2015 MorphologicalAnalysisforUnsegme

• (Morita et al., 2015) ⇒ Hajime Morita, Daisuke Kawahara, and Sadao Kurohashi. (2015). “Morphological Analysis for Unsegmented Languages Using Recurrent Neural Network Language Model.” In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing.

Subject Headings: Recurrent Neural Network Language Model, Morphological Analysis Task, Recurrent Neural Network Language Model (RNNLM) Morphological Analysis Task, Recurrent Neural Network Language Model (RNNLM) Morphological Analysis System, Recurrent Neural Network Language Model (RNNLM) Morphological Analysis Algorithm.

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Morphological+Analysis+for+Unsegmented+Languages+Using+Recurrent+Neural+Network+Language+Model

Quotes

Abstract

We present a new morphological analysis model that considers semantic plausibility of word sequences by using a recurrent neural network language model (RNNLM). In unsegmented languages, since language models are learned from automatically segmented texts and inevitably contain errors, it is not apparent that conventional language models contribute to morphological analysis. To solve this problem, we do not use language models based on raw word sequences but use a semantically generalized language model, RNNLM, in morphological analysis. In our experiments on two Japanese corpora, our proposed model significantly outperformed baseline models. This result indicates the effectiveness of RNNLM in morphological analysis.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 MorphologicalAnalysisforUnsegme	Hajime Morita Daisuke Kawahara Sadao Kurohashi			Morphological Analysis for Unsegmented Languages Using Recurrent Neural Network Language Model