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ByteScience: Bridging Unstructured Scientific Literature and Structured Data with Auto Fine-tuned Large language Model in Token Granularity 24

ByteScience: Bridging Unstructured Scientific Literature and...

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24th IEEE international conference on Data Mining Workshops, ICDMW 2024

作者： Xie, Tong Zhang, Hanzhi Wang, Shaozhou Wan, Yuwei Razzak, Imran Kit, Chunyu Zhang, Wenjie Hoex, Bram GreenDynamics Australia University of New South Wales School of Photovoltaic and Renewable Energy Engineering Australia University of North Texas Dept. of Computer Science and Engineering Denton United States University of New South Wales School of Computer Science and Engineering Australia City University of Hong Kong Dept. of Linguistics and Translation Hong Kong Hong Kong

ISBN: (纸本)9798331530631

natural language processing (NLP) is widely used to supply summarization ability from long context to structured information. However, extracting structured knowledge from scientific text by NLP models remains a challenge because of its domain-specific nature to complex data preprocessing and the granularity of multi-layered device-level information. To address this, we introduce ByteScience, a non-profit cloud-based auto finetuned Large language Model (LLM) platform, which is designed to extract structured scientific data and synthesize new scientific knowledge from vast scientific corpora. The platform capitalizes on DARWIN, an open-source, fine-tuned LLM dedicated to natural science. The platform was built on Amazon Web Services (AWS) and provides an automated, user-friendly workflow for custom model development and data extraction. The platform achieves remarkable accuracy with only a small amount of well-annotated articles. This innovative tool streamlines the transition from the science literature to structured knowledge and data and benefits the advancements in natural informatics. Demo Video. © 2024 IEEE.

关键词： Cloud platforms

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Automatic Domain-specific Corpora Generation from Wikipedia - A Replication Study 31

Automatic Domain-specific Corpora Generation from Wikipedia ...

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31st IEEE international Requirements engineering conference (RE)

作者： Ruwanpura, Seniru Morash, Cale Khan, Momin Ali Ahmad, Adnan Ginde, Gouri Univ Calgary Dept Elect & Software Engn Calgary AB Canada

ISBN: (纸本)9798350326918

Replication studies help mature our knowledge and attempt to validate the findings of a prior piece of research. However, these studies are still rare in the Requirements engineering field. Additionally, the rapidly advancing realm of natural language processing (NLP) is creating new opportunities for efficient, machine-assisted workflows application which can bring new perspectives and results to the forefront. Thus, in this paper, we replicate and extend a previous study (baseline), a tool, VVikiDoMiner, which automatically generated domain-specific corpora by crawling Wikipedia. In this study, we investigated and executed the implementation of WikiDoMiner (open-sourced code from the original paper) to recreate the results. This allowed us to strengthen the external validity of the original study. We extended the baseline to evaluate additional data sets and generated nuanced results using state-of-the-art NLP techniques such as Bidirectional Encoder Representations from Transformers (BERT). Results showed that due to the growing content in Wikipedia, the corpus generated for the Railways and Networks domains did not precisely match the results from the baseline. However, utilizing the state-of-the-art KeyBERT library from the Huggingface AI community enhanced the results, eventually generating a meaningful corpus compared to the baseline.

关键词： Replication study Requirements analysis Requirements engineering natural language processing BERT

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An Aviation Manufacturing Process knowledge Question-Answering System Based on knowledge Graph 3rd

An Aviation Manufacturing Process Knowledge Question-Answeri...

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3rd international conference on Smart Manufacturing, Industrial and Logistics engineering, SMILE 2023 and the 7th Asian conference of Management Science and Applications, ACMSA 2023

作者： Qin, Linhao Zhang, Hongpeng Ming, Zhenjun Lv, Ruiqiang Du, Tingting Lu, Hu Li, Qiang School of Mechanical Engineering Beijing Institute of Technology Beijing China Institute for Digital Manufacturing and Assembly AVIC Manufacturing Technology Institute Beijing China Manufacturing Engineering Center COMAC Shanghai Aircraft Manufacturing Co. Ltd. Shanghai China

ISBN: (纸本)9789819701933

This paper introduces a question-answering system based on a knowledge graph of the aviation manufacturing process. By collecting and extracting knowledge in the fields of machining, assembly, forming, materials, and other relevant aspects of aviation manufacturing technology, we first construct a comprehensive knowledge graph within the domain. We then leverage natural language processing technologies to address natural language queries. This encompasses named entity recognition utilizing the Aho-Corasick algorithm and Levenshtein Distance, rule-based intention recognition, query template matching and instantiation, among other techniques. Subsequently, answers are retrieved from the established knowledge graph. The test results demonstrate a precision rate of knowledge retrieval at 94.86%, enabling fast and accurate responses to the majority of questions within this domain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： knowledge graph

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A Vision-language Model Based on Prompt Learner for Few-shot Medical Images Diagnosis 27

A Vision-language Model Based on Prompt Learner for Few-shot...

