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Facilitating Message Passing with Potential Links for knowledge Graph Completion

Facilitating Message Passing with Potential Links for Knowle...

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

作者： Hong Yin Jiang Zhong Rongzhen Li Jiaqi Wang Chen Wang Dai Qizhu Xue Li College of Computer Science Chongqing University China Information Technology and Electrical Engineering The University of Queensland Australia

knowledge graph completion (KGC) aims at inferring missing links between two entities. Most previous models focus on learning representations for entities and relations via graph neural networks. In this formalism, representations heavily rely on structural information. However, it is common for knowledge graphs (KGs) to be unconnected due to their inherent incompleteness, resulting in a significant loss of vital structural information. To overcome this issue, this paper proposes to increase the connectedness of KGs to facilitate message passing between entities. Specifically, we first augment KGs with a series of auxiliary triplets derived from outer nodes. Subsequently, a dynamically weighted graph convolutional layer is employed to unequally aggregate the representations of neighboring entities and dynamically combine this aggregated information with information from themselves. Finally, ConvE is utilized to calculate scores for all triplets. Extensive experiments on two benchmark datasets demonstrate the superiority of our method compared to strong baseline models.

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Augmenting Content Retrieval Using NLP in AIML

Augmenting Content Retrieval Using NLP in AIML

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Advancement in Electronics & Communication engineering (AECE), international conference on

作者： Kapil Shrivastava Angeles Quezada Karuna Sharma Bogart Yail Marquez Lobato Nitish Vashishth Department of Computer Engineering & Applications GLA University Mathura UP India Tecnológico Nacional de México Campus Tijuana Baja California México Computer Science & Engineering SDGI Global University Ghaziabad UP India Computer Science & Engineering SDGI Global University Ghaziabad UP India Electronics & Communication Engineering Raj Kumar Goel Institute of Technology Ghaziabad UP India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

The paper Augmenting Content Retrieval Using NLP in AIML describes using natural language processing (NLP) with Artificial Intelligence Markup language (AIML) to enhance content retrieval. Among Markup languages, AIML is most commonly used in chatbots or conversational UI. On the other hand, NLP is a subfield of artificial intelligence that handles reading and understanding human language. Their abstract underscores the importance of improved content extraction for AIML systems that rely on a keyword-based approach, which wouldn't work very well as it fails to capture all elements in human language. Here, wide-scale use of the NLP techniques provides a breakthrough in the analysis. It allows understanding the meaning inherent in natural language and interpreting semanticity through context-sensitive, syntactic rule-based arrangement. The paper proposes a mix of NLP and AIML for better retrieval. Approach: In this technique, we first preprocess the user input using NLP techniques, i.e., POS tagging & Named Entity Recognition, to extract context-specific Keywords/Concepts. Then, these keywords and concepts are mapped with answers in the AIML-based system. The abstract also presents the experimental evaluation of the proposed strategy, which significantly outperforms conventional keyword-based retrieval for content. This proves that applying NLP in AIML systems can even increase the capability to understand and recover user inputs.

关键词： Text mining Markup languages Semantics Named entity recognition Machine learning knowledge representation Tagging Syntactics Chatbots natural language processing

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A Machine Learning Method for Detecting Sarcasm in Negative Sentiment Tweets using Hyperboles

A Machine Learning Method for Detecting Sarcasm in Negative ...

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2022 ieee North Karnataka Subsection Flagship international conference, NKCon 2022

作者： Preetha, V. Sathishkumar, P. Janani, M. Manohari, B. Ida, S. Jhansi Suganyadevi, S. Sri Ramakrishna Institute of Technology Department of CSE Coimbatore India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Department of CSE Chennai India KPR Institute of Engineering and Technology Department of CSE Coimbatore India Sri Krishna College of Engineering and Technology Department of M.Tech Computer Science and Engineering Coimbatore India R.M.K Engineering Colleg Department of Computer Science and Business Systems Kavaraipettai India KPR Institute of Engineering and Technology Department of ECE Coimbatore India

The use of sarcasm as an expression is a common literary device that involves someone purposefully expressing the opposite of what is being meant. The accurate identification of sarcasm in a text may boost other natural language processing activities, particularly ones involving sentiment analysis, and make it easier to comprehend the genuine intents of the speakers in the text. It is difficult to determine if someone is being sarcastic only based on their written words since sarcasm is a kind of implicitly expressing one's feelings and sarcastic speakers purposefully confound their audiences. Existing methods based on machine learning and deep learning obtained an insufficient level of performance when processing sarcastic material that included complicated expressions or required a certain set of prior knowledge to comprehend. The purpose of this research is to examine how sarcasm may be detected in tweets that have a negative mood and include hyperboles. For the purpose of sarcasm identification, the hashtags #Chinesevirus, #Kungflu, #COVID19, #Hantavirus, and #Coronavirus were included in the collection of 6,600 pre-processed negative sentiment tweets. The three well-known machine learning algorithms known as Support Vector Machine, Random Forest, and Random Forest with Bagging were utilised in order to conduct an analysis of the five hyperbole characteristics known as interjection, intensifier, capital letter, punctuation mark, and elongated word. The suggested model with the extended term attained an accuracy of 78.74% and an F-score of 71%, respectively, despite the inclusion of hyperbolic words in the tweets included in an unbiased dataset. It was discovered that intensifier was the exaggeration with the highest level of significance (p. 0001). The results of the experiments and analyses carried out for this study led the researchers to the conclusion that hyperboles may be found in unbiased datasets, which helps improve sarcasm identification as well. © 202

关键词： Sentiment analysis

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Code Debugging with LLM-Generated Explanations of Programming Error Messages

Code Debugging with LLM-Generated Explanations of Programmin...

