A Deep Learning Framework for Coreference Resolution Based on Convolutional Neural Network

Wu, Jheng-Long; Ma, Wei-Yun

doi:10.1109/icsc.2017.57

Cited by 16 publications

(17 citation statements)

References 16 publications

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“…Coreference resolution Among neural coreference resolvers (Wu and Ma, 2017;Meng and Rumshisky, 2018), Lee et al (2017) were the first to propose an end-to-end resolver which did not rely on hand-crafted rules or a syntactic parser. Extending this work, introduced a novel attention mechanism for iteratively ranking spans of candidate coreferent mentions, thereby improving the identification of long distance coreference chains.…”

Section: Related Workmentioning

confidence: 99%

Rewarding Coreference Resolvers for Being Consistent with World Knowledge

Aralikatte¹,

Lent²,

Gonzalez³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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Unresolved coreference is a bottleneck for relation extraction, and high-quality coreference resolvers may produce an output that makes it a lot easier to extract knowledge triples. We show how to improve coreference resolvers by forwarding their input to a relation extraction system and reward the resolvers for producing triples that are found in knowledge bases. Since relation extraction systems can rely on different forms of supervision and be biased in different ways, we obtain the best performance, improving over the state of the art, using multi-task reinforcement learning.

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Section: Related Workmentioning

confidence: 99%

Rewarding Coreference Resolvers for Being Consistent with World Knowledge

Aralikatte¹,

Lent²,

Gonzalez³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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show abstract

“…Moving on to related works on coreference resolution for English text, this work is most similar to [5]. They used deep neural network and CNN for learning mentions and their head words representations.…”

Section: Suherik and Purwariantimentioning

confidence: 99%

“…The difference between our architectures and the ones in [5] is that they used mentions dependency head words. We do not use this feature due to the limited NLP tools for Indonesian language.…”

Section: Neural Network Architecturementioning

confidence: 99%

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Coreference Resolution System for Indonesian Text with Mention Pair Method and Singleton Exclusion using Convolutional Neural Network

Auliarachman

Purwarianti

2019

2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)

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Neural network has shown promising performance on coreference resolution systems that uses mention pair method. With deep neural network, it can learn hidden and deep relations between two mentions. However, there is no work on coreference resolution for Indonesian text that uses this learning technique. The state-of-the-art system for Indonesian text only states the use of lexical and syntactic features can improve the existing coreference resolution system. In this paper, we propose a new coreference resolution system for Indonesian text with mention pair method that uses deep neural network to learn the relations of the two mentions. In addition to lexical and syntactic features, in order to learn the representation of the mentions' words and context, we use word embeddings and feed them to Convolutional Neural Network (CNN). Furthermore, we do singleton exclusion using singleton classifier component to prevent singleton mentions enter any entity clusters at the end. Achieving 67.37% without singleton exclusion, 63.27% with trained singleton classifier, and 75.95% with gold singleton classifier on CoNLL average F1 score, our proposed system outperforms the state-of-the-art system.

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“…Since our task is similar to coreference resolution, we take a similar approach to feature engineering by building mention and cluster embeddings with word embeddings (Clark and Manning, 2016) and include additional mention features described by Wiseman et al (2015). We are motivated to use convolutional networks through the work of Wu and Ma (2017), but we distinguish our approach by using deep convolution to build embeddings for character identification. Entity linking has traditionally relied heavily on knowledge databases, most notably, Wikipedia, for entities (Mihalcea and Csomai, 2007b;Ratinov et al, 2011b;Gattani et al, 2013;Francis-Landau et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Robust Coreference Resolution and Entity Linking on Dialogues: Character Identification on TV Show Transcripts

Chen¹,

Zhou²,

Choi³

2017

Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

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This paper presents a novel approach to character identification, that is an entity linking task that maps mentions to characters in dialogues from TV show transcripts. We first augment and correct several cases of annotation errors in an existing corpus so the corpus is clearer and cleaner for statistical learning. We also introduce the agglomerative convolutional neural network that takes groups of features and learns mention and mention-pair embeddings for coreference resolution. We then propose another neural model that employs the embeddings learned and creates cluster embeddings for entity linking. Our coreference resolution model shows comparable results to other state-of-the-art systems. Our entity linking model significantly outperforms the previous work, showing the F1 score of 86.76% and the accuracy of 95.30% for character identification.

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A Deep Learning Framework for Coreference Resolution Based on Convolutional Neural Network

Cited by 16 publications

References 16 publications

Rewarding Coreference Resolvers for Being Consistent with World Knowledge

Rewarding Coreference Resolvers for Being Consistent with World Knowledge

Coreference Resolution System for Indonesian Text with Mention Pair Method and Singleton Exclusion using Convolutional Neural Network

Robust Coreference Resolution and Entity Linking on Dialogues: Character Identification on TV Show Transcripts

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