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检索条件"任意字段=Conference on empirical methods in natural language processing"

共 15258 条记录，以下是4701-4710 订阅

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Creative Destruction: Can language Models Interpret Oxymorons? 12th

Creative Destruction: Can Language Models Interpret Oxymoron...

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12th National CCF conference on natural language processing and Chinese Computing, NLPCC 2023

作者： Xu, Fan Zhu, Ziyun Wan, Xiaojun Wangxuan Institute of Computer Technology Peking University Beijing China

ISBN: (纸本)9783031446924

Oxymoron is a figurative language which combines seemingly contradictory words in a short phrase. It is used to create an impression, enhance a concept or entertain the readers. In this work, we propose a novel task named oxymoron interpretation, which requires destructing the contradiction and creating new senses to make the phrase comprehensible and harmonious with the context. We construct a dataset, Oxymoron with Context-Based Interpretation(OCBI) by gathering data from the internet and manually annotating it. Furthermore, some prevalent models and methods, including definition generation, GPT-2, T5, ChatGPT, LLaMA and Alpaca, are tested to explicitly generate interpretation. According to automatic and manual evaluation results, models except ChatGPT still struggle to generate plain and context-dependent interpretation, which brings us some insights of the capability gap between models. Our work reveals that oxymoron interpretation is a challenging task requiring common sense and contextual association ability. It can be used to assess how well language models comprehend figurative language. The OCBI dataset is publicly available (https://***/pku0xff/oxymoron_interpretation ). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： HTTP

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Exploiting Multi-Decision and Deep Refinement for Ultrasound Image Segmentation 48

Exploiting Multi-Decision and Deep Refinement for Ultrasound...

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48th IEEE International conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Liu, Wenjing Li, Xuanya Hu, Kai Gao, Xieping Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc Beijing100085 China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Changsha410081 China

ISBN: (纸本)9781728163277

In this paper, we propose a novel convolutional neural network (MDR-Net) for ultrasound image segmentation by exploiting multi-decision and deep refinement of the target. Our MDR-Net consists of two main parts, i.e., a multi-decision module (MDM) and a deep refinement module (DRM). Specifically, the MDM effectively addresses the issue of inconspicuous target regions in ultrasound images by combining multi-scale features and multi-receptive field self-attention to enhance the discriminative representation of features and diagnose feature points multiple times. In addition, to alleviate the problem of blurred boundaries and severe speckle noise, the DRM progressively fuses multi-scale features and makes the fused features interact with higher-level features to refine the target details step by step. Finally, we evaluate the proposed method on two publicly available datasets, namely BUSI and UDIAT. We achieve a Dice of 0.8265 and 0.8827 on the two datasets, which are at least 2% and 1.24% higher than other state-of-the-art ultrasound image segmentation methods. © 2023 IEEE.

关键词： Convolutional neural network Deep refinement Multi-decision Ultrasound image segmentation

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natural language processing for Sentiment Analysis in Marketing

Natural Language Processing for Sentiment Analysis in Market...

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International conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES)

作者： Anantha Murthy Pallavi Sagar Deshpande Shweta Gakhreja Yogita Satish Garwal Neetu Jain Rajeswari Katare Department of MCA NMAM Institute of Technology NITTE (Deemed to be University) India College of Engineering Bharati Vidyapeeth (Deemed to be University) Pune Manipal University Jaipur (MUJ) Rajasthan India Institute of Management and Research Bharati Vidyapeeth (deemed to be University) New Delhi India Osmania University

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The use of natural language processing (NLP) has become an indispensable instrument for conducting sentiment analysis in the field of marketing. This technology enables organizations to decipher and analyze the thoughts of customers based on huge amounts of unstructured data. In this study, we investigate the use of natural language processing (NLP) techniques to analyze the feelings conveyed in customer reviews, social media posts, and other forms of digital communication. Through the utilization of advanced natural language processing techniques, such as sentiment brackets, emotion detection, and opinion mining, businesses can acquire a practical understanding of the preferences and stations of their customers. The purpose of this study is to examine several natural language processing (NLP) algorithms and models, including machine literacy-based approaches and deep literacy methods, and to evaluate how effective they are at comprehending delicate feelings. Additionally, it investigates case studies that demonstrate how natural language processing (NLP)-driven sentiment analysis has been successfully implemented to improve marketing strategies, enhance client interaction, and provide valuable insights for product development. Some challenges are similar to these, such as analyzing the surroundings, discovering affronts, and doing multilingual analysis. The findings highlight the revolutionary potential of natural language processing (NLP) in the process of establishing valuable sentiment perceptivity for insights generated by data in marketing.

关键词： Sentiment analysis Sensitivity Social networking (online) Reviews Instruments Force Organizations Games Product development Multilingual

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Scientometric Analysis and Implementation of Word Embedding Techniques in natural language processing

Scientometric Analysis and Implementation of Word Embedding ...

