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

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Geocontext extraction methods analysis for determining the new approach to automatic semantic places recognition 16

Geocontext extraction methods analysis for determining the n...

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16th FRUCT conference of Open-Innovations-Association

作者： Zaslavskiy, Mark Mouromtsev, Dmitry ITMO Univ St Petersburg Russia

ISBN: (纸本)9785757704890

Goal of this paper is to determine actual trends in geocontext extraction methods and to understand which types of geocontext information are the most interesting for users. For this purposes comparison of recent researches about geocontext analysis was done. Researches were compared by the type of achieved result, used formalism, source data and limitations. As the main result of comparison new approach for automatic semantic places recognition was proposed. This approach is based on geotags markup with semantic user-defined tags. The solution allows extracting information (coordinates and a set of corresponding semantic tags on the natural language) about locations which are interesting for the location-based services users. The main advantage of the approach is its simplicity - the method does not rely on any syntax analysis algorithms during the semantic labeling stage. For illustrating the approach an example of the general purpose accidents monitoring service for the Geo2Tag platform was described.

关键词： accident prevention geographic information systems natural language processing semantic networks ubiquitous computing Geo2Tag platform automatic semantic places recognition general purpose accident monitoring service geocontext analysis geocontext extraction methods analysis geocontext information geotags markup location-based services natural language semantic labeling stage semantic tags semantic user-defined tag syntax analysis algorithm Buildings Context Global Positioning System Hidden Markov models History Labeling Semantics Semantics Location based services semantic networks Accident Prevention Hidden Markov models natural language processing Ubiquitous computing Global Positioning Systems geographic information systems Buildings natural languages

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RESEARCH ON TEXT SENTIMENT ANALYSIS IN natural language processing

RESEARCH ON TEXT SENTIMENT ANALYSIS IN NATURAL LANGUAGE PROC...

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2024 International conference on Electrical Engineering and Intelligent Control, EEIC 2024

作者： Jiao, Shuoyang Department of Electronic Information Engineering Chengdu Jincheng University Sichuan Chengdu611731 China

ISBN: (纸本)9781837242672

Text sentiment analysis in natural language processing is an important means of understanding and interpreting human emotions in texts. It plays an important role in various applications, such as customer feedback analysis and social media monitoring. The topic of this article is research on text sentiment analysis in natural language processing. Four text sentiment analysis methods are reviewed: the method based on sentiment dictionary, the method based on traditional machine learning, the method based on deep learning, and the method based on hybrid method. By citing the literature on four methods, the paper analyzing the definition, advantages, disadvantages, and performance of each method in detail. The research results show that text sentiment analysis is mainly the method based on deep learning and the hybrid method. The conclusion highlights the need for continuing research to address the limitation of existing methods and improve the accuracy and applicability of sentiment analysis in different scenarios. © The Institution of Engineering & Technology 2024.

关键词： Adversarial machine learning

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Analyzing a Decade of Evolution: Trends in natural language processing 26th

Analyzing a Decade of Evolution: Trends in Natural Language ...

引用

26th International conference on Data Warehousing and Knowledge Discovery (DaWaK)

作者： Jonker, Richard A. A. Almeida, Tiago Matos, Sergio Univ Aveiro LASI IEETA DETI Aveiro Portugal

ISBN: (纸本)9783031683220;9783031683237

natural language processing (NLP) stands at the forefront of the rapidly evolving landscape of Machine Learning, witnessing the emergence and evolution of diverse methodologies over the past decade. This study delves into the dynamic trends within the NLP domain, specifically spanning the years 2010 to 2022, through an empirical analysis of papers presented at conferences hosted by the Association for Computational Linguistics (ACL). We utilize ChatGPT in order to extract meaningful information from the data before performing an in depth analysis. Our investigation encompasses an exploration of several key aspects, namely computational trends, research trends and geographic trends. We further investigate the entry cost into NLP, the longevity of hardware and the environmental impact of NLP. The code to run our system is publicly available at https://***/ieeta-pt/nlp-trends.

关键词： Data Analytics natural language processing Data Extraction NLP Trends

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Can Machine Unlearning Reduce Social Bias in language Models?

Can Machine Unlearning Reduce Social Bias in Language Models...

