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knowledge-Guided Reinforcement Learning for Low-Resource Unsupervised Syllabification

Knowledge-Guided Reinforcement Learning for Low-Resource Uns...

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international Joint conference on Neural Networks (IJCNN)

作者： Wenjun Wang Ling Dong Zhengtao Yu Yuxin Huang Junjun Guo Shengxiang Gao School of Information Engineering and Automation Kunming University of Science and Technology Kunming China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Syllabification is a crucial task in natural language processing, and syllables also play a significant role as modeling units in speech processing. While deep learning methods have shown remarkable progress in syllabification, they face challenges in low-resource languages where ready-made segmentation datasets or rules are lacking. Large language models (LLMs) are mostly unsuitable for these low-resource languages as well. To address these challenges, this paper proposes an unsupervised syllabification approach that incorporates logical reasoning into the reinforcement learning training process, achieving knowledge-guided syllabification. By introducing logical reasoning knowledge and modeling the interaction between the Agent and the knowledge base (KB), the model gains a better understanding of language structures and patterns. The study primarily focuses on low-resource Lao language, with experiments conducted on a publicly available English dataset to validate the effectiveness of the proposed method.

关键词： Training Deep learning Large language models knowledge based systems Neural networks Reinforcement learning natural language processing

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Social and Scientific Relevance of Peruvian Undergraduate Theses in Computer and Systems engineering Schools 11

Social and Scientific Relevance of Peruvian Undergraduate Th...

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11th IEEE international conference on Teaching, Assessment and Learning for engineering, TALE 2022

作者： Hidalgo-León, Pilar Luna, Ana Rivas, Lornel Universidad Andina Del Cusco School of Engineering Cusco Peru Universidad Del Pacifico School of Engineering Lima Peru

ISBN: (纸本)9781665491174

The relationship between science and society has a fundamental role in the pursuit of improvement in our quality of life. In this study, we looked forward to finding relevant study topics for society, aligned with the Sustainable Development Goals approved by the UN in the 2030 Agenda. Using NLP (natural language processing), we withdraw information from 115 undergraduate theses of systems engineering and computer science degrees, available in public repositories of Peruvian universities. We analyzed and elaborated a set of indicators (similarity with the keywords of the research's lines and the statement of the SDGs) to know and quantify the degree of the scientific and social relevance of the undergraduate theses. Finally, we used bibliometric indicators (like originality, topic similarity, citations, etc.) to complement the relevance analysis contributing to knowledge about the gap of interests between academia and society. © 2022 IEEE.

关键词： natural language processing systems

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An Exploration of the Effectiveness of Machine Learning Algorithms for Text Classification 2

An Exploration of the Effectiveness of Machine Learning Algo...

引用

2nd IEEE international conference on Futuristic Technologies, INCOFT 2023

作者： Desai, Meena Suriya Prakash, J. Isme - School of Management & Entrepreneurship Atlas Skill Tech University Maharashtra Mumbai India Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350308846

This paper seeks to discover the effectiveness of various devices in gaining knowledge of algorithms for textual content types. Text class is a vital challenge in natural language processing as it has a selection of packages, from sentiment evaluation to record categorization. Exclusive algorithms were applied to the challenge, including help Vector Machines, Naive Bayes, and okay-nearest Neighbor. The paper will evaluate the overall performance of these algorithms on a dataset composed of textual content documents for one-of-akind category responsibilities. The consequences of the experiments will provide insights into how nicely each algorithm plays and any practical implications of using them for text categories. Furthermore, the paper will discuss the demanding situations and barriers to using these algorithms for text classes. Subsequently, viable guidelines for destiny research could be mentioned. © 2023 IEEE.

关键词： Classification (of information)

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Interpretable natural language Understanding 23

Interpretable Natural Language Understanding

引用

32nd ACM international conference on Information and knowledge Management (CIKM)

作者： He, Yulan Kings Coll London London England

ISBN: (纸本)9798400701245

In recent years, we have witnessed the shift of paradigms in natural language processing (NLP) from fine-tuning large-scale pre-trained language models (PLMs) on task-specific data to prompt-based learning. In the latter, the task description is embedded into the PLM input, enabling the same model to handle multiple tasks. While both approaches have demonstrated impressive performance in various NLP tasks, their opaque nature makes comprehending their inner workings and decision-making processes challenging for *** this talk, I will share the research undertaken in my group to address the interpretability concerns surrounding neural models in language understanding. This includes a hierarchical interpretable text classifier going beyond word-level interpretations, uncertainty interpretation of text classifiers built on PLMs, explainable recommender systems by harnessing information across diverse modalities, and explainable student answer scoring. I will conclude my talk by offering insights into potential future developments in interpretable language understanding.

关键词： model interpretation uncertainty interpretation pre-trained language models recommender systems rationale generation student answer scoring

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Leveraging Machine Learning for Career Prediction and Resume Analysis in Career Assist 10

Leveraging Machine Learning for Career Prediction and Resume...

