Intra-Sentential Subject Zero Anaphora Resolution using Multi-Column
            Convolutional Neural Network

Iida, Ryu; Torisawa, Kentaro; Oh, Jong–Hoon; Kruengkrai, Canasai; Kloetzer, Julien

doi:10.18653/v1/d16-1132

Cited by 28 publications

(30 citation statements)

References 30 publications

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“…In Shibata et al (2016), a feed-forward neural network is used for the score calculation part of the joint model proposed by Ouchi et al (2015). In Iida et al (2016), multi-column convolutional neural networks are used for the zero anaphora resolution task.…”

Section: Neural Approachesmentioning

confidence: 99%

Neural Modeling of Multi-Predicate Interactions for Japanese Predicate Argument Structure Analysis

Ouchi

Shindo

Matsumoto

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

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The performance of Japanese predicate argument structure (PAS) analysis has improved in recent years thanks to the joint modeling of interactions between multiple predicates. However, this approach relies heavily on syntactic information predicted by parsers, and suffers from error propagation. To remedy this problem, we introduce a model that uses grid-type recurrent neural networks. The proposed model automatically induces features sensitive to multi-predicate interactions from the word sequence information of a sentence. Experiments on the NAIST Text Corpus demonstrate that without syntactic information, our model outperforms previous syntax-dependent models.

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Section: Neural Approachesmentioning

confidence: 99%

Neural Modeling of Multi-Predicate Interactions for Japanese Predicate Argument Structure Analysis

Ouchi

Shindo

Matsumoto

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

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“…This is also included in zero anaphora resolution. Except these special arguments of exophora, we focus on intra-sentential anaphora resolution in the same way as (Shibata et al, 2016;Iida et al, 2016;Ouchi et al, 2017;Matsubayashi and Inui, 2017). We also attach NULL labels to cases that predicates do not have.…”

Section: Corpusmentioning

confidence: 99%

Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis

Kurita

Kawahara

Kurohashi

2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Japanese predicate-argument structure (PAS) analysis involves zero anaphora resolution, which is notoriously difficult. To improve the performance of Japanese PAS analysis, it is straightforward to increase the size of corpora annotated with PAS. However, since it is prohibitively expensive, it is promising to take advantage of a large amount of raw corpora. In this paper, we propose a novel Japanese PAS analysis model based on semi-supervised adversarial training with a raw corpus.In our experiments, our model outperforms existing state-of-the-art models for Japanese PAS analysis.

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“…Recently, supervised approaches have been widely exploited for ZP resolution in Korean (Han, 2006), Italian (Iida and Poesio, 2011) and Japanese (Isozaki and Hirao, 2003;Iida et al, 2006Iida et al, , 2007Imamura et al, 2009;Sasano and Kurohashi, 2011;Iida and Poesio, 2011;Iida et al, 2015). Iida et al (2016) propose a multi-column convolutional neural network for Japanese intra-sentential subject zero anaphora resolution, where both the surface word sequence and dependency tree of a target sentence are exploited as clues in their model.…”

Section: Zero Pronoun Resolutionmentioning

confidence: 99%

Chinese Zero Pronoun Resolution with Deep Memory Network

Yin

Zhang

et al. 2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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Existing approaches for Chinese zero pronoun resolution typically utilize only syntactical and lexical features while ignoring semantic information. The fundamental reason is that zero pronouns have no descriptive information, which brings difficulty in explicitly capturing their semantic similarities with antecedents. Meanwhile, representing zero pronouns is challenging since they are merely gaps that convey no actual content. In this paper, we address this issue by building a deep memory network that is capable of encoding zero pronouns into vector representations with information obtained from their contexts and potential antecedents. Consequently, our resolver takes advantage of semantic information by using these continuous distributed representations. Experiments on the OntoNotes 5.0 dataset show that the proposed memory network could substantially outperform the state-of-the-art systems in various experimental settings.

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Intra-Sentential Subject Zero Anaphora Resolution using Multi-Column Convolutional Neural Network

Cited by 28 publications

References 30 publications

Neural Modeling of Multi-Predicate Interactions for Japanese Predicate Argument Structure Analysis

Neural Modeling of Multi-Predicate Interactions for Japanese Predicate Argument Structure Analysis

Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis

Chinese Zero Pronoun Resolution with Deep Memory Network

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