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international conference on engineering Education (ICEED)

作者： John S. Y. Lee Fengkai Liu Tianyuan Cai Department of Linguistics and Translation City University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798350367416

ISBN: (纸本)9798350367423

Programming is an essential part of the curriculum for electrical, computer and software engineering students. Since one inevitably makes coding mistakes, it is important for programmers to develop debugging skills. However, it could be challenging for beginners to repair a non-compiling program, since programming error messages tend to be opaque, and might not directly address the error. This paper investigates the use of Large language Models to generate plain, novice-friendly explanations of programming error messages. In an introductory course on natural language processing, We evaluate the extent to which these explanations help students debug Python code with six common programming error categories. Experimental results suggest that explanations generated with zero-shot GPT-4 are effective in raising the code revision success rate.

关键词： Codes Large language models Debugging Maintenance engineering Encoding Logic engineering education Programming profession Software engineering Python

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A Method for Constructing engineering-Biology Domain knowledge Network Oriented Toward Collective Intelligence Innovation Design

A Method for Constructing Engineering-Biology Domain Knowled...

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Automation in Manufacturing, Transportation and Logistics (ICaMaL), international conference on

作者： Haibo Liu Lin Gong Xianpeng Zhang Xuqiang Chang Hanhun Li Weipeng Jiang School of Mechanical Engineering Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

In the context of the era of information, the growth of personalized user needs and the development of Internet plus technology have brought new challenges and opportunities to product innovation design. As an emerging design mode, collective intelligence design can acquire design knowledge through group wisdom and extensive range to improve the market adaptability of products and the innovation ability of enterprises. As a kind of long-distance knowledge, biology domain knowledge can effectively play the incentive role of cross-domain knowledge in the collective intelligence design process. However, there is currently no effective way of combining engineering with biology domain knowledge to underpin the design process. To address this issue, we propose a method for constructing an engineering-biology domain knowledge network for collective intelligence innovation design: a cross-domain knowledge representation model is constructed to achieve unified representation of engineering and biology domain knowledge; By using cross-domain entity extraction and relationship generation methods, an engineering-biology domain knowledge network is automatically constructed. This method provides a new perspective for cross-domain knowledge motivation and inspiration in collective intelligence innovation design. Through the representation and relationship generation of cross-domain knowledge, our engineering-biology domain knowledge network can provide designers with long-distance design knowledge incentives, improving the quality and innovation of generated solutions.

关键词： knowledge engineering Technological innovation Biological system modeling Transportation Prototypes Collective intelligence Biology natural language processing Manufacturing Proposals

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Exploring the Application of natural language processing for Social Media Sentiment Analysis

Exploring the Application of Natural Language Processing for...

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Innovation in Technology (INOCON), ieee international conference for

作者： Vishakha Joseph Chandra Prakash Lora Narmadha T Department of Management ARKA JAIN University Jamshedpur Jharkhand India Department of Chemistry Vivekananda Global University Jaipur India Department of Computer Science Engineering School of Engineering and Technology JAIN Bangalore Karnataka India

natural language processing (NLP) is a developing vicinity of studies that has the potential to provide state-of-the-art sentiment analysis abilities inside the realm of social media. Its software involves the collection of facts from social networks, which include Twitter or FB, after reworking these records into an established format amenable to the NLP techniques. The last aim is to make automated predictions of sentiment from social media posts, which can be used to aid the choice-making methods of customers, marketers, researchers, and many others. As the quantity of data created by social media users continues to grow, so does the complexity of appropriately extracting sentiment from these facts. NLP allows for efficient and automatic approaches to extracting insights from social media conversations. However, the venture lies in finding approaches to extract sentiment from the sizeable amount of textual content efficaciously. It evaluates and discusses the modern techniques and equipment that can be used to apply NLP techniques to social media sentiment analysis.

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Application Study of Area-Based and YOLOv4 Smoking Behavior Detection 4

Application Study of Area-Based and YOLOv4 Smoking Behavior ...