引用

10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer engineering, UPCON 2023

作者： Soni, Nupur Nishant, Neerav Singh, Mohit Ansari, Shadab Siddiqui, Ahmad Tasneem Ansar, Syed Anas School of Computer Applications Babu Banarasi das University Lucknow India School of Engineering Babu Banarasi das University Lucknow India

ISBN: (纸本)9798350382471

A reliable career guidance system has become more crucial in this era of rapid technological advancements and ever-evolving industries. Career Prediction Application emerges as a trailblazer, reshaping our perception of professional journeys. This cutting-edge web-based platform, meticulously crafted using Django, leverages the wonders of machine learning to forecast potential career options for students and professionals alike accurately. The application puts the power of a personal career advisor right at fingertips, guiding you toward the most suitable paths in the vast landscape of opportunities. With its interactive and user-friendly interface, this system takes career planning to a whole new level, making it a must-have tool in today's fast-paced world. The platform boasts a plethora of powerful features, including a cutting-edge Resume Analyzer, an extensive knowledge Network, and a curated collection of Courses, all aimed at empowering users to augment their skills and excel in their chosen careers. The centerpiece of this application lies in its Career Prediction module, which leverages the K-Nearest Neighbors (KNN) algorithm, a state-of-the-art machine learning technique, to scrutinize and evaluate user data meticulously. The resume analyzer utilizes natural language processing to analyze resumes, ensuring industry-standard content and formatting for increased visibility and job prospects. Overall proposed application leverages sophisticated algorithms and data analysis techniques to effectively match individuals' unique aptitudes, skills, and interests with the most suitable professions, enabling informed decision-making and fostering heightened career satisfaction. © 2023 IEEE.

关键词： data analysis data analysis techniques forecast k-nearest neighbors (knn) algorithm natural language processing (nip) prediction module user's career

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Secure Sensitive Information on IoT using Machine Learning

Secure Sensitive Information on IoT using Machine Learning

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2023 international conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Nagendiran, S. Renugadevi, R. Kumar, R. P. Anto Sasirekha, K. Harini, R. R.M.K. Engineering College India R.M.K. Engineering College Department of Computer Science and Engineering India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering India R.M.D Engineering College Computer Science and Business Systems India Veltech Multitech Dr.rangarajan Dr. Sakunthala Engineering College Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350313987

In a number of industries, including banking, cybersecurity, healthcare, and others, risk assessment is an essential procedure. By automating data analysis, seeing trends, and offering insights that might help organisations make better decisions, artificial intelligence (AI) tools can greatly improve risk assessment. Here are some typical AI methods and strategies for risk assessment. Machine learning is the first step, followed by natural language processing (NLP), Reinforcement Learning, Predictive Analytics, and so on. While AI may significantly improve risk assessment, it ought to be used in combination with human judgement and domain knowledge. Additionally, when using AI for risk assessment, it's imperative to address issues with the accuracy of data, bias, and ethics. To guarantee the accuracy and continued relevance of risk evaluations, regular verification of models and updating is also necessary. The importance of the attributes in the UNSW-NB15 collections is therefore thoroughly analysed in this work. This research analyses a UNSW-NB15 collection of data generation to address the problems associated with the lack of any network reference data sets. This data collection combines network traffic assault actions that are now synthesised with real-world modern norms. The UNSWNB15 information set's characteristics are produced using both established and cutting-edge techniques. Many Machine Learning algorithms are used to analyse the intrusion by measuring the accuracy and other features. Here the data set is classified as Binary classification and Multi Class Classification where Machine learning are applied to find out the intrusion. Random Forest Classifier (RFC) performance good for binary classification and Linear Support Vector Machine(LSVm)performance is good for Multiclass Classification. © 2023 IEEE.

关键词： Artificial Intelligence Multiclass Classification natural language processing Random Forest Classifier Reinforcement Learning

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Optimized NLP Model with Caching, Parallel processing and Streamlined I/O 7

Optimized NLP Model with Caching, Parallel Processing and St...

引用

7th international conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Rajalakshmi, B. Rani, C Nagamanjula Vijayasekaran, G. Kumar, S Mahesh Hegde, Shabrish B Srikanth, Sai New Horizon College of Engineering Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350372816

This research explores optimization strategies employed within ***, an advanced question generation system driven by natural language processing (NLP) and machine learning (ML) algorithms. The study delves into three pivotal areas of optimization: caching, parallel processing, and streamlining I/O operations. By implementing caching mechanisms using Python's ***-cache, parallelization via *** and multiprocessing modules, and streamlining I/O operations through efficient batching and buffering techniques, significant enhancements in time and space complexity are realized. The effectiveness of these optimization approaches is empirically evaluated, showcasing their profound impact on the efficiency and scalability of the *** platform. This research contributes to the advancement of question generation systems, providing valuable insights into effective optimization strategies for NLP applications, such as question generation, semantic analysis, and language modeling. © 2024 IEEE.

