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

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Improving Habitability of NLI for Querying Ontologies with Feedback

Improving Habitability of NLI for Querying Ontologies with F...

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International conference on Information Communication and Embedded Systems (ICICES)

作者： Arivarasan, S. Tagore Engn Coll Dept Informat Technol Chennai Tamil Nadu India

ISBN: (纸本)9781479938346

natural language Interfaces (NLIs) are a viable, human-readable alternative to complex, formal query languages like SPARQL, which are typically used for accessing semantically structured data (e.g. RDF and OWL repositories). However, in order to cope with natural language ambiguities, NLIs typically support a more restricted language. A major challenge when designing such restricted languages is habitability- how easily, naturally and effectively users can use the language to express themselves within the con-strains imposed by the system. In this paper, we investigate two methods for improving the habitability of a natural language Interface: feedback and clarification dialogues. We model feedback by showing the user how the system interprets the query, thus suggesting repair through query reformulation. Next, we investigate how clarification dialogues can be used to control the query interpretations generated by the system. To reduce the cognitive overhead, clarification dialogues are coupled with a learning mechanism. Both methods are shown to have a positive effect on the overall performance and habitability.

关键词： learning (artificial intelligence) natural language interfaces ontologies (artificial intelligence) query processing NLI habitability SPARQL clarification dialogues cognitive overhead feedback formal query languages human-readable alternative learning mechanism natural language ambiguities ontology querying query reformulation semantically structured data Context Educational institutions Maintenance engineering natural languages Ontologies Syntactics Usability natural language interfaces query reformulation Learning mechanisms natural languages Ontology Feedback Maintenance engineering data base language Educational institutions query analysis habitability LDB1 gene

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Parameter-Efficient Cross-lingual Transfer of Vision and language Models via Translation-based Alignment

Parameter-Efficient Cross-lingual Transfer of Vision and Lan...

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conference on empirical methods in natural language processing (EMNLP)

作者： Zhang, Zhen Wang, Jialu Wang, Xin Eric UC Santa Barbara Santa Barbara CA 93106 USA UC Santa Cruz Santa Cruz CA USA

ISBN: (纸本)9798891760615

Pre-trained vision and language models such as CLIP (Radford et al., 2021) have witnessed remarkable success in connecting images and texts with a primary focus on English texts. Despite recent efforts to extend CLIP to support other languages, disparities in performance among different languages have been observed due to uneven resource availability. Additionally, current cross-lingual transfer methods of those pre-trained models would consume excessive resources for a large number of languages. Therefore, we propose a new parameter-efficient cross-lingual transfer learning framework that utilizes a translation-based alignment method to mitigate multilingual disparities and explores parameterefficient fine-tuning methods for parameterefficient cross-lingual transfer. Extensive experiments on XTD (Aggarwal and Kale, 2020) and Multi30K (Elliott et al., 2016) datasets, covering 11 languages under zero-shot, few-shot, and full-dataset learning scenarios, show that our framework significantly reduces the multilingual disparities among languages and improves cross-lingual transfer results, especially in low-resource scenarios, while only keeping and fine-tuning an extremely small number of parameters compared to the full model (e.g., Our framework only requires 0.16% additional parameters of a full-model for each language in the few-shot learning scenario). The codes are available at https://***/eric-ai-lab/PECTVLM.

关键词： Zero-shot learning

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An Electric System Abnormal Analysis Framework based on natural language processing 8

An Electric System Abnormal Analysis Framework based on Natu...

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8th International conference on Advanced Cloud and Big Data (CBD)

作者： Gan, Wenfeng Su, Bo Li, Yan Corp China Southwest Branch State Grid 299 Shuxiu West Rd Chengdu Sichuan Peoples R China

ISBN: (纸本)9781665423137

The stable operation of power grid system is essential for our daily lives. The on-site patrol and examining play an important rule to detect the potential damage and stabilize the power grid system. In this paper, we propose a framework to analyze possible errors in electric system by natural language processing method so that the on-site workers can directly describe the phenomenon and get the instruction to deal with the situation. The framework applies the word2vec algorithm to calculate the similarity between the words, and Earth Movers' Distance method is adopted to compute the overall semantic similarity between the on-site situation and the history record. Based on the similar records, the next step operation is generated to guide the on-site workers. We implement the proposed framework for electric distribution network and the experimental results indicate that the proposed method has better accuracy (93%) compared with existing methods (84%) and can be used to improve the management of power grid system.

关键词： Abnormal analysis natural language processing word embedding semantic similarity

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REDISCOVERING 50 YEARS OF DISCOVERIES IN SPEECH AND language processing: A SURVEY. 20

REDISCOVERING 50 YEARS OF DISCOVERIES IN SPEECH AND LANGUAGE...

