Kuniko Saito


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Automatically Extracting Variant-Normalization Pairs for Japanese Text Normalization
Itsumi Saito | Kyosuke Nishida | Kugatsu Sadamitsu | Kuniko Saito | Junji Tomita
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Social media texts, such as tweets from Twitter, contain many types of non-standard tokens, and the number of normalization approaches for handling such noisy text has been increasing. We present a method for automatically extracting pairs of a variant word and its normal form from unsegmented text on the basis of a pair-wise similarity approach. We incorporated the acquired variant-normalization pairs into Japanese morphological analysis. The experimental results show that our method can extract widely covered variants from large Twitter data and improve the recall of normalization without degrading the overall accuracy of Japanese morphological analysis.


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Grammar Error Correction Using Pseudo-Error Sentences and Domain Adaptation
Kenji Imamura | Kuniko Saito | Kugatsu Sadamitsu | Hitoshi Nishikawa
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Constructing a Class-Based Lexical Dictionary using Interactive Topic Models
Kugatsu Sadamitsu | Kuniko Saito | Kenji Imamura | Yoshihiro Matsuo
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

This paper proposes a new method of constructing arbitrary class-based related word dictionaries on interactive topic models; we assume that each class is described by a topic. We propose a new semi-supervised method that uses the simplest topic model yielded by the standard EM algorithm; model calculation is very rapid. Furthermore our approach allows a dictionary to be modified interactively and the final dictionary has a hierarchical structure. This paper makes three contributions. First, it proposes a word-based semi-supervised topic model. Second, we apply the semi-supervised topic model to interactive learning; this approach is called the Interactive Topic Model. Third, we propose a score function; it extracts the related words that occupy the middle layer of the hierarchical structure. Experiments show that our method can appropriately retrieve the words belonging to an arbitrary class.

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Entity Set Expansion using Interactive Topic Information
Kugatsu Sadamitsu | Kuniko Saito | Kenji Imamura | Yoshihiro Matsuo
Proceedings of the 26th Pacific Asia Conference on Language, Information, and Computation

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Creating an Extended Named Entity Dictionary from Wikipedia
Ryuichiro Higashinaka | Kugatsu Sadamitsu | Kuniko Saito | Toshiro Makino | Yoshihiro Matsuo
Proceedings of COLING 2012


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Entity Set Expansion using Topic information
Kugatsu Sadamitsu | Kuniko Saito | Kenji Imamura | Genichiro Kikui
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


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Tag Confidence Measure for Semi-Automatically Updating Named Entity Recognition
Kuniko Saito | Kenji Imamura
Proceedings of the 2009 Named Entities Workshop: Shared Task on Transliteration (NEWS 2009)

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Discriminative Approach to Predicate-Argument Structure Analysis with Zero-Anaphora Resolution
Kenji Imamura | Kuniko Saito | Tomoko Izumi
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers


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A Clustered Global Phrase Reordering Model for Statistical Machine Translation
Masaaki Nagata | Kuniko Saito | Kazuhide Yamamoto | Kazuteru Ohashi
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics


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Portable Translator Capable of Recognizing Characters on Signboard and Menu Captured by its Built-in Camera
Hideharu Nakajima | Yoshihiro Matsuo | Masaaki Nagata | Kuniko Saito
Proceedings of the ACL Interactive Poster and Demonstration Sessions


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Multi-Language Named-Entity Recognition System based on HMM
Kuniko Saito | Masaaki Nagata
Proceedings of the ACL 2003 Workshop on Multilingual and Mixed-language Named Entity Recognition