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Technological Advancements in Computational Sciences (ICTACS), International conference on

作者： Vikas Attri Ankush Kapoor Simranjeet Singh Payal Sharma University School of Computing Rayat Bahra University Mohali India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Understanding a language's syntactic, semantic, and expressive components is the goal of natural language processing, also known as NLP. This field of study involves processing a language to comprehend its components. The developments in natural language processing (NLP) have benefited various industries, such as natural machine translation, named entity recognition, sentiment analysis, and chatbots, to name just a few examples. The process of natural language processing (NLP) may be broken down into two primary components: the first is the transformation of the input text (raw data) into a numerical format (vectors or matrices), and the second is the building of models for the processing of numerical data. Both aspects are necessary for understanding the topic. The primary focus of this work is investigating how the discipline of natural language processing (NLP) has evolved from rule-based, statistical representations to learning representations that are more sensitive to context. This study aims to provide an overview of the approaches that allow text representations, with a particular emphasis on embeddings for text of varying lengths and, more significantly, on works that go beyond word embeddings simultaneously. The analysis of individual words is typically more accessible than the analysis of entire text sections because several extra elements come into play. Due to this reason, representations of lengthier pieces of text can be created through various methodologies, which leverage more excellent information compared to what is done for single words. In addition to providing a report on the most recent research trend in this field, the purpose of this study is to provide an overview of the development of word embedding methodology and specific descriptions of typical methods. With the help of this paper, one can acquire a concise guide to comprehending the fundamental concept of word embedding and its progression.

关键词： Sentiment analysis Semantics Production Named entity recognition Syntactics natural language processing Vectors Question answering (information retrieval) Numerical models Testing

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Weakly-Supervised methods for Suicide Risk Assessment: Role of Related Domains 59

Weakly-Supervised Methods for Suicide Risk Assessment: Role ...

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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： Yang, Chenghao Zhang, Yudong Muresan, Smaranda Columbia Univ Dept Comp Sci New York NY 10027 USA Columbia Univ Data Sci Inst New York NY 10027 USA

ISBN: (纸本)9781954085534

Social media has become a valuable resource for the study of suicidal ideation and the assessment of suicide risk. Among social media platforms, Reddit has emerged as the most promising one due to its anonymity and its focus on topic-based communities (subreddits) that can be indicative of someone's state of mind or interest regarding mental health disorders such as r/SuicideWatch, r/Anxiety, r/depression. A challenge for previous work on suicide risk assessment has been the small amount of labeled data. We propose an empirical investigation into several classes of weakly-supervised approaches, and show that using pseudo-labeling based on related issues around mental health (e.g., anxiety, depression) helps improve model performance for suicide risk assessment.

关键词： Risk assessment

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Enhancing Resume Analysis: Leveraging natural language processing and Machine Learning for Automated Resume Screening Using KSA Parameters

Enhancing Resume Analysis: Leveraging Natural Language Proce...

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Advanced Research in Computing (ICARC), International conference on

作者： B. A Thisara Dilshan P. P. G Dinesh Asanka Department of Industrial Management Faculty of Science University of Kelaniya Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

In the digital age, the recruitment landscape has changed dramatically. This study aims to optimize recruitment by automating resume screening through advanced natural language processing (NLP) and Machine Learning (ML) techniques. The purpose of this research is to address the limitations of traditional Applicant Tracking Systems (ATS) by introducing a framework employing Bidirectional Encoder Representations from Transformers (BERT). A comprehensive methodology was implemented, including data preprocessing, manual dataset annotation, feature extraction via TF-IDF, and cosine similarity for ranking resumes against job descriptions. empirical findings reveal that BERT outperforms other ML technologies including SVM, achieving a classification accuracy of 78.6%, validating its effectiveness in KSA extraction and alignment. Additionally, the system's outputs were validated by industry experts, who confirmed the accuracy and relevance of the extracted KSAs in real-world scenarios. The practical implications of this work include providing actionable insights for HR professionals, enhancing candidate-job matching, and streamlining recruitment workflows. The originality of this study lies in integrating advanced ML models for dynamic KSA classification, paving the way for more efficient recruitment practices. Future research may explore ensemble techniques and transfer learning to further refine the methodology and adapt it to diverse industries.

关键词： Industries Accuracy Resumes Transfer learning Bidirectional control Feature extraction Transformers natural language processing Encoding Recruitment

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Explainable Deep-Learning Model Reveals Past Cardiovascular Disease in Patients with Diabetes Using Free-Form Visit Reports 8th

Explainable Deep-Learning Model Reveals Past Cardiovascular ...