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20th conference of the Oriental-Chapter-of-the-International-Coordinating-Committee-on-Speech-Databases-and-Speech-I/O-Systems-and-Assessment (O-COCOSDA)

作者： Mariani, Joseph Francopoulo, Gil Paroubek, Patrick Vernier, Frederic CNRS LIMSI Paris France Tagmatica Paris France

ISBN: (纸本)9781538633335

We have created the NLP4NLP corpus to study the content of scientific publications in the field of speech and natural language processing. It contains articles published in 34 major conferences and journals in that field over a period of 50 years (1965-2015). comprising 65.000 documents. gathering 50.000 authors. including 325.000 references and representing approximately 270 million words. Most of these publications are in English. some are in French. German or Russian. Some are open access. others have been provided by the publishers. In order to constitute and analyze this corpus several tools have been used or developed. Some of them use natural language processing methods that have been published in the corpus. hence its name. Numerous manual corrections were necessary. which demonstrated the importance of establishing standards for uniquely identifying authors. publications or resources. We have conducted various studies: evolution over time of the number of articles and authors. collaborations between authors. citations between papers and authors. evolution of research themes and identification of the authors who introduced them. measure of innovation and detection of epistemological ruptures. use of language resources. reuse of articles and plagiarism in the context of a global or comparative analysis between sources.

关键词： Speech processing natural language processing Text Analytics Bibliometrics Scientometrics Informetrics

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RealBehavior: A Framework for Faithfully Characterizing Foundation Models' Human-like Behavior Mechanisms

RealBehavior: A Framework for Faithfully Characterizing Foun...

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conference on empirical methods in natural language processing (EMNLP)

作者： Zhou, Enyu Zheng, Rui Xi, Zhiheng Gao, Songyang Fan, Xiaoran Fei, Zichu Ye, Jingting Gui, Tao Zhang, Qi Huang, Xuanjing Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Dept Chinese Language & Literature Shanghai Peoples R China Fudan Univ Inst Modern Languages & Linguist Shanghai Peoples R China

ISBN: (纸本)9798891760615

Reports of human-like behaviors in foundation models are growing, with psychological theories providing enduring tools to investigate these behaviors. However, current research tends to directly apply these human-oriented tools without verifying the faithfulness of their outcomes. In this paper, we introduce a framework, RealBehavior, which is designed to characterize the humanoid behaviors of models faithfully. Beyond simply measuring behaviors, our framework assesses the faithfulness of results based on reproducibility, internal and external consistency, and generalizability. Our findings suggest that a simple application of psychological tools cannot faithfully characterize all human-like behaviors. Moreover, we discuss the impacts of aligning models with human and social values, arguing for the necessity of diversifying alignment objectives to prevent the creation of models with restricted characteristics.

关键词： Behavioral research

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natural language processing based Cross Lingual Summarization 6

Natural Language Processing based Cross Lingual Summarizatio...

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6th International conference on Trends in Electronics and Informatics, ICOEI 2022

作者： Shree Akshaya, A.T. Shankaran, Shruthi Thrupthi, H M Mamatha, H.R. PES University Dept. of Computer Science Engineering Karnataka Bengaluru India

ISBN: (纸本)9781665483285

natural language processing (NLP) is a domain that programs machines to interpret/ comprehend human language like human beings do. Application of NLP to summarization and machine translation has been accelerating from past years. One such application, Cross-lingual summarization (CLS), produces a summary in a particular language (target language) for an input document in a different language (source language). CLS can help people understand an article's gist written in an unfamiliar language by translating the summary to their familiar language. Current methods divide CLS into two methods: Summarization (Abstractive or Extractive) and Translation. Although there has been a tremendous amount of progress in abstractive summarization in recent years, most research focuses on monolingual summarization because of lack of high-quality multilingual resources. While there have been a few studies to address the lack of resources for cross-lingual summarization, the datasets employed are very limited in size. This paper presents the Cross-Lingual Summarization from English to Kannada by summarizing English ne articles and translating the summaries to Kannada. Traditional CLS methods are pipeline-based i.e., it either involves summarization and then translation or vice versa. The summarization is followed by translation, hence it is termed as 'Late Translation'. © 2022 IEEE.

关键词： natural language processing systems

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Genetic algorithms for feature relevance assignment in memory-based language processing 4

Genetic algorithms for feature relevance assignment in memor...

