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检索条件"任意字段=2nd Workshop on Graph-Based Algorithms for Natural Language Processing, TextGraphs 2007"

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Analysis of the wikipedia category graph for NLP applications

Analysis of the wikipedia category graph for NLP application...

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2nd workshop on graph-based algorithms for natural language processing, textgraphs 2007

作者： Zesch, Torsten Gurevych, Iryna Telecooperation Division Ubiquitous Knowledge Processing Group Darmstadt University of Technology Hochschulstrabe 10 D-64289 Darmstadt Germany

In this paper, we discuss two graphs in Wikipedia (i) the article graph, and (ii) the category graph. We perform a graphtheoretic analysis of the category graph, and show that it is a scale-free, small world graph like other well-known lexical semantic networks. We substantiate our findings by transferring semantic relatedness algorithms defined on WordNet to the Wikipedia category graph. To assess the usefulness of the category graph as an NLP resource, we analyze its coverage and the performance of the transferred semantic relatedness algorithms.

关键词： natural language processing systems

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HLT-NAACL 2007 - textgraphs 2007: graph-based algorithms for natural language processing, Proceedings of the workshop

HLT-NAACL 2007 - TextGraphs 2007: Graph-Based Algorithms for...

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2nd workshop on graph-based algorithms for natural language processing, textgraphs 2007

The proceedings contain 13 papers. The topics discussed include: analysis of the Wikipedia category graph for NLP applications;multi-level association graphs - a new graph-based model for information retrieval;extractive automatic summarization: does more linguistic knowledge make a difference?;time-stamped graphs: evolutionary models of text for multi-document summarization;unigram language models using diffusion smoothing over graphs;transductive structured classification through constrained min-cuts;latent semantic grammar induction: context, projectivity, and prior distributions;learning to transform linguistic graphs;semi-supervised algorithm for human-computer dialogue mining;correlations in the organization of large-scale syntactic dependency networks;DLSITE-2: semantic similarity based on syntactic dependency trees applied to textual entailment;and how difficult is it to develop a perfect spell-checker? a cross-linguistic analysis through complex network approach.

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Unigram language models using diffusion smoothing over graphs

Unigram language models using diffusion smoothing over graph...

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2nd workshop on graph-based algorithms for natural language processing, textgraphs 2007

作者： Jedynak, Bruno Karakos, Damianos Dept. of Appl. Mathematics and Statistics Center for Imaging Sciences Johns Hopkins University Baltimore MD 21218-2686 United States Dept. of Electrical and Computer Engineering Center for Language and Speech Processing Johns Hopkins University Baltimore MD 21218-2686 United States

We propose to use graph-based diffusion techniques with data-dependent kernels to build unigram language models. Our approach entails building graphs, where each vertex corresponds uniquely to a word from a closed vocabulary, and the existence of an edge (with an appropriate weight) between two words indicates some form of similarity between them. In one of our constructions, we place an edge between two words if the number of times these words were seen in a training set differs by at most one count. This graph construction results in a similarity matrix with small intrinsic dimension, since words with the same counts have the same neighbors. Experimental results from a benchmark task from language modeling show that our method is competitive with the Good-Turing estimator.

关键词： graphic methods

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DLSITE-2: Semantic similarity based on syntactic dependency trees applied to textual entailment

DLSITE-2: Semantic similarity based on syntactic dependency ...

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2nd workshop on graph-based algorithms for natural language processing, textgraphs 2007

作者： Micol, Daniel Ferrández, Óscar Muñoz, Rafael Palomar, Manuel Department of Computing Languages and Systems Natural Language Processing and Information Systems Group University of Alicante San Vicente del Raspeig Alicante 03690 Spain

In this paper we attempt to deduce textual entailment based on syntactic dependency trees of a given text-hypothesis pair. The goals of this project are to provide an accurate and fast system, which we have called DLSITE-2, that can be applied in software systems that require a near-realtime interaction with the user. To accomplish this we use MINIPAR to parse the phrases and construct their corresponding trees. Later on we apply syntacticbased techniques to calculate the semantic similarity between text and hypothesis. To measure our method's precision we used the test text corpus set from Second PASCAL Recognising Textual Entailment Challenge (RTE-2), obtaining an accuracy rate of 60.75%.

关键词： Semantics

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ASU at textgraphs 2019 shared task: Explanation regeneration using language models and iterative re-ranking 13

ASU at TextGraphs 2019 shared task: Explanation regeneration...

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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： Banerjee, Pratyay School of Computing Informatics and Decision Systems Engineering Arizona State University United States

ISBN: (纸本)9781950737864

In this work we describe the system from natural language processing group at Arizona State University for the textgraphs 2019 Shared Task. The task focuses on Explanation Regeneration, an intermediate step towards general multi-hop inference on large graphs. Our approach consists of modeling the explanation regeneration task as a learning to rank problem, for which we use state-of-theart language models and explore dataset preparation techniques. We utilize an iterative reranking based approach to further improve the rankings. Our system secured 2nd rank in the task with a mean average precision (MAP) of 41.3% on the test set. © 2019 EMNLP-IJCNLP 2019 - graph-based Methods for natural language processing - Proceedings of the 13th workshop. All rights reserved.

关键词： Iterative methods

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