关键词： Semantics

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AI JURIST: EMPOWERING LEGAL knowledge AND PRACTICES IN INDIA

AI JURIST: EMPOWERING LEGAL KNOWLEDGE AND PRACTICES IN INDIA

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international conference on Green Energy, Computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Kadhare, Rupak Shaharao, Rushikesh Thomas, Achamma Sakarkar, Gopal Department of Artificial Intelligence G.H. Raisoni College of Engineering Maharashtra Nagpur India Dr Vishwanath Karad MIT World Peace University Kothrud Pune India

ISBN: (纸本)9781837242689

In the field of legal studies and other related fields, identifying and to study accurate legal information, such as laws and case precedents, is a difficult challenge. The primary focus of this research is to utilise natural language processing (NLP) and AI techniques for Legal Information Retrieval System to solve this challenge. This system will help lawyers and legal practitioners in building strong arguments, developing effective strategies, and making well-informed decisions critical for successful judicial proceedings. Additionally, it empowers individuals, even without prior legal expertise, to enhance their legal knowledge and better understand the law. Our study used a dataset of 70 thousand records where each record comprises case titles, proceedings, authors, citations, and legal related information. The input options for the system are either legal documents like FIRs or Query containing legal questions in the format of PDFs or Images. Afterwards, utilising the capabilities of LLMs the output is a detailed structured Legal Research Report that consists of prior cases, judgements, laws, courts etc. The system will exponentially boost efficiency by providing precise, time-saving, and easily accessible information with enhanced retrieval accuracy. The evaluation metrics yielded an 80% accuracy in quantitative evaluations, complemented by positive feedback from legal professionals highlighting the system's efficacy in delivering accurate, comprehensive, and relevant legal research reports. These results showcase the potential of proposed solution for practical implementation and further evaluations for its use in the legal domain. The proposed system has the capability to be a companion to humans in information gathering and understanding, thereby contributing towards the improvement of legal knowledge, legal research and the decision-making process. © The Institution of engineering & Technology 2024.

关键词： Semantics

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Utilizing Edge Attention in Graph-Based Code Search 34

Utilizing Edge Attention in Graph-Based Code Search

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34th international conference on Software engineering and knowledge engineering, SEKE 2022

作者： Zhao, Wei Liu, Yan School of Software Engineering Tongji University Shanghai China

ISBN: (纸本)1891706543

Code search refers to searching code snippets with specific functions in a large codebase according to natural language description. Classic code search approaches, using information retrieval technologies, fail to utilize code semantics and bring noisy and irrelevant. During the last recent years, there has been an ample increase in the number of deep learning-based approaches, which embeds lexical semantics into unified vectors to achieve higher-level mapping between natural language queries and source code. However, these approaches are struggling with how to mine and utilize deep code semantics. In this work, we study how to leverage deeper source code semantics in graph-based source code search, given graph-based representation is a promising way of capturing program and has rich explainability. We propose a novel code search approach called EAGCS (Edge Attention-based Graph Code Search), which is composed of a novel code graph representation method called APDG (Advanced Program Dependence Graph) and a graph neural network called EAGGNN (Edge Attention-based GGNN) which can learn the latent code semantics of APDG. Experiment results demonstrate that our model outperforms the GGNN-based search model and DeepCS. Moreover, our comparison study shows that different edge enhancement strategies have different contributions to learning the code semantics. © 2022 knowledge Systems Institute Graduate School. All rights reserved.

关键词： natural language processing systems

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Comparative Analysis of Features in Formal Semantic Conceptual Representation (FSCR) of Modal Verb Based on Causal Attribute Partial Ordered Structure A case study of the FSCRs of modal verbs expressing the meaning of "permission" 24

Comparative Analysis of Features in Formal Semantic Conceptu...

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3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Wang, Liyang Yu, Jianping Fu, Jilin Zhang, Tao Center of Computational Linguistic Studies School of Foreign Studies Yanshan University Hebei Qinhuangdao China School of Information Science and Engineering Yanshan University Hebei Qinhuangdao China

ISBN: (纸本)9798400707308

The progression of natural language processing (NLP) has considerably contributed to the development of general linguistic studies. Among them, the complexity and uncertainty of the semantic system of modal verbs lead to the demand of an effective approach for word sense disambiguation (WSD). In this article, the casual attribute partial order structure (CAPOS) approach is adopted to extract the optimal linguistic features to form formal semantic conceptual representation (FSCR) of English modal verbs. Modal verbs can, may, could, might and be able to, which express the modal meaning of permission, are selected as target words of the comparative study. A WSD rule model with an average accuracy reaching 94.11% is constructed by 1,600 samples sentence randomly selected from a multi-genre corpus of 5 million words. On basis of the features in WSD rules, the FSCRs of modal verbs could be defined and some valuable knowledge about modal semantic class analysis can be mined through the comparative study among modal verbs delivering the same meaning. © 2024 Copyright held by the owner/author(s).

关键词： Latent semantic analysis

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