, Bo Johnson-Shapoval, Sophie Bowie State Univ Dept Comp Sci Bowie MD 20715 USA

ISBN: (纸本)9798350374353;9798350374346

Large language models are crucial for processing social media data, aiding in recommendation systems, sentiment analysis, and more. This study proposes a method to enhance sentiment analysis accuracy by combining word embeddings with insights from a language-based model. Focused on consumer reviews, sentiments are categorized based on extreme polarities, excluding intermediate ratings. Empirical assessments show superior outcomes when combining opinions, summaries, and pre-processed summaries. Machine learning algorithms are applied to hybrid vectors to classify sentiments accurately, distinguishing between positive and negative viewpoints. This approach offers nuanced sentiment analysis within consumer feedback, facilitating understanding of product reviews. Overall, the method integrates feature extraction and machine learning to analyze binary polar data, providing sentiment estimates in a concise vector form, with promising implications for sentiment analysis and consumer sentiment understanding.

关键词： sentiment analysis polarity categorization language models artificial intelligence natural language processing

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POS Tagging Using Hidden Markov Models in natural language processing

POS Tagging Using Hidden Markov Models in Natural Language P...

引用

2024 international conference on Signal processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Poorna Chandrika, V.V.N.S. Verma, Ridhi Charan, Natra. Ditheswar, Sabbu Hansika, S. Ishwariya, R. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Amrita school of Engineering Amrita Vishwa Vidyapeetham Dept of Science and Humanities Chennai India

ISBN: (纸本)9798331540685

In this paper, we thoroughly examine the fundamental part which is Parts-Of-Speech (POS) Tagging of natural language processing (NLP), especially. Our examination revolves around Hidden Markov Models (HMMs) usage to face up to this crucial element of language comprehension. Starting with the fundamental principles of the NLP, we go through its history after that, and we explore the inner workings of the N-gram Models and the Markov Models with a more detailed view finally in Hidden Markov Models. We meticulously uncover approaches used in POS Tagging preparation, so that we can get down to the in-depth methodological part of the journey. The intricate workings of Viterbi Algorithm and Maximum Entropy Markov Models will be clarified in the methodology section as they are keystone in our POS Tagging exploits. By performing highly standardized experimentation, we check the competence of HMMs in POS tagging, separately with CRFs (Conditional Random Fields). Ultimately, the report is a comprehensive description of the use of Hidden Markov Models in POS Tagging, that presents the applications, theoretical and practical bases, and the implications in the area of natural language understanding. © 2024 IEEE.

关键词： Hidden Markov models

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ChatGPT the Omniscient? A Guide to Effective Prompting 7

ChatGPT the Omniscient? A Guide to Effective Prompting

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7th IEEE international conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Kovari, Attila Institute of Digital Technology Eszterházy Károly Catholic University Faculty of Computer Science Eger Hungary Institute of Electronics and Communication Systems Obuda University Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary Institute of Computer Science University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The invention of artificial intelligence and natural language processing has revolutionised human-machine interaction, and OpenAI's ChatGPT models are at the forefront of this. GPT-3 and GPT-4 models generate human-like text, answer questions and perform all kinds of language-related tasks. ChatGPT relies heavily on the quality of the requests it receives. This paper addresses the issue of efficient prompting in order to maximise its potential. Different prompting techniques are described in the paper: prompt template design, continuous prompts, few-shot learning, chain of thought prompting and metadata expansion. The conclusions show that learning how to write good prompts is crucial for realizing the full potential of ChatGPT in various natural language processing tasks. © 2024 IEEE.

关键词： Zero-shot learning

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AI-enabled Regulatory Change Analysis of Legal Requirements 32

AI-enabled Regulatory Change Analysis of Legal Requirements

引用

32nd IEEE international Requirements engineering conference (RE)

作者： Abualhaija, Sallam Ceci, Marcello Sannier, Nicolas Bianculli, Domenico Briand, Lionel C. Zetzsche, Dirk Bodellini, Marco Univ Luxembourg SnT Luxembourg Luxembourg Univ Limerick Lero SFI Ctr Software Res Limerick Ireland Univ Ottawa Sch EECS Ottawa ON Canada Univ Luxembourg FDEF Luxembourg Luxembourg

ISBN: (纸本)9798350395129;9798350395112

Statutory law is subject to change as legislation develops over time - new regulation can be introduced, while existing regulation can be amended, or repealed. From a requirements engineering (RE) perspective, such change must be dealt with to ensure the compliance of software systems at all times. Understanding the implications of regulatory change on compliance of software requirements requires navigating hundreds of legal provisions. Analyzing instances of regulatory change entirely manually is not only time-consuming, but also risky, since missing a change may result in non-compliant software which can in turn lead to hefty fines. In this paper, we propose MURCIA, an automated approach that leverages recent language models to assist human analysts in analyzing regulatory changes. To build MURCIA, we define a taxonomy that characterizes the regulatory changes at the textual level as well as the changes in the text's meaning and legal interpretation. We evaluate MURCIA on four regulations from the financial domain. Over our evaluation set, MURCIA can identify textual changes with F1 score of 90.5%, and it can provide, according to our taxonomy, the text meaning and legal interpretation with an F1 score of 90.8% and 83.7%, respectively.

