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检索条件"任意字段=International Conference on Natural Language Processing and Knowledge Engineering"

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Attention-Based Image Caption Generation 5th

Attention-Based Image Caption Generation

引用

5th international conference on Data Science, Machine Learning and Applications

作者： Manasa, M. Sowmya, D. Reddy, Y. Supriya Sreedevi, Pogula G Pulla Reddy Engn Coll Dept Comp Sci & Engn Data Sci Kurnool 5185007 India G Pulla Reddy Engn Coll Dept Comp Sci & Engn Kurnool 5185007 India Rajeev Gandhi Mem Coll Engn & Technol Dept Comp Sci & Engn Nandyal 518501 India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

The automatic development of meaningful, detailed textual descriptions for supplied images is a difficult task in the fields of computer vision and natural language processing. As a result, an AI-powered image caption generator can be incredibly useful for producing captions. In this study, we present a unique method for creating picture captions utilizing an attention mechanism that concentrates on pertinent areas of the image while it creates captions. On benchmark datasets, our model, which uses deep neural networks to extract picture attributes and produce captions, obtains state-of-the-art results, confirming the effectiveness of the attention mechanism in raising the caliber of the generated captions. We also offer a thorough evaluation of the performance of our approach and talk about potential future directions for enhancing image caption generation.

关键词： Image Caption Computer Vision natural language processing Attention Mechanism Performance

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Chatbot for patients suffering from mild cognitive impairment 16

Chatbot for patients suffering from mild cognitive impairmen...

引用

16th international conference on ENTERprise Information Systems, CENTERIS 2024 - 12th international conference on Project MANagement, ProjMAN 2024 - 14th international conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Podoreanu, Bianca Vasilateanu, Andrei Marian, Constantin Viorel Department of Engineering in Foreign Languages National University of Science and Technology Politehnica Bucuresti Romania

This paper introduces a novel approach that leverages interactions with a chatbot for assessing the progression of mild cognitive impairment while at the same time aiding users in their daily tasks. Given the potential progression of this condition into dementia, monitoring behavioral changes becomes a tool for prevention. The application uses a large language model to generate responses to user prompts and incorporates natural language processing libraries equipped with methods for analyzing the user's interactions with the chatbot. Responses are analyzed using the following features: similarity between the prompts from the user to the chatbot;the number of grammatical errors the user has made in his prompt;the overall sentiment of the prompt from the user. The application can be easily integrated into other health monitoring frameworks. © 2025 The Author(s).

关键词： natural language processing systems

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A Dataset of Semantically Related Multiword Terms of the Electrical engineering Domain 24

A Dataset of Semantically Related Multiword Terms of the Ele...

引用

8th international conference on natural language processing and Information Retrieval, NLPIR 2024

作者： Druselmann, Maria Harbusch, Karin Institute for Computational Visualistics University of Koblenz Koblenz Germany

ISBN: (纸本)9798400717383

This paper presents the EEMWT dataset, a collection of 1873 triplets of co-hyponymic multiword terms of the electrical engineering domain. Each triplet combines an anchor term with closely and distantly related co-hyponymic terms. The degree of semantic relatedness is determined by the presence or absence of shared domain-specific semantic features. The primary purpose of this dataset is to serve as a tool for evaluating domain-specific language representation models and embedding vector pooling techniques by assessing the semantic relatedness of co-hyponymic multiword terms. The novelty of the dataset lies in the development approach, which replaces intuition-based scaling of relatedness with linguistically justified semantic-feature-based judgment. Furthermore, the traditional method of calculating interrater agreement rate is replaced with a statistical analysis of vector space distance between low and highly related terms. These innovations in dataset construction and evaluation have the potential to significantly reduce the costs associated with expert questionnaires in dataset development. © 2024 Copyright held by the owner/author(s).

关键词： dataset of semantically related terms

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Enhancing Word-Level Completion for Masked language Model with Multi-Model Fusion 13th

Enhancing Word-Level Completion for Masked Language Model wi...

引用

13th international conference on natural language processing and Chinese Computing

作者： Chang, Xinquan Zhu, Junguo Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Artificial Intelligence Kunming 650500 Yunnan Peoples R China

ISBN: (纸本)9789819794393;9789819794409

Word-level completion can automatically complete words as the translator types character sequences. Word-level completion can accelerate the editing process of human translation and ensure the translation quality. Although significant progress has been made in the field, there may be multiple candidate words when models predict words. Multiple words make up a list of candidate words. We improve the existing model by determining the most credible word in the candidate word list. We propose a multi-model fusion method to increase the accuracy of word-level completion. The improved model can use multiple evaluation criteria (Lesk method, WordNet knowledge base, and pre-training model) to calculate the scores of words by classification and weighting. The word with the highest score is selected as the most credible word. The experimental results prove that our proposed method is effective. In De-.En, our method improves the accuracy by 2.83%. In Zh-.En, our method improves the accuracy by 2.77%.