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2024 conference on empirical methods in natural language processing, EMNLP 2024

作者： Dige, Omkar Singh, Diljot Yau, Tsz Fung Zhang, Qixuan Bolandraftar, Mohammad Zhu, Xiaodan Khattak, Faiza Khan Vector Institute Canada Scotiabank Canada Ernst & Young Queen’s University Canada

ISBN: (纸本)9798891761667

Mitigating bias in language models (LMs) has become a critical problem due to the widespread deployment of LMs in the industry and customer-facing applications. Numerous approaches revolve around data pre-processing and subsequent fine-tuning of language models, tasks that can be both time-consuming and computationally demanding. As alternatives, machine unlearning techniques are being explored, yet there is a notable lack of comparative studies evaluating the effectiveness of these methods. In this work, we explore the effectiveness of two machine unlearning methods: Partitioned Contrastive Gradient Unlearning (PCGU) (Yu et al., 2023) applied on decoder models, and Negation via Task Vector (Ilharco et al., 2022), and compare them with Direct Preference Optimization (DPO) (Rafailov et al., 2024) to reduce social biases in open-source LMs such as LLaMA-2 and OPT1. We also implement distributed PCGU for large models2. It is empirically shown, through quantitative and qualitative analyses, that negation via Task Vector method outperforms PCGU and is comparable to DPO in debiasing models with minimum deterioration in model performance and perplexity. Negation via Task Vector reduces the bias score by 25.5% for LLaMA-2 and achieves bias reduction of up to 40% for OPT models. Moreover, it can be easily tuned to balance the trade-off between bias reduction and generation quality, unlike DPO. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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natural language processing for the automation of operations in an online bookstore 19

Natural language processing for the automation of operations...

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19th IEEE International conference on Intelligent Computer Communication and processing conference, ICCP 2023

作者： Iorga, Elena Dumitriu, Tiberius Gheorghe Asachi' Technical University of Iaşi Faculty of Automatic Control and Computer Engineering Iaşi Romania

ISBN: (纸本)9798350370355

The use of natural language processing Algorithms (NLP) for automation purposes in various applications is frequently encountered recently. Some research managed to identify the dominant emotion from a text using neural networks (ANN), Random Forest (RF) and Support Vectors Machines (SVM) while other studies classified documents with the aim of mechanizing the extraction process of *** paper presents a study on different natural language processing Algorithms (NLP) used for automation of a virtual bookstore. The objective was the application of artificial intelligence algorithms to streamline the necessary processes of an online bookstore or library, taking into consideration the short descriptions and summaries of the books. The aim is to improve the results obtained by supervised algorithms using various techniques such as aggregating unsupervised classifications. In order to boost their understanding of natural language, GPT is used to enhance the dataset by adding additional context. Finally, some of the methods utilized in improving accuracy can be used to create a personalized recommendation system that suits each reader's needs. © 2023 IEEE.

关键词： Automation

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COQ:AN empirical FRAMEWORK FOR MULTI-HOP QUESTION ANSWERING EMPOWERED BY LARGE language MODELS 49

COQ:AN EMPIRICAL FRAMEWORK FOR MULTI-HOP QUESTION ANSWERING ...

引用

49th IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Huang, Qiang Huang, Feng Tao, DeHao Zhao, YueTong Wang, BingKun Huang, YongFeng Xinjiang Univ Coll Informat Sci & Engn Urumqi Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China Zhengzhou Coll Finance & Econ Sch Informat Engn Zhengzhou Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China Tsinghua Univ Sch Humanities Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Prompt-based Large language Models(LLMs) are surprisingly powerful in generating natural language reasoning steps or Chains of Thoughts(CoT) for multi-hop question answering(QA). However, LLMs struggle when they lack access to necessary knowledge or when the knowledge within their parameters is outdated. Additionally, LLMs that rely solely on CoT tend to generate hallucinations during the reasoning process. To address these dilemmas, we propose the Chain of Question (CoQ) framework, a novel multi-hop QA approach. This approach decomposes a complex original question into multiple sub-questions according to a CoT to retrieve knowledge from an external knowledge base. It then answers the question process based on the retrieved knowledge in accordance with a CoT. We design that each point of thought generated during the reasoning process be supported by the knowledge retrieved in the external knowledge base. Experiments show that CoQ is effective in reducing model hallucinations, leading to higher factual accuracy than *** average, it reduces factual errors by 31% over CoT, and even by 38% on the two most commonly used models today.

关键词： Large language Models Question Answering CoT Knowledge Bases

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Breaking the language Barrier: Improving Cross-Lingual Reasoning with Structured Self-Attention

Breaking the Language Barrier: Improving Cross-Lingual Reaso...

