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Unstructured Transportation Safety Board Findings Categorization Using the knowledge Graph Pipeline

Unstructured Transportation Safety Board Findings Categoriza...

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2023 IEEE international conference on Big Data, BigData 2023

作者： Panday, Ritesh Lung, Chung-Horng Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350324457

In this study, the Transportation Safety Board's (TSB) Findings data was analyzed to assist Transport Canada Civil Aviation (TCCA) in better informing safety policy decision-making. As the TSB Findings data was unstructured, various methods to categorize and analyze unstructured data were explored in the existing literature. It was found that knowledge Graphs (KGs), in combination with Deep Learning and natural language processing (NLP) models, such as Neuralcoref and REBEL, were versatile and adaptable to different data needs, which could provide insights into the analysis of the TSB data. This paper first emulated and validated the KG pipeline using the BBC News dataset and then applied the KG pipeline technique to the unstructured TSB Findings Reports data consisting of 4,121 rows, each containing text for an incident or accident. The results showed that the model detected an average of 1.03 entities per row of the data and a total of 5,484 relationships or 1.33 relationships per row. Further, the top-four relationships in the graph database structure obtained from Neo4j accounted for 50% of all relations, though not all relations were found to be valuable. However, a few less-frequent relations were also found to be valuable due to their ability to capture critical components of aviation safety. The results of this data pipeline can be used for further analysis and categorization of TSB's Findings data to improve aviation safety. © 2023 IEEE.

关键词： Data Pipeline Deep Learning Graph Database knowledge Graph Machine Learning natural language processing

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natural language processing (NLP) based Extraction of Tacit knowledge from Written Communication during Software Development 4

Natural Language Processing (NLP) based Extraction of Tacit ...

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4th international conference on Advancements in Computational Sciences, ICACS 2023

作者： Noor, Maham Rana, Zeeshan Ali University of Central Punjab Faculty of Information and Technology Lahore Pakistan FAST School of Computing FAST-NUCES Lahore Pakistan

ISBN: (纸本)9781665463775

Software engineering, in general, and Global Software engineering (GSE), in particular, face challenges such as handling communication and collaboration issues and inappropriate knowledge management. It is difficult to ensure availability of right knowledge at the right time to the right person during software engineering activity. The knowledge needs to be shared across the organization but limitations of knowledge sharing tools or dispersed knowledge sharing media and improper handling of tacit knowledge makes it more challenging to share knowledge. Significant amount of communication during software engineering process takes place via emails and discussion forums. Decisions taken during planning activities are also communicated through emails. These emails and the discussions do not necessarily cover the rationale behind a decision or approach adopted. This leaves a substantial portion of shared knowledge as tacit. Mostly, due to undefined tacit knowledge handling procedures, organizations suffer with the loss of critical knowledge and information. One way to handle this loss of knowledge is to store every information in knowledge repository but this can increase the size of the knowledge repository. This paper presents a three step approach to identify tacit knowledge in the written communication such that the organizations can save the tacit knowledge for future use. The presented approach does not only extract up to 57% of tacit knowledge but also indicates that only 20% of the whole communication need to be externalized hence saving 80% capacity of the knowledge repository, if every communication was to be preserved as potential tacit knowledge. © 2023 IEEE.

关键词： knowledge management

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A study of Chinese Text Classification based on a new type of BERT pre-training 5

A study of Chinese Text Classification based on a new type o...

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5th international conference on natural language processing, ICNLP 2023

作者： Liu, Youyao Huang, Haimei Gao, Jialei Gai, Shihao Xi'an University of Posts and Telecommunication School of Electronic Engineering Xi'an China

ISBN: (纸本)9798350302219

Chinese Text Classification (TC) is the process of mapping text to a pre-given topics category. In recent years, it has been found that TC is mainly based on RNN and BERT, so the development of different novel pre-training applied to Chinese TC is described as based on BERT pre-training. BERT combined with convolutional neural network is proposed to extend the BERT-CNN model for the problem of lack of semantic knowledge of BERT to derive a good classification effect. The second RoBERTa model performs feature extraction and fusion to obtain the feature word vector as the text output vector, which can solve the problem of insufficient BERT extracted features. The BERT-BiGRU model, on the other hand, mainly avoids the increase in the number of texts leading to long training time and overfitting, and uses a simpler GRU bi-word network as the main network to fully extract the contextual information of Chinese texts and finally complete the classification of Chinese texts;at the same time, it makes an outlook and conclusion on the new pre-training model for Chinese TC. © 2023 IEEE.

关键词： Classification (of information)

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Research On Machine Analogy Translation Technology Based On Machine Learning 2

Research On Machine Analogy Translation Technology Based On ...

