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检索条件"任意字段=5th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2010"

共 16 条记录，以下是1-10 订阅

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textgraphs 2010 - 2010 workshop on graph-based methods for natural language processing at the 48th Annual Meeting of the Association for Computational Linguistics, ACL 2010 - Proceedings of the workshop

TextGraphs 2010 - 2010 Workshop on Graph-Based Methods for N...

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2010 workshop on graph-based methods for natural language processing, textgraphs 2010 at the 48th Annual Meeting of the Association for Computational Linguistics, ACL 2010 - Proceedings of the workshop

ISBN: (纸本)1932432779

the proceedings contain 16 papers. the topics discussed include: graph-based clustering for computational linguistics: a survey;towards the automatic creation of a wordnet from a term-based lexical network;an investigation on the influence of frequency on the lexical organization of verbs;robust and efficient page rank for word sense disambiguation;hierarchical spectral partitioning of bipartite graphs to cluster dialects and identify distinguishing features;a character-based intersection graph approach to linguistic phylogeny;spectral approaches to learning in the graph domain;and cross-lingual comparison between distributionally determined word similarity networks.

关键词： Computational linguistics

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graph-based clustering for computational linguistics: A survey 48

Graph-based clustering for computational linguistics: A surv...

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5th workshop on graph-based methods for natural language processing, textgraphs 2010

作者： Chen, Zheng Ji, Heng Graduate Center City University of New York United States Graduate Center Queens College City University of New York United States

ISBN: (纸本)1932432779

In this survey we overview graph-based clustering and its applications in computational linguistics. We summarize graph-based clustering as a five-part story: hypothesis, modeling, measure, algorithm and evaluation. We then survey three typical NLP problems in which graph-based clustering approaches have been successfully applied. Finally, we comment on the strengths and weaknesses of graph-based clustering and envision that graph-based clustering is a promising solution for some emerging NLP problems. © 2010 the Association for Computational Linguistics.

关键词： natural language processing systems

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Robust and efficient page rank for word sense disambiguation 48

Robust and efficient page rank for word sense disambiguation

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5th workshop on graph-based methods for natural language processing, textgraphs 2010

作者： De Cao, Diego Basili, Roberto Luciani, Matteo Mesiano, Francesco Rossi, Riccardo Dept. of Computer Science University of Roma Tor Vergata Rome Italy

ISBN: (纸本)1932432779

graph-based methods that are en vogue in the social network analysis area, such as centrality models, have been recently applied to linguistic knowledge bases, including unsupervised Word Sense Disambiguation. Although the achievable accuracy is rather high, the main drawback of these methods is the high computational demanding whenever applied to the large scale sense repositories. In this paper an adaptation of the PageRank algorithm recently proposed for Word Sense Disambiguation is presented that preserves the reachable accuracy while significantly reducing the requested processing time. Experimental analysis over well-known benchmarks will be presented in the paper and the results confirm our hypothesis. © 2010 the Association for Computational Linguistics.

关键词： graphic methods

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ACL 2010 - textgraphs 2010: 2010 workshop on graph-based methods for natural language processing, Proceedings of the workshop

ACL 2010 - TextGraphs 2010: 2010 Workshop on Graph-Based Met...

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5th workshop on graph-based methods for natural language processing, textgraphs 2010

ISBN: (纸本)1932432779

the proceedings contain 17 papers. the topics discussed include: graph-based clustering for computational linguistics: a survey;towards the automatic creation of a wordnet from a term-based lexical network;an investigation on the influence of frequency on the lexical organization of verbs;robust and efficient page rank for word sense disambiguation;hierarchical spectral partitioning of bipartite graphs to cluster dialects and identify distinguishing features;a character-based intersection graph approach to linguistic phylogeny;spectral approaches to learning in the graph domain;cross-lingual comparison between distributionally determined word similarity networks;co-occurrence cluster features for lexical substitutions in context;contextually-mediated semantic similarity graphs for topic segmentation;and experiments with CST-based multidocument summarization.

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Co-occurrence cluster features for lexical substitutions in context 48

Co-occurrence cluster features for lexical substitutions in ...

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5th workshop on graph-based methods for natural language processing, textgraphs 2010

作者： Biemann, Chris 475 Brannan St Ste. 330 San Francisco CA 94107 United States

ISBN: (纸本)1932432779

this paper examines the influence of features based on clusters of co-occurrences for supervised Word Sense Disambiguation and Lexical Substitution. Cooccurrence cluster features are derived from clustering the local neighborhood of a target word in a co-occurrence graph based on a corpus in a completely unsupervised fashion. Clusters can be assigned in context and are used as features in a supervised WSD system. Experiments fitting a strong baseline system with these additional features are conducted on two datasets, showing improvements. Cooccurrence features are a simple way to mimic Topic Signatures (Mart´inez et al., 2008) without needing to construct resources manually. Further, a system is described that produces lexical substitutions in context with very high precision. © 2010 the Association for Computational Linguistics.