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27th international conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Chang, Tianyou Chen, Shizhan Fan, Guodong Feng, Zhiyong Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China

ISBN: (纸本)9798350349184;9798350349191

In the real world, it can be challenging to annotate a large-scale dataset for all medical images, making few-shot medical image classification an important task. The latest advancements in pre-trained vision-language models as CLIP have demonstrated excellent performance in zero-shot natural image recognition and show advantages in medical applications. However, we have found that deploying such models in practical applications faces challenges in terms of engineering effort. It requires specialized medical domain knowledge and is time-consuming, as even slight variations in wording can have a significant impact on performance. Inspired by recent research on prompt learning in the field of natural language processing (NLP), we propose a simple approach called Prompt Learner (PoLe) for automating the design of prompts in pre-trained vision-language models. This is a simple method specifically designed to fine-tune vision-language models, similar to CLIP, for downstream image recognition. In particular, PoLe models context tokens using continuous vectors that can automatically learn from medical images, thus avoiding the tedious process of handcrafting prompt engineering. Additionally, this approach maintains the frozen state of the large-scale pre-trained parameters, saving computational resources. Through extensive experiments on 5 medical image datasets, we have demonstrated that PoLe surpasses manually designed prompts with just one or two shots, and further training with more shots significantly improves the performance of image classification. For instance, when trained with 16 shots, the average improvement is approximately 20% (with a maximum improvement of over 31%). PoLe effectively transforms CLIP into a powerful few-shot learner. In terms of recognition performance, adjusting the CLIP model using PoLe yields better results than manually designed prompts for CLIP. When enhancing CLIP, PoLe demonstrates stronger learning capabilities compared to other few-s

关键词： Medical images CLIP Learnable prompt Vision-language model Chatgpt

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A Comprehensive Study on Advancements in Text Mining and natural language processing 2

A Comprehensive Study on Advancements in Text Mining and Nat...

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2nd IEEE international conference on Emerging Research in Computational Science, ICERCS 2024

作者： Mahire, Sarthak Balaji Saravanan, U.K. Murali, M. Prabakaran, N. Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India Sona College of Technology Department of Information Technology Salem India

ISBN: (纸本)9798331534950

Text mining and natural language processing (NLP) have witnessed significant advancements in recent years, driven by the increasing availability of unstructured data and the development of sophisticated machine learning models. This review explores the evolution of text mining and NLP, highlighting the transition from rule-based systems to modern deep learning approaches. This paper reviews various techniques and its impact across various domains. Challenges such as data bias and model interpretability still remain. The paper also discusses future directions, emphasizing the need for fair, interpretable and sustainable NLP systems. This paper aims to provide insights into the current state of text mining and NLP, the challenges faced and potential pathways for future research. © 2024 IEEE.

关键词： Adversarial machine learning

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An Interactive Approach for Query-based Multi-Document Scientific Text Summarization 14

An Interactive Approach for Query-based Multi-Document Scien...

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14th international conference on Computer and knowledge engineering, ICCKE 2024

作者： Nejati, Mohammadsadra Ahmadi, Abbas Mohebi, Azadeh Dept. of Industrial Engineering Amirkabir University of Technology Tehran Iran Tehran Iran

ISBN: (纸本)9798331511272

The overwhelming volume of scientific documents necessitates automatic summarization to assist researchers in efficiently finding relevant data, making informed decisions, and retrieving appropriate answers to their queries. Query-based automatic summarization is to provide a textual summary related to user’s initial query. Most of the previous approaches in query-based summarization, only consider the user’s initial query to model user’s information need, and rarely rely on additional interaction between user and system. This study proposes an interactive multi-document summarization system designed to assist researchers in processing scientific documents. The interactive approach is necessary to refine document selection and improve summary relevance through user feedback. The system utilizes advanced natural language processing techniques to retrieve and summarize relevant content, including query-based summarization to tailor summaries to specific user queries. By incorporating user-generated questions, the system enhances the coherence and pertinence of summaries. The system is tested and evaluated for Persian language. Despite the lack of labeled datasets in the Persian, the proposed approach demonstrates effective summarization through unsupervised methods. User evaluations confirm the system’s potential to significantly improve information retrieval and comprehension in scientific research. © 2024 IEEE.

关键词： Structured Query language

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GAEL: A Global-Aware Entity Linking Model Based on Retriever-Reader Paradigm 7

GAEL: A Global-Aware Entity Linking Model Based on Retriever...