关键词： Computer-Aided Translation Mask language Model Word-Level Auto Completion

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Voice Over Vision: A Sequence-to-Sequence Model by Text to Speech Technology 1

Voice Over Vision: A Sequence-to-Sequence Model by Text to S...

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1st international conference on Innovations in Communications, Electrical and Computer engineering, ICICEC 2024

作者： Satish, E.G. Naidu, P. Ramesh Mogera, Girish Madhava Karthik, H.V. Abhinandan Nitte Meenakshi Institute of Technology Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350376517

Text-to-speech (TTS) turns written text into spoken words using artificial voices. This uses natural language processing (NLP) and speech synthesis to make audio from text input. TTS has many uses - for people with visual impairments, hands-free communication, voiceovers for multimedia content. The development of neural network-based models like WaveNet and Tacotron has made synthetic speech much more natural and expressive. This paper reviews the history of TTS, the key components and techniques of modern TTS and future directions including emotion and personalization in synthetic speech. We also explore the ethical considerations and societal impacts of TTS and why we need to be responsible with its development and deployment. © 2024 IEEE.

关键词： natural language processing systems

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Social Media Analysis using Fuzzy natural language processing with an extension of semantic queries

Social Media Analysis using Fuzzy Natural Language Processin...

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2023 Annual international conference on Emerging Research Areas: international conference on Intelligent Systems, AICERA/ICIS 2023

作者： Haroon, Noor Hanoon Abdulsada, Zainab.R. Sharif, Haider Ahmed, Wasan Saad Saleem, Munqith Jawad, Israa Abed Technical Engineering College Al-Ayen University Department of Computer Technical Engineering Thi-Qar Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Medical Instruments Engineering Techniques Al-farahidi University Baghdad Iraq Computer Technologies Engineering Al-Turath University College Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Computer Engineering Techniques Al-Nisour University College Baghdad Iraq

ISBN: (纸本)9798350303452

Information retrieval (IR) is a topic of continuing study, with the apparent goal being to find the most relevant data in massive repositories. The user's inquiry is essential at this stage. Finding crucial documents written in natural language has required extensive effort. Expertise retrieval relies on query extension to provide accurate data retrieval. This research presents a semantic query extension for social media analysis utilizing fuzzy natural language processing. This model computes the sentiment of social media posts using an innovative set of fuzzy rules informed by a wide range of lexicons and data sets. Using a combination of NLP and fuzzy logic, the proposed technique classifies posts according to whether they were written with joy, sadness, or no particular emotion. Researchers can use the results to zero in on the ideal social media jargon. Based on experimental results, it is clear that the suggested model can guarantee effective performance on semantic query expansion, outperforming state-of-the-art models in terms of classification accuracy (97.32 percent). Based on the studies mentioned above, we find that the success rate of enhancing fuzzy natural language is 85.7%, the success rate for boosting precision on text search engines is 85.3%, the success rate of exploring records from databases is 83.6%, the success rate of investigating the lexicons of social media conversations is 82.5%, the success rate of developing the knowledge retrieval system is 91.84%, and the success rate of decreasing query extensions is 84.6%. © 2023 IEEE.

关键词： Search engines

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The Smart Intuitive natural language Understanding with Recurrent Neural Networks

The Smart Intuitive Natural Language Understanding with Recu...

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2024 international conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Raghavendra, R. Pandeya, Megha Kumar, Deepak Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Mathematics Jaipur India

ISBN: (纸本)9798350383348

Recurrent neural networks (RNNs) are becoming increasingly popular in addressing natural language knowledge duties. It is because of their ability to capture lengthy-time period dependencies and collection shapes by leveraging the temporal houses of lengthy-time period reminiscence. Mainly, RNNs can process facts with unique sequences of inputs, continuously running the same functions over various series steps. By using expertise-primarily based techniques in the shape of knowledge graphs, ontologies, and semantic triples, RNNs may be used to explain the semantic relationships between domain entities and their related attributes. The innovative Intuitive natural language information (INLU) with RNNs combines knowledge-based tactics with the strength of collecting and gaining knowledge of RNNs. It allows for understanding natural language by modulating the understanding of graph semantic models to seize and interact with the distributed representations of language. This method allows RNNs to research languages and identifiers that may be used to interpret personal queries. Moreover, INLU permits efficient inference over the semantic graph to provide correct information on herbal language queries. INLU can obtain superior semantic accuracy and extra-accurate question matching by offering semantic relationships among user questions and related domains. © 2024 IEEE.