关键词： graphic methods

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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

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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Word sense induction by community detection

Word sense induction by community detection

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6th workshop on graph-based methods for natural language processing, textgraphs 2011

作者： Jurgens, David HRL Laboratories LLC Malibu CA United States Department of Computer Science Malibu University of California Los Angeles United States

ISBN: (纸本)9781937284008

Word Sense Induction (WSI) is an unsupervised approach for learning the multiple senses of a word. graph-based approaches to WSI frequently represent word co-occurrence as a graph and use the statistical properties of the graph to identify the senses. We reinterpret graph-basedWSI as community detection, a well studied problem in network science. the relations in the co-occurrence graph give rise to word communities, which distinguish senses. Our results show competitive performance on the SemEval-2010WSI Task. © 2011 the Association for Computational Linguistics.

关键词： graphic methods

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graph-based semi-supervised learning for natural language understanding 13

Graph-based semi-supervised learning for natural language un...

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13th workshop on graph-based methods for natural language processing, textgraphs 2019, in conjunction with the 2019 Conference on Empirical methods in natural language processing and 9th International Joint Conference on natural language processing, EMNLP-IJCNLP 2019

作者： Qiu, Zimeng Cho, Eunah Ma, Xiaochun Campbell, William M. Electrical and Computer Engineering Department Carnegie Mellon University United States Amazon Alexa AI

ISBN: (纸本)9781950737864

Semi-supervised learning is an efficient method to augment training data automatically from unlabeled data. Development of many natural language understanding (NLU) applications has a challenge where unlabeled data is relatively abundant while labeled data is rather limited. In this work, we propose transductive graphbased semi-supervised learning models as well as their inductive variants for NLU. We evaluate the approach's applicability using publicly available NLU data and models. In order to find similar utterances and construct a graph, we use a paraphrase detection model. Results show that applying the inductive graph-based semi-supervised learning can improve the error rate of the NLU model by 5%. © 2019 EMNLP-IJCNLP 2019 - graph-based methods for natural language processing - Proceedings of the 13th workshop. All rights reserved.

关键词： graphic methods

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A graph based semi-supervised approach for analysis of derivational nouns in Sanskrit 11

A graph based semi-supervised approach for analysis of deriv...

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11th workshop on graph-based methods for natural language processing, textgraphs 2017, in conjunction with the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017

作者： Krishna, Amrith Satuluri, Pavankumar Ponnada, Harshavardhan Ahmed, Muneeb Arora, Gulab Hiware, Kaustubh Goyal, Pawan School of Linguistics and Literary Studies Chinmaya Vishwavidyapeeth CEG Campus India Dept. of Electrical Engineering Indian Institute of Technology BHU India Dept. of Computer Science and Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9781945626609

Derivational nouns are widely used in Sanskrit corpora and is a prevalent means of productivity in the language. Currently there exists no analyser that identifies the derivational nouns. We propose a semi supervised approach for identification of derivational nouns in Sanskrit. We not only identify the derivational words, but also link them to their corresponding source words. the novelty of our work is primarily in its design of the network structure for the task. the edge weights are featurised based on the phonetic, morphological, syntactic and the semantic similarity shared between the words to be identified. We find that our model is effective for the task, even when we employ a labelled dataset which is only 5 % to that of the entire dataset. © 2017 Association for Computational Linguistics

关键词： graphic methods

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Understanding seed selection in bootstrapping 8

Understanding seed selection in bootstrapping

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8th workshop on graph-based methods for natural language processing, textgraphs 2013, at the Conference on Empirical methods in natural language processing, EMNLP 2013

作者： Ehara, Yo Sato, Issei Oiwa, Hidekazu Nakagawa, Hiroshi Graduate School of Information Science and Technology United States Information Technology Center University of Tokyo / 7-3-1 Hongo Bunkyo-ku Tokyo Japan JSPS Research Fellow Kojimachi Business Center Building 5-3-1 Kojimachi Chiyoda-ku Tokyo Japan

ISBN: (纸本)9781937284978

Bootstrapping has recently become the focus of much attention in natural language processing to reduce labeling cost. In bootstrapping, unlabeled instances can be harvested from the initial labeled "seed" set. the selected seed set affects accuracy, but how to select a good seed set is not yet clear. thus, an "iterative seeding" framework is proposed for bootstrapping to reduce its labeling cost. Our framework iteratively selects the unlabeled instance that has the best "goodness of seed" and labels the unlabeled instance in the seed set. Our framework deepens understanding of this seeding process in bootstrapping by deriving the dual problem. We propose a method called expected model rotation (EMR) that works well on not well-separated data which frequently occur as realistic data. Experimental results show that EMR can select seed sets that provide significantly higher mean reciprocal rank on realistic data than existing naive selection methods or random seed sets. © 2013 Association for Computational Linguistics

关键词： Iterative methods

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