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conference on empirical methods in natural language processing (EMNLP)

作者： Foroutan, Negar Banaei, Mohammadreza Aberer, Karl Bosselut, Antoine Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9798891760615

In this work, we study whether multilingual language models (MultiLMs) can transfer logical reasoning abilities to other languages when they are fine-tuned for reasoning in a different language. We evaluate the cross-lingual reasoning abilities of MultiLMs in two schemes: (1) where the language of the context and the question remain the same in the new languages that are tested (i.e., the reasoning is still monolingual, but the model must transfer the learned reasoning ability across languages), and (2) where the language of the context and the question is different (which we term code-switched reasoning). On two logical reasoning datasets, RuleTaker and LeapOfThought, we demonstrate that although MultiLMs can transfer reasoning ability across languages in a monolingual setting, they struggle to transfer reasoning abilities in a code-switched setting. Following this observation, we propose a novel attention mechanism that uses a dedicated set of parameters to encourage cross-lingual attention in code-switched sequences, which improves the reasoning performance by up to 14% and 4% on the RuleTaker and LeapOfThought datasets,

关键词： Computational linguistics

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Text Window Denoising Autoencoder: Building Deep Architecture for Chinese Word Segmentation

Text Window Denoising Autoencoder: Building Deep Architectur...

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2nd Annual conference of China-Computer-Federation Technical-Committee-of-Chinese-Information on natural language processing and Chinese Computing (CCF TCCI NLPCC)

作者： Wu, Ke Gao, Zhiqiang Peng, Cheng Wen, Xiao Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9783642416439;9783642416446

Deep learning is the new frontier of machine learning research, which has led to many recent breakthroughs in English natural language processing. However, there are inherent differences between Chinese and English, and little work has been done to apply deep learning techniques to Chinese natural language processing. In this paper, we propose a deep neural network model: text window denoising autoencoder, as well as a complete pre-training solution as a new way to solve classical Chinese natural language processing problems. This method does not require any linguistic knowledge or manual feature design, and can be applied to various Chinese natural language processing tasks, such as Chinese word segmentation. On the PKU dataset of Chinese word segmentation bakeoff 2005, applying this method decreases the F1 error rate by 11.9% for deep neural network based models. We are the first to apply deep learning methods to Chinese word segmentation to our best knowledge.

关键词： Deep Learning Word Segmentation Denoising Autoencoder Chinese natural language processing

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Indicatements that character language models learn English morpho-syntactic units and regularities 1

Indicatements that character language models learn English m...

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1st Workshop on BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, co-located with the 2018 conference on empirical methods in natural language processing, EMNLP 2018

作者： Kementchedjhieva, Yova Lopez, Adam University of Copenhagen Denmark University of Edinburgh United Kingdom

ISBN: (纸本)9781948087711

Character language models have access to surface morphological patterns, but it is not clear whether or how they learn abstract morphological regularities. We instrument a character language model with several probes, finding that it can develop a specific unit to identify word boundaries and, by extension, morpheme boundaries, which allows it to capture linguistic properties and regularities of these units. Our language model proves surprisingly good at identifying the selectional restrictions of English derivational morphemes, a task that requires both morphological and syntactic awareness. Thus we conclude that, when morphemes overlap extensively with the words of a language, a character language model can perform morphological abstraction. © 2018 Association for Computational Linguistics

关键词： Syntactics

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Domain Ontology Induction using Word Embeddings 15

Domain Ontology Induction using Word Embeddings

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15th IEEE International conference on Machine Learning and Applications (ICMLA)

作者： Gupta, Niharika Podder, Sanjay Annervaz, K. M. Sengupta, Shubhashis IIIT Delhi Delhi India Accenture Technol Labs Chicago IL USA

ISBN: (纸本)9781509061679

Ontology, the shared formal conceptualization of domain information, has been shown to have multiple applications in modeling, processing and understanding natural language text. In this work, we use distributed word vectors out of various recent language models from Deep Learning for semi- automated domain ontology creation for closed domains. We cover all major aspects of Domain Ontology Induction or Learning like concept identification, attribute identification, taxonomical and nontaxonomical relationship identification using the distributed word vectors. Preliminary results show that simple clustering based methods using distributed word vectors from these language models outperforms methods using models like LSI in ontology learning for closed domains.

关键词： language Modeling Deep Learning natural language processing Ontology Learning Word Vectors Clustering Latent Semantic Indexing

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