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2nd international conference on Digital Society and Intelligent Systems, DSInS 2022

作者： Zhuang, Siyu Cai, Yingyi South China Normal University No. 55 West of Yat-sen Avenue Guangdong Province China

ISBN: (纸本)9781510663244

With the acceleration of economic globalization, the importance of translation services has gradually manifested. The machine translation technology has a long and glorious history from statistical machine translation to neural machine translation, At present, machine translation software is emerging, mostly following the rules of natural language processing. In order to achieve quality and efficiency in translation, the various ambiguities in the language should be processed accurately and the speed of translation should be increased, while the anthropomorphism of machine translation should be enhanced and the machine's knowledge of human language should be constantly improved. Therefore, it is important to strengthen the research of machine learning technology, and to insist on the research on machine analogy translation technology based on machine learning is an important research topic. © 2023 SPIE.

关键词： Computer aided language translation

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Harvesting knowledge: An Innovative Ontology Framework for Agricultural Advancement using NPL methodologies 3

Harvesting Knowledge: An Innovative Ontology Framework for A...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Deepa, R. Das, Manisha Thilakavathy, P. Bennet Prabhu, A. Surendran, R. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Andhrapradesh Rajam India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Kone Elevator India Technology & Engineering Centre Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

This paper introduces Agricultural Advancement using NLP (AA-NLP) techniques as a strategy to overcome the limitations of traditional agricultural management information systems. There is a lot of unstructured data available but this situation makes agricultural research inefficient because of its knowledge extraction and usage failure. This problem is addressed by combining an ontology framework developed for agricultural information management with natural language processing (NLP) methods. The solution, AA-NLP, utilizes modern NLP algorithms which extract, classify and evaluate agricultural information from many textual sources that include research papers, reports and online repositories. AA-NLP achieves effective information retrieval and exploitation through named entity identification, sentiment analysis and semantic similarity as well as other techniques to convert unstructured input into structured ontological representations. Applying AA-NLP has several benefits such as increased discoverability of relevant agricultural information, automatic classification of scientific findings, and generation of actionable insights for decision making in farming practices, crop management and formulation of agricultural policies. The effectiveness of AA-NLP was analyzed in this paper by measuring the precision associated with knowledge extraction and ontology creation through various performance metrics including accuracy, sustainability farming practices, agricultural productivity and precision. © 2024 IEEE.

关键词： Semantics

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Classification of Questionnaires with Open-Ended Questions 15

Classification of Questionnaires with Open-Ended Questions

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15th international conference on knowledge Discovery and Information Retrieval, KDIR 2023 as part of the 15th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2023

作者： Tuğcu, Miraç Çekiç, Tolga Erdinç, Begüm Çıtamak Akay, Seher Can Deniz, Onur Natural Language Processing Department Yapı Kredi Teknoloji Istanbul Turkey

ISBN: (纸本)9789897586712

Questionnaires with open-ended questions are used across industries to collect insights from respondents. The answers to these questions may lead to labelling errors because of the complex questions. However, to handle this noise in the data, manual labour might not be feasible due to low-resource scenarios. Here, we propose an end-to-end solution to handle questionnaire-style data as a text classification problem. In order to mitigate labelling errors, we use a data-centric approach to group inconsistent examples from the banking customer questionnaire dataset in Turkish. For the model architecture, BiLSTM is preferred to capture long-term dependencies between contextualized word embeddings of BERT. We achieved significant results on the binary questionnaire classification task. We obtained results up to 81.9% recall and 79.8% F1 score with the clustering method to clean the dataset and presented the results of how it impacts overall model performance on both the original and clean versions of the data. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Classification (of information)

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Multi feature fusion paper classification model based on attention mechanism 5

Multi feature fusion paper classification model based on att...

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5th international conference on natural language processing, ICNLP 2023

作者： Fan, Chunxiao Li, Yongchun Wu, Yuexin Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing China

ISBN: (纸本)9798350302219

In recent years, the number of published scientific research papers has shown a growing trend. How to classify scientific research papers efficiently and accurately is a very important issue. However, excellent paper classification system platforms at home and abroad, such as China National knowledge Infrastructure, Microsoft Academic Network, etc., rely heavily on the structured or semi-structured text in papers for classification, and do not interpret the unstructured text data in papers enough. To solve this problem, we proposed a multi-feature fusion paper classification model based on attention mechanism (AttentionMFF), which uses the fusion features of structured and unstructured text data in papers to improve classification performance. First, Attention MFF extracts the features of different texts in papers by a BERT layer, then uses attention mechanism to fuse different features, and finally get category through the linear layer. Experiments on the arXiv paper dataset show that the Attention MFF has higher F1-Score than TextCNN model and BERT model that only uses the feature of abstract. © 2023 IEEE.

