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AcawebAgent: A Large language Model-Powered Assistant for Early Academic Research

AcawebAgent: A Large Language Model-Powered Assistant for Ea...

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international conference on Computer engineering and Applications (ICCEA)

作者： Yingli Yang Xiaoting Wang Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

The rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies, particularly in large language models (LLMs) like the GPT series, has significantly impacted research and industrial applications. These models excel in various natural language processing (NLP) tasks, including text generation, comprehension, and translation. However, harnessing these capabilities for academic research still presents challenges, particularly for early-career researchers navigating extensive literature. In this paper, we introduce AcawebAgent, an inventive AutoAgent specifically crafted to enhance the abilities of beginner researchers. It leverages the advanced generation and analysis capabilities of large language models (LLMs) to collect open academic knowledge from the web. AcawebAgent offers customized research reports that include indepth overviews, practical applications, the latest developments, and future trajectories of specific research domains, thereby significantly diminishing the time and effort needed for comprehensive literature reviews and trend analyses.

关键词： knowledge engineering Technological innovation Navigation Large language models Bibliographies Refining Market research

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Proceedings of the 2023 IEEE international conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023

Proceedings of the 2023 IEEE International Conference on Ind...

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2023 IEEE international conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023

ISBN: (纸本)9798350313635

The proceedings contain 72 papers. The topics discussed include: knowledge engineering using natural language processing of user reviews for Bahrain's mobile government applications;digital twin for automated industrial optimization: intelligent machine selection via process modelling;semantic textual similarity in requirement specification and use case description based on sentence transformer model;enhanced visual cryptosystem using BLAKE2 hash algorithm;detection of physical adversarial attacks on traffic signs for autonomous vehicles;performance analysis of enhancement methods on fetal ultrasound images;a modular deep learning framework for scene understanding in augmented reality applications;coordinate-based geometric features and nearest neighbor performance in 2d facial classification;feature selection for cycle life prediction of fast-charged lithium-ion batteries;optimization of multi-objective rolling stock maintenance scheduling with Harris' Hawk optimizer;and circular polarized 30-pointed star wideband antenna with cross slot.

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Advancements in natural language Understanding and Applications: A Study on ChatGPT with Novel Approaches to Fairness and Accuracy in Education

Advancements in Natural Language Understanding and Applicati...

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Signal processing, Communications and engineering Management conference (ISPCEM), international

作者： Siyuan Guo Johns Hopkins University Baltimore USA

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

natural language processing (NLP) has evolved significantly since the 1950s, with early research focusing on tasks like machine translation, information retrieval, and text summarization. Initially, most NLP research emphasized syntactic analysis, but the challenge of understanding and processing unstructured text remained. Recent advancements in large-scale language models (LLMs), such as ChatGPT, have revolutionized the field by enabling zero-shot execution of various NLP tasks without task-specific training. ChatGPT, based on the Transformer architecture, represents a major breakthrough in NLP, generating high-quality natural language responses and self-correcting based on dialogue context. This paper investigates the applications of ChatGPT in educational tasks, focusing on enhancing the fairness and accuracy of its responses. Fairness refers to ensuring that users receive consistent and unbiased answers to the same queries, while accuracy involves improving the precision of ChatGPT's outputs, particularly in complex educational tasks such as scientific literature review and content comprehension. We propose novel solutions, including dynamic model weighting for fairness and integrating external knowledge bases to boost accuracy. Experiments conducted on educational tasks demonstrate significant improvements in both fairness and accuracy, showing that the proposed methods enable ChatGPT to provide more reliable and consistent educational support. Our findings suggest that, with these innovations, ChatGPT can play a pivotal role in improving educational outcomes by offering fair access to quality learning content while addressing accuracy challenges. Future work will explore expanding this approach to other fields where NLP plays a crucial role.

关键词： Training Technological innovation Accuracy Computational modeling knowledge based systems Focusing Text summarization Chatbots Transformers natural language processing

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On the use of Pretrained language Models for Legal Italian Document Classification 27

On the use of Pretrained Language Models for Legal Italian D...

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

作者： Benedetto, Irene Sportelli, Gianpiero Bertoldo, Sara Tarasconi, Francesco Cagliero, Luca Giacalone, Giuseppe Politecnico di Torino Corso Duca degli Abruzzi 24 TO Torino10129 Italy H-Farm Innovation Via San Quintino 31 Torino10121 Italy Giuffrè Francis Lefebvre Via Busto Arsizio 40 Milano20151 Italy

Document classification is helpful for law professionals to improve content browsing and retrieval. Pretrained language Models, such as BERT, have become established for legal document classification. However, legal content is quite diversified. For example, documents vary in length from very short maxims to relatively long judgements;certain document types are rich of domain-specific expressions and can be annotated with multiple labels from domain-specific taxonomies. This paper studies to what extent existing pretrained models are suited to the legal domain. Specifically, we examine a real business case focused on Italian legal document classification. On a proprietary dataset with thousands of diversified categories (e.g., legal judgements, maxims, and legal news) we explore the use of Pretrained language Models adapted to handle various content types. We collect both quantitative and qualitative results, highlighting best and worst cases, anomalous categories, and limitations of currently available models. © 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： Multi-Class Legal Document Classification natural language processing Pretrained language Models

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A Lexicon Enhanced Chinese Long Named Entity Recognition Using Word-Aware Attention 6

A Lexicon Enhanced Chinese Long Named Entity Recognition Usi...

