What's New

We will present the following papers at ACL2018 (2018/7/15-20) (4/23)

  • Tomohide Shibata and Sadao Kurohashi: Entity-Centric Joint Modeling of Japanese Coreference Resolution and Predicate Argument Structure Analysis
  • Shuhei Kurita, Daisuke Kawahara and Sadao Kurohashi: Neural Adversarial Training for Semi-supervised Japanese Predicate-argument Structure Analysis

We published a press release about joint research for social problem-solving using LINE (4/12)

We will present the following paper at NAACL2018 (2018/6/1-6/6) (4/3)

  • Abhishek Kumar, Daisuke Kawahara and Sadao Kurohashi: Knowledge-enriched Two-layered Attention Network for Sentiment Analysis (short)

We will present the following papers at LREC2018 (2018/5/7-5/12) (4/3)

  • Yudai Kishimoto, Shinnosuke Sawada, Yugo Murawaki, Daisuke Kawahara and Sadao Kurohashi: Improving Crowdsourcing-Based Annotation of Japanese Discourse Relations
  • Tetsuaki Nakamura and Daisuke Kawahara: JFCKB: Japanese Feature Change Knowledge Base
  • Tetsuaki Nakamura and Daisuke Kawahara: JDCFC: A Japanese Dialogue Corpus with Feature Changes
  • Tomohiro Sakaguchi, Daisuke Kawahara and Sadao Kurohashi: Comprehensive Annotation of Various Types of Temporal Information on the Time Axis

Dr. Ribeka Tanaka joined our lab as researcher (4/1).

Research Overview

Language is the most reliable medium of human intellectual activities. Our objective is to establish the technology and academic discipline for handling and understanding language, in a manner that is as close as possible to that of humans, using computers. These include syntactic language analysis, semantic analysis, context analysis, text comprehension, text generation and dictionary systems to develop various application systems for machine translation and information retrieval.

Search Engine Infrastructure based on Deep Natural Language Processing

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The essential purpose of information retrieval is not to retrieve just a relevant document but to acquire the information or knowledge in the document. We have been developing a next-generation infrastructure of information retrieval on the basis of the following techniques of deep natural language processing: precise processing based not on words but on predicate-argument structures, identifying the variety of linguistic expressions and providing a bird's-eye view of search results via clustering and interaction.

Machine Translation

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To bring automatic translation by computers to the level of human translation, we have been studying next-generation methodology of machine translation on the basis of text understanding and a large collection of translation examples. We have already accomplished practical translation on the domain of travel conversation, and constructed a translation-aid system that can be used by experts of patent translation.

Fundamental Studies on Text Understanding

To make computers understand language, it is essential to give computers world knowledge. This was a very hard problem ten years ago, but it has become possible to acquire knowledge from a massive amount of text in virtue of the drastic progress of computing power and network. We have successfully acquired linguistic patterns of predicate-argument structures from automatic parses of 7 billion Japanese sentences crawled from the Web using grid computing machines. By utilizing such knowledge, we study text understanding, i.e., recognizing the relationships between words and phrases in text.

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