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8th Annual International conference on Machine Learning, Optimization and Data science (LOD)

作者： Guazzo, Alessandro Longato, Enrico Fadini, Gian Paolo Morieri, Mario Luca Sparacino, Giovanni Di Camillo, Barbara Univ Padua Dept Informat Engn Padua Italy Univ Padua Dept Med Padua Italy Univ Padua Dept Comparat Biomed & Food Sci Legnaro Italy

ISBN: (纸本)9783031255984;9783031255991

Writing notes remains the most widespread method by which medical doctors record clinical events. As a result, a relevant portion of the information concerning a patient's health status is recorded in the form of text. This is particularly relevant in the case of people affected by chronic diseases, such as diabetes, who need frequent medical attention, and, thus, produce substantial amounts of textual data. natural language processing methods are valuable in this context, as they can enable the automatic conversion of free-form text into structured data. However, in everyday clinical practice, these approaches, which often rely on artificial intelligence, are not widely used as they are perceived as black boxes. In this study, we used a typical electronic health record dataset, paired with a subset of a hospital discharge registry, to develop a tool to automatically identify past cardiovascular disease (CVD) hospitalisations using the free-form text of routine visits of diabetic patients. Thanks to its high performance (F1-score = 0.927), the proposed tool may be reliably used to help clinicians to fill medical records in a structured fashion by raising an alert when the model identifies a CVD hospitalisation, but no history of CVD is annotated within the patient's electronic health record. We also provided a robust interpretability framework following the trustworthy artificial intelligence principles of i) human agency, ii) human oversight, and iii) technical robustness and safety. Leveraging the provided interpretability framework, it is also possible to understand the most relevant factors that led to the alert, empowering the user to decide whether to follow up on the algorithm's suggestion or ignore it.

关键词： Cardiovascular diseases Deep learning Diabetes mellitus natural language processing

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Incorporating Punctuation Information to Enhance NMT Robustness on ASR Errors

Incorporating Punctuation Information to Enhance NMT Robustn...

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natural language processing (ICNLP), International conference on

作者： Jiaxin Shen Shuaibo Wang Yufeng Chen Beijing Jiaotong University Beijing China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

Neural Machine Translation (NMT) system is often confronted with the challenge of dealing with transcripts generated by the Automatic Speech Recognition (ASR) model, which poses a significant threat to the translation quality due to the various errors present in the transcripts. Although previous research methods have proved effective in combating ASR noise attacks, they have not been able to make full use of ASR noise information and have performed inadequately in low-resource scenarios. To this end, we propose a noise quality score metric PWER to maximize the impact of the Noise Generation Model (NGM) on improving the robustness of the translation model. Additionally, we introduce a Punctuation Information Detection (PID) module to alleviate sentence segmentation errors. Experimental results show that our method improves the robustness of the NMT system on ASR errors and still performs well in low-resource scenarios. Furthermore, our proposed approach outperforms previous methods, achieving state-of-the-art results in rich and low-resource scenarios.

关键词： Noise Measurement uncertainty Neural machine translation Robustness natural language processing Noise measurement Speech processing Automatic speech recognition

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Automated Question Answering for Improved Understanding of Compliance Requirements: A Multi-Document Study 30

Automated Question Answering for Improved Understanding of C...

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30th IEEE International Requirements Engineering conference (RE)

作者： Abualhaija, Sallam Arora, Chetan Sleimi, Amin Briand, Lionel C. Univ Luxembourg SnT Ctr Secur Reliabil & Trust Luxembourg Luxembourg Deakin Univ Geelong Vic Australia Univ Ottawa Sch Elect Engn & Comp Sci Ottawa ON Canada

ISBN: (数字)9781665470001

ISBN: (纸本)9781665470001

Software systems are increasingly subject to regulatory compliance. Extracting compliance requirements from regulations is challenging. Ideally, locating compliance-related information in a regulation requires a joint effort from requirements engineers and legal experts, whose availability is limited. However, regulations are typically long documents spanning hundreds of pages, containing legal jargon, applying complicated natural language structures, and including cross-references, thus making their analysis effort-intensive. In this paper, we propose an automated question-answering (QA) approach that assists requirements engineers in finding the legal text passages relevant to compliance requirements. Our approach utilizes large-scale language models fine-tuned for QA, including BERT and three variants. We evaluate our approach on 107 question-answer pairs, manually curated by subject-matter experts, for four different European regulatory documents. Among these documents is the general data protection regulation (GDPR) - a major source for privacy-related requirements. Our empirical results show that, in similar to 94% of the cases, our approach finds the text passage containing the answer to a given question among the top five passages that our approach marks as most relevant. Further, our approach successfully demarcates, in the selected passage, the right answer with an average accuracy of similar to 91%.

关键词： Requirements Engineering Regulatory Compliance natural language processing (NLP) Question Answering language Models (LMs) BERT

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Evaluation of Text Generation Speeds for a Set of Compact Large language Models 43rd

Evaluation of Text Generation Speeds for a Set of Compact La...

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43rd IBIMA conference on Artificial intelligence and Machine Learning, IBIMA-AI 2024

作者： Hojda, Maciej Popek, Grzegorz Faculty of Computer Science and Telecommunications Wroclaw Univeristy of Science and Technology Janiszewskiego 11/17 Wroclaw50-372 Poland

ISBN: (纸本)9783031790850

We present an empirical evaluation of several compact large language models (LLMs) from the Llama 2 family. The models are small enough to be run on a typical consumer machine which allows them to be used offline, enabling increased privacy. Their efficiency at tasks such as giving instructions or explanations on a wide variety of subjects makes them a viable alternative to online language processing tools such as ChatGPT. We evaluate the impact of the GPU offloading, the number of threads and the size of the context on the token generation speed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： natural language processing systems

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