关键词： Regulatory Change Prompt engineering natural language processing (NLP) Large language Models (LLMs) ChatGPT Regulatory Compliance

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UNSUPERVISED HUMAN ACTIVITY RECOGNITION VIA LARGE language MODELS AND ITERATIVE EVOLUTION 49

UNSUPERVISED HUMAN ACTIVITY RECOGNITION VIA LARGE LANGUAGE M...

引用

49th IEEE international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Gao, Jiayuan Zhang, Yingwei Chen, Yiqiang Zhang, Tengxiang Tang, Boshi Wang, Xiaoyu Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Beijing Key Lab Mobile Comp & Pervas Device Beijing Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Human activity recognition (HAR) is crucial for health monitoring and disease diagnosis in Internet-of-Things environments. However, existing HAR approaches either suffer from poor accuracy or achieve high accuracy at the expense of costly manual annotations. To overcome the challenge above, we propose a novel method named LLMIE-UHAR that that leverages LLMs and Iterative Evolution to realize Unsupervised HAR. Specifically, with our designed prompt engineering mechanism, we employ large language models to fuse both contextual and semantic information, and annotate key samples selected by a clustering algorithm. Moreover, LLMIE-UHAR enhances the recognition accuracy with iterative evolution of clustering algorithm, large language models and the neural network based recognition model. Experiments conducted on the public ARAS datasets show the efficiency of our method, achieving an accuracy of 96.00%. This highlights the practical value of our approach.

关键词： Large language Models Human Activity Recognition Ubiquitous Computing Unsupervised Learning Computing methodologies

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Comparative Analysis of Learning Models for Solving natural language processing Tasks

Comparative Analysis of Learning Models for Solving Natural ...

引用

2024 international Russian Smart Industry conference, SmartIndustryCon 2024

作者： Khabibrakhmanova, Alsu Alekseev, Ilya Tazieva, Ramilya Kazan State Power Engineering University Department of Information Technologies and Intelligent Systems Kazan Russia Kazan National Research Technological University Department of Informatics and Applied Mathematics Kazan Russia

ISBN: (纸本)9798350395044

The paper contains an analytical review of methods for solving problems of semantically coherent text processing, search and selection of learning models for solving text processing problems, comparison of the obtained results, summarising the results. The learning models provided in Hugging Face and Scikit-Learn library for solving text generation, text tone detection and text classification tasks were selected for the study. As a result of the study, a comparative analysis of learning models for solving natural language processing tasks was carried out. Various machine learning methods such as SGDClassifier, KNeighborsClassifier, MultinomialNB, LogisticRegression, Decision TreeClassifier, RandomForestClassifier, support vector method and others were considered. © 2024 IEEE.

关键词： Text processing

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End-To-End knowledge Graph Construction and Reasoning Using Path Based Metrics 1

End-To-End Knowledge Graph Construction and Reasoning Using ...

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1st IEEE international conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Shejole, Rohit Dofe, Sanket Shejole, Prachi Veermata Jijabai Technological Institute Dept. of Electrical Engineering Mumbai India Veermata Jijabai Technological Institute Dept. of Computer Engineering and Information Technology Mumbai India D.Y. Patil College of Engineering Dept. of Mechanical Engineering Pune India

ISBN: (纸本)9798350356816

The schema-less nature of knowledge graphs has led to their widespread adoption. It makes it possible for knowledge graphs to expand without interruption and to add new entities and relationships as needed. knowledge graphs are now an effective tool for providing textual data with semantics and representing knowledge as a labelled directed graph. Every item, entity, and user are represented as nodes in a knowledge graph, which is then created by connecting the nodes that communicate with one another through edges. knowledge graphs can hold a variety of more comprehensive types of information and have a wealth of natural semantics. Its mode of expression is similar to that of natural language. Nevertheless, there is still no accepted definition or format for expressing knowledge graphs. © 2024 IEEE.

关键词： knowledge graph

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Research on the Construction of Non-subject-object Argument knowledge Base of Chinese Verbs

Research on the Construction of Non-subject-object Argument ...

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26th international conference on Asian language processing (IALP)

作者： Wang, Chengwen Xun, Endong Peking Univ MOE Key Lab Computat Linguist Beijing Peoples R China Beijing Language & Culture Univ Inst Big Data & Language Educ Beijing Peoples R China

ISBN: (数字)9781665476744

ISBN: (纸本)9781665476744

Whether in the field of linguistics or natural language processing (NLP), the study of Chinese verb arguments has always been a priority. The knowledge base of verb arguments is an essential resource for linguistic research and natural language processing research. However, previous knowledge bases have the following problems: (1) The method of constructing argument knowledge with verbs as items only lists few instances that can act as verb-argument roles, and cannot comprehensively present a large number of instances that act as argument roles. (2) Regarding determining the number of argument roles dominated by verbs, except for subject and object arguments, there is no clear operational standard for how many non-subject-object argument roles a verb dominates and how to determine them. Therefore, we propose an argument knowledge acquisition strategy based on big data, relying on a structured corpus and a structured retrieval system to acquire verb-argument instances from a large-scale corpus. At the same time, a calculation method for automatically determining the priority of verb non-subject-object argument is proposed, and preliminary experiments verify its effectiveness.

关键词： structure retrieval non-subject-object argument argument role priorities

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