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7th international conference on Machine Learning and natural language processing, MLNLP 2024

作者： Wang, Linjie Hao, Shiyi Lu, Yuxing Wang, Chen Liu, Yunfei Li, Shengyang Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China Key Laboratory of Space Utilization Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Tencent AI Lab Shenzhen China

ISBN: (纸本)9798350354973

The field of space science and applications, rich in domain knowledge, has witnessed research on domain knowledge extraction and the preliminary construction of domain knowledge graphs. Entity linking serves as a fundamental component for intelligent applications of knowledge graphs. Recently, the retriever-reader paradigm, inspired by question-answering tasks, has advanced the development of entity linking. However, the existing studies overlook the relationships between entities, resulting in the lack of global consistency in the predictions. Furthermore, they struggle to deal with redundant entities effectively. We propose a Global-Aware Entity Linking Model (GAEL) to address these issues. GAEL employs incremental training during model training, integrating linking results in successive rounds. By incorporating the entity information predicted in previous rounds into new training inputs, GAEL continuously optimizes semantic information and strengthens interactions between candidate entities, which helps to identify potential entities corresponding to mentions. Besides, GAEL utilizes a novel entity screening mechanism that encodes candidate entities and input text separately and ranks them based on semantic similarity, effectively resolving the issue of multiple entities corresponding to the same mention. Experimental results demonstrate that GAEL significantly improves entity linking performance on datasets in the field of space science and applications, as well as on multiple public datasets. © 2024 IEEE.

关键词： knowledge graph

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Optimizing High-Dimensional Text Embeddings in Emotion Identification: A Sliding Window Approach 16

Optimizing High-Dimensional Text Embeddings in Emotion Ident...

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16th international conference on knowledge Discovery and Information Retrieval, KDIR 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Uymaz, Hande Aka Metin, Senem Kumova Izmir University of Economics Department of Software Engineering Izmir Turkey

ISBN: (纸本)9789897587160

natural language processing (NLP) is an interdisciplinary field that enables machines to understand and generate human language. One of the crucial steps in several NLP tasks, such as emotion and sentiment analysis, text similarity, summarization, and classification, is transforming textual data sources into numerical form, a process called vectorization. This process can be grouped into traditional, semantic, and contextual vectorization methods. Despite their advantages, these high-dimensional vectors pose memory and computational challenges. To address these issues, we employed a sliding window technique to partition high-dimensional vectors, aiming not only to enhance computational efficiency but also to detect emotional information within specific vector dimensions. Our experiments utilized emotion lexicon words and emotionally labeled sentences in both English and Turkish. By systematically analyzing the vectors, we identified consistent patterns with emotional clues. Our findings suggest that focusing on specific sub-vectors rather than entire high-dimensional BERT vectors can capture emotional information effectively, without performance loss. With this approach, we examined an increase in pairwise cosine similarity scores within emotion categories when using only sub-vectors. The results highlight the potential of the use of sub-vector techniques, offering insights into the nuanced integration of emotions in language and the applicability of these methods across different languages. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Vectors

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Advanced Techniques for Chinese Image Captioning: Investigating Attention Mechanisms Based on Object Detection for Chinese Image Caption Generation 5

Advanced Techniques for Chinese Image Captioning: Investigat...

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5th international conference on Machine Learning and Computer Application, ICMLCA 2024

作者： Hua, Yongbin Li, Pei'ang Zeng, Xiangjin Xu, Hong School of Computer Science and Engineering Wuhan Institute of Technology Hubei Wuhan China

ISBN: (纸本)9798331530334

The task of image caption generation aims to automatically produce natural language descriptions that match the content of images, integrating the fields of machine vision and natural language processing, which holds significant theoretical and practical value. Inspired by top-down attention mechanisms, this paper proposes an innovative attention model. Utilizing the output of pretrained object detection networks as prior knowledge for images, the model guides the generation of natural language descriptions. By directly incorporating the results of object detection as attention inputs into the text generation network, the model effectively focuses on key descriptive regions of images, thereby significantly enhancing performance. On public Chinese image captioning datasets, this model demonstrates substantial advantages in metrics such as BLEU-4 and METEOR. © 2024 IEEE.

关键词： Object recognition

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KGLM-QA: A Novel Approach for knowledge Graph-Enhanced Large language Models for Question Answering 15

KGLM-QA: A Novel Approach for Knowledge Graph-Enhanced Large...

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15th international conference on Information and knowledge Technology, IKT 2024

作者： Safaei, Alireza Akhavan Saboori, Pegah Ramezani, Reza Nematbakhsh, Mohammadali Department of Software Engineering University of Isfahan Isfahan Iran

ISBN: (纸本)9798331522254

Large language models excel in various natural language processing tasks but often struggle with knowledge-intensive queries, particularly those involve rare entities or require precise factual information. This paper presents a novel framework that enhances capabilities of an LLM-based question answering system by incorporating structured knowledge from knowledge graphs. Our approach employs entity extraction, semantic similarity scoring, and adaptive graph exploration to efficiently navigate and extract relevant information from knowledge graphs. The core of the presented solution is a knowledge graph-enhanced language model process that iteratively refines subgraph exploration and answer generation, complemented by a fallback mechanism for robustness across diverse question types. Experiments on location-based questions from the Entity Questions dataset demonstrate significant improvements in the quality of responses. Using the Gemini 1.5 Flash model, our system achieved an accuracy increase from 36% to 71% for partially correct answers and from 22% to 69% for exactly correct answers, as evaluated by human assessors. This approach offers a promising direction for developing more reliable and accurate question answering systems, particularly for queries involving long-tail entities or specific factual knowledge. © 2024 IEEE.

关键词： knowledge graph

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