关键词： modulating neural networks Recurrent reminiscence understanding

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Mixed Multimodal Contrastive Learning for Enhancing Detection and Correction of Faked and Misspelled Chinese Characters 13th

Mixed Multimodal Contrastive Learning for Enhancing Detectio...

引用

13th international conference on natural language processing and Chinese Computing

作者： He, Rui Wang, Zhongqing Wang, Hongling Soochow Univ Nat Language Proc Lab Suzhou Peoples R China

ISBN: (纸本)9789819794423;9789819794430

Visual Chinese Character Checking (C3) aims to detect and correct errors in handwritten Chinese text images, including faked characters and misspelled characters. This task is beneficial for subsequent tasks by improving the efficiency of identifying errors in handwritten text. Recent methods are mainly based on Optical Character Recognition (OCR) and Pre-trained language Models (PLMs). Visual Chinese Character Checking is an emerging task, and relevant research has made progress. However, we believe that existing work has not fully leveraged the inherent knowledge of pre-trained models and has not addressed the semantic bias issue between pre-trained models and the character checking task. These challenges result in deficiencies in recognizing misspelled Chinese characters and correcting misused characters. Therefore, we propose various multimodal contrastive learning methods based on image-to-image and image-to-text comparisons. These methods are used throughout the processes of character recognition, error detection, and correction. By aligning the semantic feature representations among different models, our approach makes these models more suitable for the Visual Chinese Character Checking task, thereby enhancing their capabilities.

关键词： Chinese Character Checking Chinese Spell Checking Contrastive Learning Optical Character Recognition

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KOSA: KO Enhanced Salary Analytics based on knowledge Graph and LLM Capabilities 23

KOSA: KO Enhanced Salary Analytics based on Knowledge Graph ...

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23rd IEEE international conference on Data Mining (IEEE ICDM)

作者： Huang, Fei Deng, Yi Zhang, Chen Guo, Menghao Zhan, Kai Sun, Shanxin Jiang, Jinling Sun, Zeyi Wu, Xindong Zhejiang Lab Res Ctr Knowledge Engn Hangzhou 311121 Zhejiang Peoples R China

ISBN: (纸本)9798350381641

knowledge base question answering (KBQA) is designed to respond to natural language inquiries by utilizing factual information, such as entities, relationships, and attributes, derived from a knowledge base (KB). The advent of large language models (LLMs) has significantly boosted the performance of KBQA, owing to their exceptional capabilities in content comprehension and generation. In this paper, we present a knowledge Ocean enhanced Salary Analytics (KOSA) system based on knowledge graphs and LLMs tailored to employee salary data from a public university. This system encompasses an interactive conversational interface, visualization of knowledge graphs, and advanced data analysis. By employing the framework of knowledge engineering, we enable knowledge graph modeling, Cypher (the query engine of Neo4j) reasoning, and question answering functionalities. Furthermore, machine learning algorithms are integrated to facilitate advanced features, such as salary prediction and allocation.

关键词： KBQA LLM Salary distribution knowledge engineering.

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Ethics in natural language processing: Addressing Bias, Privacy, and Misinformation

Ethics in Natural Language Processing: Addressing Bias, Priv...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Joshi, Himanshu Sanjay Taherdoost, Hamed Westcliff University College of Business CA United States Hamta Business Corporation R&d Department Vancouver Canada University Canada West Vancouver Canada Gus Institute | Global University Systems London United Kingdom Q Minded | Quark Minded Technology Inc. Vancouver Canada

ISBN: (纸本)9798331508685

natural language processing (NLP) stands at the forefront of artificial intelligence, empowering machines with human-like capabilities to process text and speech. The versatile applications of NLP encompass information retrieval, text summarization, text categorization, and machine translation. In the dynamic landscape of information dissemination, the prevalence of misinformation, bias and the need for credible sources have become critical concerns. This research effort addresses critical ethical considerations related to the development of NLP algorithms using the Value Sensitive Design (VSD) framework. This paper explores the intersections between NLP and knowledge graphs, through the lens of VSD aiming to enhance the ethics of NLP solutions. © 2025 IEEE.

关键词： Differential privacy

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