Tomohide Shibata


2020

pdf bib
Diverse and Non-redundant Answer Set Extraction on Community QA based on DPPs
Shogo Fujita | Tomohide Shibata | Manabu Okumura
Proceedings of the 28th International Conference on Computational Linguistics

In community-based question answering (CQA) platforms, it takes time for a user to get useful information from among many answers. Although one solution is an answer ranking method, the user still needs to read through the top-ranked answers carefully. This paper proposes a new task of selecting a diverse and non-redundant answer set rather than ranking the answers. Our method is based on determinantal point processes (DPPs), and it calculates the answer importance and similarity between answers by using BERT. We built a dataset focusing on a Japanese CQA site, and the experiments on this dataset demonstrated that the proposed method outperformed several baseline methods.

2019

pdf bib
Machine Comprehension Improves Domain-Specific Japanese Predicate-Argument Structure Analysis
Norio Takahashi | Tomohide Shibata | Daisuke Kawahara | Sadao Kurohashi
Proceedings of the 2nd Workshop on Machine Reading for Question Answering

To improve the accuracy of predicate-argument structure (PAS) analysis, large-scale training data and knowledge for PAS analysis are indispensable. We focus on a specific domain, specifically Japanese blogs on driving, and construct two wide-coverage datasets as a form of QA using crowdsourcing: a PAS-QA dataset and a reading comprehension QA (RC-QA) dataset. We train a machine comprehension (MC) model based on these datasets to perform PAS analysis. Our experiments show that a stepwise training method is the most effective, which pre-trains an MC model based on the RC-QA dataset to acquire domain knowledge and then fine-tunes based on the PAS-QA dataset.

2018

pdf bib
Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis
Tomohide Shibata | Sadao Kurohashi
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Predicate argument structure analysis is a task of identifying structured events. To improve this field, we need to identify a salient entity, which cannot be identified without performing coreference resolution and predicate argument structure analysis simultaneously. This paper presents an entity-centric joint model for Japanese coreference resolution and predicate argument structure analysis. Each entity is assigned an embedding, and when the result of both analyses refers to an entity, the entity embedding is updated. The analyses take the entity embedding into consideration to access the global information of entities. Our experimental results demonstrate the proposed method can improve the performance of the inter-sentential zero anaphora resolution drastically, which is a notoriously difficult task in predicate argument structure analysis.

2016

pdf bib
Neural Network-Based Model for Japanese Predicate Argument Structure Analysis
Tomohide Shibata | Daisuke Kawahara | Sadao Kurohashi
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

pdf bib
Location Name Disambiguation Exploiting Spatial Proximity and Temporal Consistency
Takashi Awamura | Daisuke Kawahara | Eiji Aramaki | Tomohide Shibata | Sadao Kurohashi
Proceedings of the third International Workshop on Natural Language Processing for Social Media

2014

pdf bib
Rapid Development of a Corpus with Discourse Annotations using Two-stage Crowdsourcing
Daisuke Kawahara | Yuichiro Machida | Tomohide Shibata | Sadao Kurohashi | Hayato Kobayashi | Manabu Sassano
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

pdf bib
A Large Scale Database of Strongly-related Events in Japanese
Tomohide Shibata | Shotaro Kohama | Sadao Kurohashi
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

The knowledge about the relation between events is quite useful for coreference resolution, anaphora resolution, and several NLP applications such as dialogue system. This paper presents a large scale database of strongly-related events in Japanese, which has been acquired with our proposed method (Shibata and Kurohashi, 2011). In languages, where omitted arguments or zero anaphora are often utilized, such as Japanese, the coreference-based event extraction methods are hard to be applied, and so our method extracts strongly-related events in a two-phrase construct. This method first calculates the co-occurrence measure between predicate-arguments (events), and regards an event pair, whose mutual information is high, as strongly-related events. To calculate the co-occurrence measure efficiently, we adopt an association rule mining method. Then, we identify the remaining arguments by using case frames. The database contains approximately 100,000 unique events, with approximately 340,000 strongly-related event pairs, which is much larger than an existing automatically-constructed event database. We evaluated randomly-chosen 100 event pairs, and the accuracy was approximately 68%.

pdf bib
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.

2013

pdf bib
Precise Information Retrieval Exploiting Predicate-Argument Structures
Daisuke Kawahara | Keiji Shinzato | Tomohide Shibata | Sadao Kurohashi
Proceedings of the Sixth International Joint Conference on Natural Language Processing

2011

pdf bib
Acquiring Strongly-related Events using Predicate-argument Co-occurring Statistics and Case Frames
Tomohide Shibata | Sadao Kurohashi
Proceedings of 5th International Joint Conference on Natural Language Processing

2009

pdf bib
Bottom-up Named Entity Recognition using Two-stage Machine Learning Method
Hirotaka Funayama | Tomohide Shibata | Sadao Kurohashi
Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications (MWE 2009)

2008

pdf bib
TSUBAKI: An Open Search Engine Infrastructure for Developing New Information Access Methodology
Keiji Shinzato | Tomohide Shibata | Daisuke Kawahara | Chikara Hashimoto | Sadao Kurohashi
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-I

pdf bib
SYNGRAPH: A Flexible Matching Method based on Synonymous Expression Extraction from an Ordinary Dictionary and a Web Corpus
Tomohide Shibata | Michitaka Odani | Jun Harashima | Takashi Oonishi | Sadao Kurohashi
Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II

2006

pdf bib
Unsupervised Topic Identification by Integrating Linguistic and Visual Information Based on Hidden Markov Models
Tomohide Shibata | Sadao Kurohashi
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

2005

pdf bib
Automatic Slide Generation Based on Discourse Structure Analysis
Tomohide Shibata | Sadao Kurohashi
Second International Joint Conference on Natural Language Processing: Full Papers