Toshihiko Yanase


Learning to Generate Market Comments from Stock Prices
Soichiro Murakami | Akihiko Watanabe | Akira Miyazawa | Keiichi Goshima | Toshihiko Yanase | Hiroya Takamura | Yusuke Miyao
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper presents a novel encoder-decoder model for automatically generating market comments from stock prices. The model first encodes both short- and long-term series of stock prices so that it can mention short- and long-term changes in stock prices. In the decoding phase, our model can also generate a numerical value by selecting an appropriate arithmetic operation such as subtraction or rounding, and applying it to the input stock prices. Empirical experiments show that our best model generates market comments at the fluency and the informativeness approaching human-generated reference texts.

StruAP: A Tool for Bundling Linguistic Trees through Structure-based Abstract Pattern
Kohsuke Yanai | Misa Sato | Toshihiko Yanase | Kenzo Kurotsuchi | Yuta Koreeda | Yoshiki Niwa
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We present a tool for developing tree structure patterns that makes it easy to define the relations among textual phrases and create a search index for these newly defined relations. By using the proposed tool, users develop tree structure patterns through abstracting syntax trees. The tool features (1) intuitive pattern syntax, (2) unique functions such as recursive call of patterns and use of lexicon dictionaries, and (3) whole workflow support for relation development and validation. We report the current implementation of the tool and its effectiveness.

bunji at SemEval-2017 Task 3: Combination of Neural Similarity Features and Comment Plausibility Features
Yuta Koreeda | Takuya Hashito | Yoshiki Niwa | Misa Sato | Toshihiko Yanase | Kenzo Kurotsuchi | Kohsuke Yanai
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes a text-ranking system developed by bunji team in SemEval-2017 Task 3: Community Question Answering, Subtask A and C. The goal of the task is to re-rank the comments in a question-and-answer forum such that useful comments for answering the question are ranked high. We proposed a method that combines neural similarity features and hand-crafted comment plausibility features, and we modeled inter-comments relationship using conditional random field. Our approach obtained the fifth place in the Subtask A and the second place in the Subtask C.


Neural Attention Model for Classification of Sentences that Support Promoting/Suppressing Relationship
Yuta Koreeda | Toshihiko Yanase | Kohsuke Yanai | Misa Sato | Yoshiki Niwa
Proceedings of the Third Workshop on Argument Mining (ArgMining2016)

bunji at SemEval-2016 Task 5: Neural and Syntactic Models of Entity-Attribute Relationship for Aspect-based Sentiment Analysis
Toshihiko Yanase | Kohsuke Yanai | Misa Sato | Toshinori Miyoshi | Yoshiki Niwa
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)


End-to-end Argument Generation System in Debating
Misa Sato | Kohsuke Yanai | Toshinori Miyoshi | Toshihiko Yanase | Makoto Iwayama | Qinghua Sun | Yoshiki Niwa
Proceedings of ACL-IJCNLP 2015 System Demonstrations

Learning Sentence Ordering for Opinion Generation of Debate
Toshihiko Yanase | Toshinori Miyoshi | Kohsuke Yanai | Misa Sato | Makoto Iwayama | Yoshiki Niwa | Paul Reisert | Kentaro Inui
Proceedings of the 2nd Workshop on Argumentation Mining


A Comparison of Rule-Based and Machine Learning Methods for Medical Information Extraction
Osamu Imaichi | Toshihiko Yanase | Yoshiki Niwa
The First Workshop on Natural Language Processing for Medical and Healthcare Fields