Yoshihiro Matsuo


2016

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Name Translation based on Fine-grained Named Entity Recognition in a Single Language
Kugatsu Sadamitsu | Itsumi Saito | Taichi Katayama | Hisako Asano | Yoshihiro Matsuo
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We propose named entity abstraction methods with fine-grained named entity labels for improving statistical machine translation (SMT). The methods are based on a bilingual named entity recognizer that uses a monolingual named entity recognizer with transliteration. Through experiments, we demonstrate that incorporating fine-grained named entities into statistical machine translation improves the accuracy of SMT with more adequate granularity compared with the standard SMT, which is a non-named entity abstraction method.

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Analyzing Post-dialogue Comments by Speakers – How Do Humans Personalize Their Utterances in Dialogue? –
Toru Hirano | Ryuichiro Higashinaka | Yoshihiro Matsuo
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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Towards an Entertaining Natural Language Generation System: Linguistic Peculiarities of Japanese Fictional Characters
Chiaki Miyazaki | Toru Hirano | Ryuichiro Higashinaka | Yoshihiro Matsuo
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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A Hierarchical Neural Network for Information Extraction of Product Attribute and Condition Sentences
Yukinori Homma | Kugatsu Sadamitsu | Kyosuke Nishida | Ryuichiro Higashinaka | Hisako Asano | Yoshihiro Matsuo
Proceedings of the Open Knowledge Base and Question Answering Workshop (OKBQA 2016)

This paper describes a hierarchical neural network we propose for sentence classification to extract product information from product documents. The network classifies each sentence in a document into attribute and condition classes on the basis of word sequences and sentence sequences in the document. Experimental results showed the method using the proposed network significantly outperformed baseline methods by taking semantic representation of word and sentence sequential data into account. We also evaluated the network with two different product domains (insurance and tourism domains) and found that it was effective for both the domains.

2015

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Discourse Relation Recognition by Comparing Various Units of Sentence Expression with Recursive Neural Network
Atsushi Otsuka | Toru Hirano | Chiaki Miyazaki | Ryo Masumura | Ryuichiro Higashinaka | Toshiro Makino | Yoshihiro Matsuo
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

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Automatic conversion of sentence-end expressions for utterance characterization of dialogue systems
Chiaki Miyazaki | Toru Hirano | Ryuichiro Higashinaka | Toshiro Makino | Yoshihiro Matsuo
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

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Classification and Acquisition of Contradictory Event Pairs using Crowdsourcing
Yu Takabatake | Hajime Morita | Daisuke Kawahara | Sadao Kurohashi | Ryuichiro Higashinaka | Yoshihiro Matsuo
Proceedings of the The 3rd Workshop on EVENTS: Definition, Detection, Coreference, and Representation

2014

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Towards an open-domain conversational system fully based on natural language processing
Ryuichiro Higashinaka | Kenji Imamura | Toyomi Meguro | Chiaki Miyazaki | Nozomi Kobayashi | Hiroaki Sugiyama | Toru Hirano | Toshiro Makino | Yoshihiro Matsuo
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Learning to Generate Coherent Summary with Discriminative Hidden Semi-Markov Model
Hitoshi Nishikawa | Kazuho Arita | Katsumi Tanaka | Tsutomu Hirao | Toshiro Makino | Yoshihiro Matsuo
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Morphological Analysis for Japanese Noisy Text based on Character-level and Word-level Normalization
Itsumi Saito | Kugatsu Sadamitsu | Hisako Asano | Yoshihiro Matsuo
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Constructing a Corpus of Japanese Predicate Phrases for Synonym/Antonym Relations
Tomoko Izumi | Tomohide Shibata | Hisako Asano | Yoshihiro Matsuo | Sadao Kurohashi
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We construct a large corpus of Japanese predicate phrases for synonym-antonym relations. The corpus consists of 7,278 pairs of predicates such as “receive-permission (ACC)” vs. “obtain-permission (ACC)”, in which each predicate pair is accompanied by a noun phrase and case information. The relations are categorized as synonyms, entailment, antonyms, or unrelated. Antonyms are further categorized into three different classes depending on their aspect of oppositeness. Using the data as a training corpus, we conduct the supervised binary classification of synonymous predicates based on linguistically-motivated features. Combining features that are characteristic of synonymous predicates with those that are characteristic of antonymous predicates, we succeed in automatically identifying synonymous predicates at the high F-score of 0.92, a 0.4 improvement over the baseline method of using the Japanese WordNet. The results of an experiment confirm that the quality of the corpus is high enough to achieve automatic classification. To the best of our knowledge, this is the first and the largest publicly available corpus of Japanese predicate phrases for synonym-antonym relations.

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Extraction of Daily Changing Words for Question Answering
Kugatsu Sadamitsu | Ryuichiro Higashinaka | Yoshihiro Matsuo
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

This paper proposes a method for extracting Daily Changing Words (DCWs), words that indicate which questions are real-time dependent. Our approach is based on two types of template matching using time and named entity slots from large size corpora and adding simple filtering methods from news corpora. Extracted DCWs are utilized for detecting and sorting real-time dependent questions. Experiments confirm that our DCW method achieves higher accuracy in detecting real-time dependent questions than existing word classes and a simple supervised machine learning approach.

2013

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A Pilot Study of Readability Prediction with Reading Time
Hitoshi Nishikawa | Toshiro Makino | Yoshihiro Matsuo
Proceedings of the Second Workshop on Predicting and Improving Text Readability for Target Reader Populations

2012

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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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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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Text Summarization Model based on Redundancy-Constrained Knapsack Problem
Hitoshi Nishikawa | Tsutomu Hirao | Toshiro Makino | Yoshihiro Matsuo
Proceedings of COLING 2012: Posters

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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

2010

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Optimizing Informativeness and Readability for Sentiment Summarization
Hitoshi Nishikawa | Takaaki Hasegawa | Yoshihiro Matsuo | Genichiro Kikui
Proceedings of the ACL 2010 Conference Short Papers

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Recognizing Relation Expression between Named Entities based on Inherent and Context-dependent Features of Relational words
Toru Hirano | Hisako Asano | Yoshihiro Matsuo | Genichiro Kikui
Coling 2010: Posters

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Opinion Summarization with Integer Linear Programming Formulation for Sentence Extraction and Ordering
Hitoshi Nishikawa | Takaaki Hasegawa | Yoshihiro Matsuo | Genichiro Kikui
Coling 2010: Posters

2007

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Detecting Semantic Relations between Named Entities in Text Using Contextual Features
Toru Hirano | Yoshihiro Matsuo | Genichiro Kikui
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions

2005

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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

2000

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Learning Semantic-Level Information Extraction Rules by Type-Oriented ILP
Yutaka Sasaki | Yoshihiro Matsuo
COLING 2000 Volume 2: The 18th International Conference on Computational Linguistics

1999

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Extraction of translation equivalents from non-parallel corpora
Takaaki Tanaka | Yoshihiro Matsuo
Proceedings of the 8th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages