Tsuneaki Kato


2019

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Bridging the Defined and the Defining: Exploiting Implicit Lexical Semantic Relations in Definition Modeling
Koki Washio | Satoshi Sekine | Tsuneaki Kato
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Definition modeling includes acquiring word embeddings from dictionary definitions and generating definitions of words. While the meanings of defining words are important in dictionary definitions, it is crucial to capture the lexical semantic relations between defined words and defining words. However, thus far, the utilization of such relations has not been explored for definition modeling. In this paper, we propose definition modeling methods that use lexical semantic relations. To utilize implicit semantic relations in definitions, we use unsupervisedly obtained pattern-based word-pair embeddings that represent semantic relations of word pairs. Experimental results indicate that our methods improve the performance in learning embeddings from definitions, as well as definition generation.

2018

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Filling Missing Paths: Modeling Co-occurrences of Word Pairs and Dependency Paths for Recognizing Lexical Semantic Relations
Koki Washio | Tsuneaki Kato
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Recognizing lexical semantic relations between word pairs is an important task for many applications of natural language processing. One of the mainstream approaches to this task is to exploit the lexico-syntactic paths connecting two target words, which reflect the semantic relations of word pairs. However, this method requires that the considered words co-occur in a sentence. This requirement is hardly satisfied because of Zipf’s law, which states that most content words occur very rarely. In this paper, we propose novel methods with a neural model of P(path|w1,w2) to solve this problem. Our proposed model of P (path|w1, w2 ) can be learned in an unsupervised manner and can generalize the co-occurrences of word pairs and dependency paths. This model can be used to augment the path data of word pairs that do not co-occur in the corpus, and extract features capturing relational information from word pairs. Our experimental results demonstrate that our methods improve on previous neural approaches based on dependency paths and successfully solve the focused problem.

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Neural Latent Relational Analysis to Capture Lexical Semantic Relations in a Vector Space
Koki Washio | Tsuneaki Kato
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Capturing the semantic relations of words in a vector space contributes to many natural language processing tasks. One promising approach exploits lexico-syntactic patterns as features of word pairs. In this paper, we propose a novel model of this pattern-based approach, neural latent relational analysis (NLRA). NLRA can generalize co-occurrences of word pairs and lexico-syntactic patterns, and obtain embeddings of the word pairs that do not co-occur. This overcomes the critical data sparseness problem encountered in previous pattern-based models. Our experimental results on measuring relational similarity demonstrate that NLRA outperforms the previous pattern-based models. In addition, when combined with a vector offset model, NLRA achieves a performance comparable to that of the state-of-the-art model that exploits additional semantic relational data.

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Undersampling Improves Hypernymy Prototypicality Learning
Koki Washio | Tsuneaki Kato
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2006

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WoZ Simulation of Interactive Question Answering
Tsuneaki Kato | Jun’ichi Fukumoto | Fumito Masui | Noriko Kando
Proceedings of the Interactive Question Answering Workshop at HLT-NAACL 2006

2004

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Handling Information Access Dialogue through QA Technologies - A novel challenge for open-domain question answering
Tsuneaki Kato | Jun’ichi Fukumoto | Fumito Masui | Noriko Kando
Proceedings of the Workshop on Pragmatics of Question Answering at HLT-NAACL 2004

2002

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Answering it with Charts: Dialogue in Natural Language and Charts
Tsuneaki Kato | Mitsunori Matsushita | Eisaku Maeda
COLING 2002: The 19th International Conference on Computational Linguistics

1998

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Exploring the Characteristics of Multi-Party Dialogues
Masato Ishizaki | Tsuneaki Kato
COLING 1998 Volume 1: The 17th International Conference on Computational Linguistics

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Exploring the Characteristics of Multi-party Dialogues
Masato Ishizaki | Tsuneaki Kato
36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, Volume 1

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Cue Phrase Selection in Instruction Dialogue Using Machine Learning
Yukiko I. Nakano | Tsuneaki Kato
Discourse Relations and Discourse Markers

1997

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Towards Generation of Fluent Referring Action in Multimodal Situations
Tsuneaki Kato | Yukiko I. Nakano
Referring Phenomena in a Multimedia Context and their Computational Treatment

1994

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Yet Another Chart-Based Technique for Parsing Ill-Formed Input
Tsuneaki Kato
Fourth Conference on Applied Natural Language Processing