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4th conference on Computational natural language Learning, CoNLL 2000 and of the 2nd Learning language in Logic Workshop, LLL 2000

作者： Kool, Anne Daelemans, Walter Zavrel, Jakub CNTS - Language Technology Group University of Antwerp UIA Universiteitsplein 1 Antwerpen2610 Belgium

We investigate the usefulness of evolutionary algorithms in three incarnations of the problem of feature relevance assignment in memory-based language processing (MBLP): feature weighting, feature ordering and feature selection. We use a simple genetic algorithm (GA) for this problem on two typical tasks in natural language processing: morphological synthesis and unknown word tagging. We find that GA feature selection always significantly outperforms the MBLP variant without selection and that feature ordering and weighting with GA significantly outperforms a situation where no weighting is used. However, GA selection does not significantly do better than simple iterative feature selection methods, and GA weighting and ordering reach only similar performance as current information-theoretic feature weighting methods. © ICGI *** right reserved.

关键词： Genetic algorithms

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Basic Structure of natural language processing Applied on Multiple Domains: Review 4

Basic Structure of Natural Language Processing Applied on Mu...

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4th International conference on Innovative Computing (ICIC)

作者： Hassan, Taimoor Ahmad, Abrar Anwar, Mehmood Ali, Muhammad Nadeem Saher, Farva Farooq, Muhammad Sajid Univ Lahore Dept Software Engn Lahore Pakistan Lahore Garrison Univ Dept Comp Sci Lahore Pakistan Bahauddin Zakariya Univ Dept Comp Sci Multan Pakistan

ISBN: (纸本)9781665400916

Artificial intelligence is the field of computer science and natural language processing is the sub-part method of artificial intelligence. Its major purpose is to remove the transmission gap between human being and computer machine. Their usage in deep learning, machine learning with the help of different algorithms. In this paper, talk about different systems/methods of natural language in the light of multiple six survey-based articles. Defined the basic structure of NLP, their different steps from input sentence to last outcome with the help of figure. Paper represents the NLP techniques, their structure, and their unavoidable importance in the filed of computer science as well as other fields.

关键词： natural language processing Neural Network Sentiment Analysis Semantic Technology Ontologies

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Do Vision-and-language Transformers Learn Grounded Predicate-Noun Dependencies?

Do Vision-and-Language Transformers Learn Grounded Predicate...

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2022 conference on empirical methods in natural language processing, EMNLP 2022

作者： Nikolaus, Mitja Salin, Emmanuelle Ayache, Stephane Fourtassi, Abdellah Favre, Benoit Aix Marseille Univ Université de Toulon CNRS LIS Marseille France Aix-Marseille Univ CNRS LPL Aix-en-Provence France

Recent advances in vision-and-language modeling have seen the development of Transformer architectures that achieve remarkable performance on multimodal reasoning tasks. Yet, the exact capabilities of these black-box models are still poorly understood. While much of previous work has focused on studying their ability to learn meaning at the word-level, their ability to track syntactic dependencies between words has received less attention. We take a first step in closing this gap by creating a new multimodal task targeted at evaluating understanding of predicate-noun dependencies in a controlled setup. We evaluate a range of state-of-the-art models and find that their performance on the task varies considerably, with some models performing relatively well and others at chance level. In an effort to explain this variability, our analyses indicate that the quality (and not only sheer quantity) of pretraining data is essential. Additionally, the best performing models leverage fine-grained multimodal pretraining objectives in addition to the standard image-text matching objectives. This study highlights that targeted and controlled evaluations are a crucial step for a precise and rigorous test of the multimodal knowledge of vision-and-language models. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Evaluation of Semantic Relatedness Measures for Turkish language 17th

Evaluation of Semantic Relatedness Measures for Turkish Lang...

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17th International conference on Intelligent Text processing and Computational Linguistics (CICLing)

作者： Sopaoglu, Ugur Ercan, Gonenc Cankaya Univ Dept Comp Engn Eskisehir Yolu 29 Km Etimesgut Ankara Turkey Hacettepe Univ Inst Informat Ankara Turkey

ISBN: (纸本)9783319754772;9783319754765

The problem of quantifying semantic relatedness level of two words is a fundamental sub-task for many natural language processing systems. While there is a large body of research on measuring semantic relatedness in the English language, the literature lacks detailed analysis for these methods in agglutinative languages. In this research, two new evaluation resources for the Turkish language are constructed. An extensive set of experiments involving multiple tasks: word association, semantic categorization, and automatic WordNet relationship discovery are performed to evaluate different semantic relatedness measures in the Turkish language. As Turkish is an agglutinative language, the morphological processing component is important for distributional similarity algorithms. For languages with rich morphological variations and productivity, methods ranging from simple stemming strategies to morphological disambiguation exists. In our experiments, different morphological processing methods for the Turkish language are investigated.

关键词： Semantic relatedness Distributional similarity Lexical semantics

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