关键词： Classification (of information)

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Confront Insider Threat: Precise Anomaly Detection in Behavior Logs Based on LLM Fine-Tuning 31

Confront Insider Threat: Precise Anomaly Detection in Behavi...

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31st international conference on Computational Linguistics, COLING 2025

作者： Song, Shuang Zhang, Yifei Gao, Neng Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China Key Laboratory of Cyberspace Security Defense China

ISBN: (纸本)9798891761964

Anomaly-based detection is effective against evolving insider threats but still suffers from low precision. Current data processing can result in information loss, and models often struggle to distinguish between benign anomalies and actual threats. Both issues hinder precise detection. To address these issues, we propose a precise anomaly detection solution for behavior logs based on Large language Model (LLM) fine-tuning. By representing user behavior in natural language, we reduce information loss. We fine-tune the LLM with a user behavior pattern contrastive task for anomaly detection, using a two-stage strategy: first learning general behavior patterns, then refining with user-specific data to improve differentiation between benign anomalies and threats. We also implement a fine-grained threat tracing mechanism to provide behavior-level audit trails. To the best of our knowledge, our solution is the first to apply LLM fine-tuning in insider threat detection, achieving an F1 score of 0.8941 on the CERT v6.2 dataset, surpassing all baselines. © 2025 Association for Computational Linguistics.

关键词： Information leakage

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Native vs Non-native language Prompting: A Comparative Analysis 25th

Native vs Non-native Language Prompting: A Comparative Analy...

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25th international conference on Web Information Systems engineering, WISE 2024

作者： Kmainasi, Mohamed Bayan Khan, Rakif Shahroor, Ali Ezzat Bendou, Boushra Hasanain, Maram Alam, Firoj Qatar University Doha Qatar University of Doha for Science and Technology Doha Qatar Liverpool John Moores University Doha Qatar Carnegie Mellon University in Qatar Ar-Rayyan Qatar Qatar Computing Research Institute Ar-Rayyan Qatar

ISBN: (纸本)9789819605750

Large language models (LLMs) have shown remarkable abilities in different fields, including standard natural language processing (NLP) tasks. To elicit knowledge from LLMs, prompts play a key role, consisting of natural language instructions. Most open and closed source LLMs are trained on available labeled and unlabeled resources-digital content such as text, images, audio, and videos. Hence, these models have better knowledge for high-resourced languages but struggle with low-resourced languages. Since prompts play a crucial role in understanding their capabilities, the language used for prompts remains an important research question. Although there has been significant research in this area, it is still limited, and less has been explored for medium to low-resourced languages. In this study, we investigate different prompting strategies (native vs. non-native) on 11 different NLP tasks associated with 11 different Arabic datasets (8.7K data points). In total, we conducted 198 experiments involving 3 open and closed LLMs (including an Arabic-centric model), and 3 prompting strategies. Our findings suggest that, on average, the non-native prompt performs the best, followed by mixed and native prompts. All prompts will be made available to the community through the LLMeBench (https://***/) framework. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Translation (languages)

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Fine-Tuned Transformer Models for Question Answering 14

Fine-Tuned Transformer Models for Question Answering

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14th international conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Lakkimsetty, Sai Satvikh Latchireddy, Sai Varshini Lakkoju, Sai Manikanta Manukonda, Gopalakrishna Reddy Krishna, R. V. V. Murali Computer Science and Engineering Vishakapatnam India Information Technology Vishakapatnam India

ISBN: (纸本)9798350335095

Effective question-answering is crucial for applications involving natural language processing such as conversational artificial systems. The pre-trained BERT (Bidirectional Encoder Representations from Transformers) model achieved outstanding results on the Stanford Question Answering Dataset (SQuAD), a well-known benchmark for question answering tasks. The three different BERT models (BERT-base, BERT-large, DistilBERT) are pre-trained on general text corpus. These BERT models are trained to identify the right context in a paragraph to give answers. Further, fine-tuning the models with few-shot learning on domain related questions and answers improves the performance. Hence, our work proposes different strategies to improve the performance of BERT models on tasks requiring domain-specific question-answering tasks using a customized SQuAD dataset. Our work highlights the significance of optimizing the BERT models on domain-specific data for enhanced performance in particular tasks. The results of this study have implications for domain-specific knowledge in natural language processing. With a score of 0.9632, the BERT-Large model has the best accuracy out of all the BERT models tested. The performance of the BERT-Large model consistently beats that of the other models in several parameters, including precision, recall, and F1 score. © 2023 IEEE.

关键词： Domain knowledge

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