Deep Learning for Character-Based Information Extraction

Qi, Yanjun; Das, Sujatha; Collobert, Ronan; Weston, Jason

doi:10.1007/978-3-319-06028-6_74

Cited by 21 publications

(12 citation statements)

References 5 publications

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“…The most popular word segmentation methods is based on sequence labeling (Xue, 2003). Recently, researchers have tended to explore neural network based approaches (Collobert et al, 2011) to reduce efforts of feature engineering (Zheng et al, 2013;Pei et al, 2014;Qi et al, 2014;. The features of all these methods are extracted from a local context and neglect the long distance information.…”

Section: Related Workmentioning

confidence: 99%

Long Short-Term Memory Neural Networks for Chinese Word Segmentation

Chen¹,

Qiu²,

Zhu³

et al. 2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

255

232

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Currently most of state-of-the-art methods for Chinese word segmentation are based on supervised learning, whose features are mostly extracted from a local context. These methods cannot utilize the long distance information which is also crucial for word segmentation. In this paper, we propose a novel neural network model for Chinese word segmentation, which adopts the long short-term memory (LSTM) neural network to keep the previous important information in memory cell and avoids the limit of window size of local context. Experiments on PKU, MSRA and CTB6 benchmark datasets show that our model outperforms the previous neural network models and state-of-the-art methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Long Short-Term Memory Neural Networks for Chinese Word Segmentation

Chen¹,

Qiu²,

Zhu³

et al. 2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

255

232

View full text Add to dashboard Cite

show abstract

“…Most modern CWS methods followed (Xue, 2003) treated CWS as a sequence labeling problems (Zhao et al, 2006b). Recently, researchers have tended to explore neural network based approaches (Collobert et al, 2011) to reduce efforts of feature engineering (Zheng et al, 2013;Qi et al, 2014;Chen et al, 2015a;Chen et al, 2015b). They modeled CWS as tagging problem as well, scoring tags on individual characters.…”

Section: Related Workmentioning

confidence: 99%

Neural Word Segmentation Learning for Chinese

Cai¹,

Zhao²

2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

135

129

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Most previous approaches to Chinese word segmentation formalize this problem as a character-based sequence labeling task so that only contextual information within fixed sized local windows and simple interactions between adjacent tags can be captured. In this paper, we propose a novel neural framework which thoroughly eliminates context windows and can utilize complete segmentation history. Our model employs a gated combination neural network over characters to produce distributed representations of word candidates, which are then given to a long shortterm memory (LSTM) language scoring model. Experiments on the benchmark datasets show that without the help of feature engineering as most existing approaches, our models achieve competitive or better performances with previous stateof-the-art methods.

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“…Turian et al (2010),Collobert et al (2011), andQi et al (2014) consider representation learning for coarse label named entity recognition.…”

mentioning

confidence: 99%

Embedding Methods for Fine Grained Entity Type Classification

Yogatama¹,

Gillick²,

Lazic³

2015

Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Confere

107

150

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We propose a new approach to the task of fine grained entity type classifications based on label embeddings that allows for information sharing among related labels. Specifically, we learn an embedding for each label and each feature such that labels which frequently co-occur are close in the embedded space. We show that it outperforms state-of-the-art methods on two fine grained entity-classification benchmarks and that the model can exploit the finer-grained labels to improve classification of standard coarse types.

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Deep Learning for Character-Based Information Extraction

Cited by 21 publications

References 5 publications

Long Short-Term Memory Neural Networks for Chinese Word Segmentation

Long Short-Term Memory Neural Networks for Chinese Word Segmentation

Neural Word Segmentation Learning for Chinese

Embedding Methods for Fine Grained Entity Type Classification

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