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检索条件"任意字段=4th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2009"

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UMR Annotation of Multiword Expressions 4

UMR Annotation of Multiword Expressions

引用

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

ISBN: (纸本)9781959429654

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 sentence to concepts in the UMR graph. Multiword expressions (MWEs) pose a particular challenge to UMR annotation because they deviate from the default one-to-one mapping between words and concepts. there are different types of MWEs which require different kinds of annotation that must be specified in guidelines. this paper discusses the specific treatment for each type of MWE in UMR. ©2023 Association for Computational Linguistics

关键词： graphic methods

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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

ISBN: (纸本)9781952148422

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 Ustalov, 2020). the shared task required participants to develop methods to reconstruct gold explanations for elementary science questions from the WorldTree Corpus (Xie et al., 2020). Although our research was not funded by any organization and all the models were trained on freely available tools like Google Colab which restricted our computational capabilities, we have managed to achieve noteworthy results placing ourselves in the 4th place with a MAP score of 0.49021 in the evaluation leaderboard and 0.5062 MAP score on the post-evaluation-phase leaderboard using RoBERTa. We incorporated some of the methods proposed in the previous edition of textgraphs-13 (Chia et al., 2019), which proved to be very effective, improved upon them, and built a model on top of it using powerful state-of-the-art pre-trained language models like RoBERTa (Liu et al., 2019), BART (Lewis et al., 2020), SciBERT (Beltagy et al., 2019) among others. Further optimization of our work can be done with the availability of better computational resources. © COLING *** rights reserved.

关键词： Computational linguistics

来源：评论

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Sar-graphs: A Linked Linguistic Knowledge Resource Connecting Facts with language 4

Sar-graphs: A Linked Linguistic Knowledge Resource Connectin...

引用

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

ISBN: (纸本)9781941643570

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 existing lexicosemantic resources by modeling syntactic and semantic information at the level of relations, and are hence useful for tasks such as knowledge base population and relation extraction. We present a languageindependent method to automatically construct sar-graph instances that is based on distantly supervised relation extraction. We link sar-graphs at the lexical level to BabelNet, WordNet and UBY, and present our ongoing work on pattern- and relationlevel linking to FrameNet. An initial dataset of English sar-graphs for 25 relations is made publicly available, together with a Java-based API. © 2015 Association for Computational Linguistics and Asian Federation of natural language processing.

关键词： graphic methods

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Cause-effect relation learning

Cause-effect relation learning

引用

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

ISBN: (纸本)9781937284374

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 on cause-effect learning. this paper describes an automated bootstrapping procedure which can learn and produce with minimal effort a cause-effect term repository. To filter out the erroneously extracted information, we incorporate graph-based methods. To evaluate the performance of the acquired cause-effect terms, we conduct three evaluations: (1) human-based, (2) comparison with existing knowledge bases and (3) application driven (SemEval-1 Task 4) in which the goal is to identify the relation between pairs of nominals. the results show that the extractions at rank 1500 are 89% accurate, they comprise 61% from the terms used in the SemEval-1 Task 4 dataset and can be used in the future to produce additional training examples for the same task. © 2012 the Association for Computational Linguistics.

关键词： graphic methods

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Nonparametric Bayesian word sense induction

Nonparametric Bayesian word sense induction

引用

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

ISBN: (纸本)9781937284008

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 parametric Bayesian model employed by Brody and Lapata (2009) for this task. We find that the two models achieve similar levels of induction quality, while the HDP confers the advantage of automatically inducing a variable number of senses per word, as compared to manually fixing the number of senses a priori, as in LDA. this flexibility allows for the model to adapt to terms with greater or lesser polysemy, when evidenced by corpus distributional statistics. When trained on out-of-domain data, experimental results confirm the model's ability to make use of a restricted set of topically coherent induced senses, when then applied in a restricted domain. © 2011 the Association for Computational Linguistics.

关键词： Statistics

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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...

引用

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

ISBN: (纸本)1932432779

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 data obtained from psycholinguistic action naming tasks performed by children and adults (speakers of Brazilian Portuguese), and analyze some characteristics of the verbs used by each group in terms of similarity of content, using Jaccard?s coefficient, and of topology, using graph theory. the experiments suggest that younger children tend to use more frequent verbs than adults to describe events in the world. © 2010 the Association for Computational Linguistics.

关键词： graph theory

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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...

引用

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

ISBN: (纸本)193243254X

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 the words are related semantically. We show that such a structure can be used to distinguish literal and non-literal usages of multi-word expressions.

关键词： graphic methods

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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

ISBN: (纸本)193243254X

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). these have a variety of practical applications, ranging from spam detection to speech recognition, and dialectometrical methods on the social graph. Users should be able to view any content in their language (even if it is spoken by a small population), and to browse our site with appropriately translated interface (automatically generated, for locales with little crowd-sourced community effort).

关键词： Speech recognition

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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...

引用

4th workshop on graph-based methods for natural language processing, textgraphs 2009

ISBN: (纸本)193243254X

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 partitioning to co-cluster varieties and sound correspondences in dialectology;WikiWalk: random walks on Wikipedia for semantic relatedness;classifying Japanese polysemous verbs based on Fuzzy C-means clustering;measuring semantic relatedness with vector space models and random walks;ranking and semi-supervised classification on large scale graphs using Map-Reduce;opinion graphs for polarity and discourse classification;a cohesion graph based approach for unsupervised recognition of literal and non-literal use of multiword expressions;social (distributed) language modeling, clustering and dialectometry;and quantitative analysis of treebanks using frequent subtree mining methods.

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Quantitative analysis of treebanks using frequent subtree mining methods

Quantitative analysis of treebanks using frequent subtree mi...

引用

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

ISBN: (纸本)193243254X

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 complex structured data found in treebanks and in corpora with sophisticated annotation than for tokenized texts. Recent developments in data mining, particularly in the extraction of frequent subtrees from treebanks, offer some solutions. We have applied a modified version of the TreeMiner algorithm to a small treebank and present some promising results.

关键词： Extraction

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