引用

4th international conference on natural language processing, ICNLP 2022

作者： Bai, Chenbing Zhou, You Guo, Qingkai Xi'an University of Posts and Telecommunications School of Automation Xi'an China Tian'jin Branch of the 3rd Construction Co. Ltd of China Construction 5th Engineering Bureau Tianjin China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

Due to the cigarette targets are not obvious, quality of monitoring picture is not clear and other factors lead to slow detection and feature extraction difficulties for the actual monitoring process. This paper proposes a target detection scheme of cigarettes with using the fusion of Yolov4 target detection algorithm and feature extraction of the human body region algorithm based on HOG. This paper uses the Yolov4 target detection algorithm as the main framework, intending to shorten the time of cigarette detection and ensure the real-time monitoring process. To solve the problem of difficult feature extraction for cigarette targets detection and effectively reduce the CPU usage, it makes a preliminary check of the human area for the presence of smoke while adding the hog based human area extraction algorithm to the algorithm before the cigarette targets detection. Experiments show that the method can effectively solve the problem of cigarette targets detection in public places. Compared with the original faster region convolution algorithm, the detecting time of a single image slightly increases but still meets the requirements of practical engineering applications. On the other hand, the rate of detecting incorrectly is reduced by about 2% and the detection accuracy is significantly improved. © 2022 ieee.

关键词： Extraction

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Abstractive Text Summarization: A Transformer Based Approach

Abstractive Text Summarization: A Transformer Based Approach

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international conference for Convergence of Technology (I2CT)

作者： Anushka R Kale Pratiksha R Deshmukh Department of Computer Engineering COEP Technological University Pune India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

This research delves into the difficulty of summarizing legal documents using natural language processing. It examines how cutting-edge models like XLNet and BART can be used for abstractive summarization specifically tailored for lengthy legal cases. The study assesses these models’ abilities to condense complex legal texts, highlighting the constraints imposed by input token limits. Through a thorough comparison of XLNet and BART based on legal-specific standards, the research introduces a fresh approach to improve summarization by leveraging these models’ strengths while addressing their limitations. Evaluation methods include ROUGE scores. This study advances our understanding of abstractive summarization, particularly in the realm of legal texts, offering valuable insights for both legal professionals and NLP researchers.

关键词： Law Noise reduction Coherence Transformers natural language processing Task analysis Standards

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Hanging Around: Cognitive Inspired Reasoning for Reactive Robotics 14

Hanging Around: Cognitive Inspired Reasoning for Reactive Ro...

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14th international conference on Formal Ontology in Information Systems, FOIS 2024

作者： Pomarlan, Mihai de Giorgis, Stefano Ringe, Rachel Hedblom, Maria M. Tsiogkas, Nikolaos Applied Linguistics Department University of Bremen Bremen Germany Institute of Cognitive Sciences and Technologies National Research Council Catania Italy Digital Media Lab University of Bremen Bremen Germany Department of Computing Jönköping School of Engineering Jönköping Sweden Department of Computer Science KU Leuven Leuven Belgium

ISBN: (纸本)9781643685618

Situationally-aware artificial agents operating with competence in natural environments face several challenges: spatial awareness, object affordance detection, dynamic changes and unpredictability. A critical challenge is the agent’s ability to identify and monitor environmental elements pertinent to its objectives. Our research introduces a neurosymbolic modular architecture for reactive robotics. Our system combines a neural component performing object recognition over the environment and image processing techniques such as optical flow, with symbolic representation and reasoning. The reasoning system is grounded in the embodied cognition paradigm, via integrating image schematic knowledge in an ontological structure. The ontology is operatively used to create queries for the perception system, decide on actions, and infer entities’ capabilities derived from perceptual data. The combination of reasoning and image processing allows the agent to focus its perception for normal operation as well as discover new concepts for parts of objects involved in particular interactions. The discovered concepts allow the robot to autonomously acquire training data and adjust its subsymbolic perception to recognize the parts, as well as making planning for more complex tasks feasible by focusing search on those relevant object parts. We demonstrate our approach in a simulated world, in which an agent learns to recognize parts of objects involved in support relations. While the agent has no concept of handle initially, by observing examples of supported objects hanging from a hook it learns to recognize the parts involved in establishing support and becomes able to plan the establishment/destruction of the support relation. This underscores the agent’s capability to expand its knowledge through observation in a systematic way, and illustrates the potential of combining deep reasoning with reactive robotics in dynamic settings. © 2024 The Authors.

关键词： Optical flows

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Job Applications Selection and Identification: Study of Resumes with natural language processing and Machine Learning

Job Applications Selection and Identification: Study of Resu...

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ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Amit Pimpalkar Aastha Lalwani Roshan Chaudhari Mohd. Inshall Mahak Dalwani Tarandeep Saluja Dept. of Computer Science Engineering JIT Nagpur India

For each job ad, the organization received a high number of applicants. Finding the right person's application from various applications is a time-consuming task for any company these days. The categorization of a representative's resume is a time-consuming, labor-intensive, and resource-intensive procedure. natural language processing and advanced analytics can understand and analyze unstructured written material to extract the required information. The idea is to teach the machine to study text in the same manner that people do. By reviewing applications with natural language processing and machine-learning processing, we may be able to locate people with the abilities and attributes we need more quickly. This study provides a comprehensive assessment of resume selection and identifies the existing work gaps. Different algorithms for machine learning and methodologies for analyzing and interpreting unstructured data have been researched. This study also addresses the research problems and future potential of resume analysis regarding the writing style, word choice, and grammar of unstructured written communication.

关键词： Machine learning algorithms Semantic search Resumes Machine learning Syntactics natural language processing Software

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