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检索条件"任意字段=4th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2009"
15 条 记 录,以下是1-10 订阅
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UMR Annotation of Multiword Expressions  4
UMR Annotation of Multiword Expressions
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4th Internationalworkshop on Designing Meaning Representations, DMR 2023
作者: Bonn, Julia Cowell, Andrew Hajič, Jan Palmer, Alexis Palmer, Martha Pustejovsky, James Sun, Haibo Urešová, Zdenka Wein, Shira Xue, Nianwen Zhao, Jin University of Colorado Boulder United States Brandeis University United States Charles University Prague Czech Republic Georgetown University United States
Rooted in AMR, Uniform Meaning Representation (UMR) is a graph-based formalism with nodes as concepts and edges as relations between them. When used to represent natural language semantics, UMR maps words in a sentenc... 详细信息
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ChiSquareX at textgraphs 2020 Shared Task: Leveraging Pre-trained language Models for Explanation Regeneration  14
ChiSquareX at TextGraphs 2020 Shared Task: Leveraging Pre-tr...
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14th workshop on graph-based methods for natural language processing, textgraphs 2020, in conjunction with the 28th International Conference on Computational Linguistics, COLING 2020
作者: Pawate, Aditya Girish Chandak, Devansh Madhavan, Varun IIT Kharagpur India IIT Bombay India
In this work, we describe the system developed by a group of undergraduates from the Indian Institutes of Technology, for the Shared Task at textgraphs-14 on Multi-Hop Inference Explanation Regeneration (Jansen and Us... 详细信息
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Sar-graphs: A Linked Linguistic Knowledge Resource Connecting Facts with language  4
Sar-graphs: A Linked Linguistic Knowledge Resource Connectin...
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4th workshop on Linked Data in Linguistics: Resources and Applications, LDL 2015
作者: Krause, Sebastian Hennig, Leonhard Gabryszak, Aleksandra Xu, Feiyu Uszkoreit, Hans DFKI Language Technology Lab Berlin Germany
We present sar-graphs, a knowledge resource that links semantic relations from factual knowledge graphs to the linguistic patterns with which a language can express instances of these relations. Sar-graphs expand upon... 详细信息
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Cause-effect relation learning
Cause-effect relation learning
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7th workshop on graph-based methods for natural language processing, textgraphs 2012
作者: Kozareva, Zornitsa USC Information Sciences Institute 4676 Admiralty Way Marina del Rey CA United States
To be able to answer the question What causes tumors to shrink?, one would require a large cause-effect relation repository. Many efforts have been payed on is-a and part-of relation leaning, however few have focused ... 详细信息
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Nonparametric Bayesian word sense induction
Nonparametric Bayesian word sense induction
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6th workshop on graph-based methods for natural language processing, textgraphs 2011
作者: Yao, Xuchen Durme, Benjamin Van Department of Computer Science Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States
We propose the use of a nonparametric Bayesian model, the Hierarchical Dirichlet Process (HDP), for the task of Word Sense Induction. Results are shown through comparison against Latent Dirichlet Allocation (LDA), a p... 详细信息
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An investigation on the influence of frequency on the lexical organization of verbs  48
An investigation on the influence of frequency on the lexica...
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5th workshop on graph-based methods for natural language processing, textgraphs 2010
作者: Germann, Daniel Cerato Villavicencio, Aline Siqueira, Maity Institute of Informatics Federal University of Rio Grande do Sul Brazil Department of Computer Sciences Bath University United Kingdom Institute of Language Studies Federal University of Rio Grande do Sul Brazil
this work extends the study of Germann et al. (2010) in investigating the lexical organization of verbs. Particularly, we look at the influence of frequency on the process of lexical acquis ition and use. We examine d... 详细信息
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Cohesion graph based approach for unsupervised recognition of literal and non-literal use of multiword expressions
Cohesion graph based approach for unsupervised recognition o...
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4th workshop on graph-based methods for natural language processing, textgraphs 2009
作者: Li, Linlin Sporleder, Caroline Saarland University Postfach 15 11 50 66041 Saarbrücken Germany Germany
We present a graph-based model for representing the lexical cohesion of a discourse. In the graph structure, vertices correspond to the content words of a text and edges connecting pairs of words encode how closely th... 详细信息
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Social (distributed) language modeling, clustering and dialectometry
Social (distributed) language modeling, clustering and diale...
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4th workshop on graph-based methods for natural language processing, textgraphs 2009
作者: Ellis, David Facebook Palo Alto CA United States
We present ongoing work in a scalable, distributed implementation of over 200 million individual language models, each capturing a single user's dialect in a given language (multilingual users have several models)... 详细信息
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ACL-IJCNLP 2009 - textgraphs 2009: 2009 workshop on graph-based methods for natural language processing, Proceedings of the workshop
ACL-IJCNLP 2009 - TextGraphs 2009: 2009 Workshop on Graph-Ba...
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4th workshop on graph-based methods for natural language processing, textgraphs 2009
the proceedings contain 12 papers. the topics discussed include: network analysis reveals structure indicative of syntax in the corpus of undeciphered Indus civilization inscriptions;bipartite spectral graph partition...
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Quantitative analysis of treebanks using frequent subtree mining methods
Quantitative analysis of treebanks using frequent subtree mi...
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4th workshop on graph-based methods for natural language processing, textgraphs 2009
作者: Martens, Scott Centrum Voor Computerlinguïstiek KU Leuven Blijde-Inkomststraat 13 3000 Leuven Belgium
the first task of statistical computational linguistics, or any other type of datadriven processing of language, is the extraction of counts and distributions of phenomena. this is much more difficult for the type of ... 详细信息
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