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6th international conference on Machine Learning and natural language processing, MLNLP 2023

作者： Wang, Chen Xiong, Xiong Wang, Linjie Zheng, Yifeng Liu, Yunfei Li, Shengyang Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences BeiJing China University of Chinese Academy of Sciences BeiJing China

ISBN: (纸本)9798400709241

In recent years, due to the rapid growth of space science and utilization research of China Manned Space engineering, a considerable amount of technical documents and web data have been produced. Named entity recognition (NER) plays a vital role in extracting valuable information from these resources. However, the presence of numerous long named entities, which consist of complex, specialized terms and diverse phrase combinations, poses significant challenges for existing methods in identifying them correctly. To resolve this issue, we introduced two modules, SkipWord-Lattice and Word-Aware Attention, to improve the widely used lexical enhancement model in Chinese NER. SkipWord-Lattice reduces the overlap, redundancy, and confusion of word tokens, while Word-Aware Attention enhances the semantic interaction between character tokens and word tokens, improving the model's comprehension of word tokens. Collectively, these modules significantly increase the model's ability to identify long named entities. Moreover, using relevant corpus data publicly available in the field of space science and utilization of China Manned Space engineering, we built a Chinese NER dataset, named SSUIE-NER, comprising rich entity types and a substantial number of long named entities. Experimental results indicate that, our method significantly improves the recognition of long named entities, outperforming other state-of-The-Art (SOTA) approaches on SSUIE-NER and other three benchmarks in Chinese NER. © 2023 ACM.

关键词： Semantics

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Detecting the Political Bias in News Articles and Similarity Using Word Embeddings in American Journalism 1

Detecting the Political Bias in News Articles and Similarity...

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1st international conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Sinha, Apoorv Kumar Kumar Singh, Sanskriti Sanjay Sai, Shreyas School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350339727

Political polarity is on the rise in the world and machine learning tasks are adapting to the same. No longer is it sufficient to simply classify something as positive or negative, for a subject can be polarly opposite like positive or negative, but at the same time, can be switched entirely depending on the perspective. This is why the study of political bias makes up for a very interesting study since the multi-classification task of determining the alignment of a paper is a different take as to the usual understanding of sentiment analysis. Sentiment analysis in general is a natural language processing technique that helps to contextualize whether the natural text has any sort of bias to it. For the purposes of this paper, this polarity will be more aligned to whether a given news article in question is left-leaning, right-leaning or centrist. The outcome is doing so is to gain a knowledge of reporting in the USA as to see whether press organizations are truly neutral in their reporting and exactly how severe is the bias when it comes to particular words along with the other words they are naturally associated with. For the crux of this study, the proposed model will be utilizing three unsupervised document modelling algorithm that utilize document embeddings using paragraph vectors after utilizing Latent Dirichlet Allocation for fuzzy clustering. All three of these models will be used to make a prediction and while their accuracies will be evaluated separately, these three models will be combined towards a hard voting ensemble classifier so that a better classification score can be obtained and to reduce doubts since subjectivity is a large problem when dealing with natural text and especially, articles. © 2023 IEEE.

关键词： Sentiment analysis

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Text-Based Analysis of COVID-19 Comments Using natural language processing 3rd

Text-Based Analysis of COVID-19 Comments Using Natural Langu...

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3rd international conference on Artificial Intelligence and Speech Technology, AIST 2021

作者： Naithani, Kanchan Raiwani, Y.P. Sissodia, Rajeshwari Department of Computer Science and Engineering HNB Garhwal University Garhwal Uttarakhand Srinagar India

ISBN: (纸本)9783030957100

In dialectology, natural language processing is the process of recognizing the various ontologies of words generated in human language. Various techniques are used for analyzing the corpus from naturally generated content by users on various platforms. The analysis of these textual contents collected during the COVID-19 has become a goldmine for marketing experts as well as for researchers, thus making social media comments available on various platforms like Facebook, Twitter, YouTube, etc., a popular area of applied artificial intelligence. Text-Based Analysis is measured as one of the exasperating responsibilities in natural language processing (NLP). The chief objective of this paper is to work on a corpus that generates relevant information from web-based statements during COVID-19. The findings of the work may give useful insights to researchers working on Text analytics, and authorities concerning to current pandemic. To achieve this, NLP is discussed which extracts relevant information and comparatively computes the morphology on publicly available data thus concluding knowledge behind the corpus. © 2022, Springer Nature Switzerland AG.

关键词： COVID-19

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Frame-Based knowledge Representation for Intelligent Tutoring Systems

Frame-Based Knowledge Representation for Intelligent Tutorin...

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IT Innovation and knowledge Discovery (ITIKD), international conference on

作者： Tina Babu Rekha R Nair H S Ganesh G S Dhanush A R Varun B R Karthik Department of Computer Science and Engineering Alliance University Bengaluru India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Frame-based knowledge representation has come as a robust paradigm for representing and organizing knowledge in Intelligent Tutoring Systems (ITS). Frames as mental constructs facilitate the representation of entities of the world, their char-acteristics, and associations and, thus, are particularly amenable to domain knowledge modeling, student profiles, and teaching tactics modeling in ITS. Developments in frame-based systems have more recently targeted the promotion of adaptability, per-sonalization, and scalability in tutoring systems. Researchers have incorporated frames into machine learning methods, including deep learning and reinforcement learning, to allow for dynamic knowledge updates and real-time adjustment according to stu-dent needs. In addition the integration of frame-based represen-tations with otologies and semantic web technologies enhanced interoperability and reasoning, enabling more precise diagnosis of student misconceptions and adaptive feedback generation. This work has also been investigated in collaborative learning contexts, where frames are used to represent group interactions and collective knowledge construction. In addition, the use of natural language processing (NLP) and frame-based representations has made it possible for ITS to improve comprehension and answer students' questions in natural language more effectively resulting in an enhanced user experience. Challenges persist nonetheless, including addressing the complexity of large-scale frame-based systems as well as making knowledge acquisition and maintenance efficient. This paper summaries the latest work on frame-based knowledge representation in ITS, major innovation, uses, and prospects. Through dealing with these problems, frame-based ITS can advance, providing even better and adaptive learning experiences to learners.

关键词： Semantic Web Technological innovation Adaptation models Adaptive systems Uncertainty knowledge based systems knowledge representation Cognition natural language processing User experience

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ITALIAN-LEGAL-BERT: A Pre-trained Transformer language Model for Italian Law 23

ITALIAN-LEGAL-BERT: A Pre-trained Transformer Language Model...

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23rd international conference on knowledge engineering and knowledge Management, EKAW-C 2022

作者： Licari, Daniele Comandè, Giovanni EMbeDS Sant'Anna School of Advanced Studies Pisa56127 Italy

The state of the art in natural language processing is based on transformer models that are pre-trained on general knowledge and enable efficient transfer learning in a wide variety of downstream tasks even with limited data sets. However, these models significantly decrease performance when operating in specific and sectoral domains. This is problematic in the Italian legal context, as there are many discrepancies between the language found in generic open source corpora (e.g., Wikipedia and news articles) and legal language, which can be cryptic, Latin-based, and domain idiolectal formulas. In this paper, we introduce the ITALIAN-LEGAL-BERT model with additional pre-training of the Italian BERT model on Italian civil law corpora. It achieves better results than the 'general-purpose' Italian BERT in different domain-specific tasks. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0)

关键词： Computational linguistics

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KACE: Generating knowledge-Aware Contrastive Explanations for natural language Inference 59

KACE: Generating Knowledge-Aware Contrastive Explanations fo...

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Joint conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th international Joint conference on natural language processing (IJCNLP) / 6th Workshop on Representation Learning for NLP (RepL4NLP)

作者： Chen, Qianglong Ji, Feng Zeng, Xiangji Li, Feng-Lin Zhang, Ji Chen, Haiqing Zhang, Yin Zhejiang Univ Coliege Comp Sci & Technol Hangzhou Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China Tencent Shenzhen Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9781954085527

In order to better understand the reason behind model behaviors (i.e., making predictions), most recent work has exploited generative models to provide complementary explanations. However, existing approaches in natural language processing (NLP) mainly focus on "WHY A" rather than contrastive "WHY A NOT B", which is shown to be able to better distinguish confusing candidates and improve model performance in other research fields. In this paper, we focus on generating Contrastive Explanations with counterfactual examples in NLI and propose a novel knowledge-Aware generation framework (KACE). Specifically, we first identify rationales (i.e., key phrases) from input sentences, and use them as key perturbations for generating counterfactual examples. After obtaining qualified counterfactual examples, we take them along with original examples and external knowledge as input, and employ a knowledge-aware generative pre-trained language model to generate contrastive explanations. Experimental results show that contrastive explanations are beneficial to clarify the difference between predicted answer and other answer options. Moreover, we train an BERT-large based NLI model enhanced with contrastive explanations and achieve an accuracy of 91.9% on SNLI, gaining an improvement of 5.7% against ETPA ("Explain-Then-Predict-Attention") and 0.6% against NILE ("WHY A").

关键词： natural language processing systems

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