| 研究領域 | 時間生成学―時を生み出すこころの仕組み |
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| 研究課題/領域番号 |
19H05318
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| 研究種目 |
新学術領域研究(研究領域提案型)
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| 配分区分 | 補助金 |
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| 審査区分 |
複合領域
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| 研究機関 | 京都大学 |
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研究代表者 |
程 飛 京都大学, 情報学研究科, 特定助教 (70801570)
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| 研究期間 (年度) |
2019-04-01 – 2021-03-31
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| 研究課題ステータス |
完了 (2020年度)
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| 配分額 *注記 |
3,380千円 (直接経費: 2,600千円、間接経費: 780千円)
2020年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2019年度: 2,080千円 (直接経費: 1,600千円、間接経費: 480千円)
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| キーワード | Information extraction / Neural networks / Language processing / Temporal Information / Natural Language / Deep Learning / Information Extraction / Relation Extraction / Temporal Relation / Neural Networks / Multilingual Data |
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| 研究開始時の研究の概要 |
Temporal relation classification (TRC) the core task in Temporal Information Extraction (TIE), which aims to identify a temporal order (before, after, simultaneous, during, etc.) between two entities (event, time expression or Document Creation Time, DCT). TRC has great potential to create many practical applications, such as extracting event timeline from news articles. We aim to explore deep neural networks for the TRC task from three different aspect: 1) neural network structure 2) task setting 3) multilingual corpora.
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| 研究実績の概要 |
According to our research plan, we developed neural network models to improve state-of-the-art temporal information extraction (TRC) performance. We explored three valid research directions:
1) Neural network classification models can be improved in several aspects. Deeper network layers, attention mechanism and integrating more features are proved to be effective in many tasks. We first introduced latest deep layer transfer learning models (i.e. BERT) into the TRC tasks. The empirical results showed the significant improvements over the existing shallow neural models.
2) Existing research of TRC treats E-E, E-T, and E-D as three separate TLINK classification targets. We experimented training all three TLINK types in a multi-task learning scenario, which effectively exploy the use of all the data and significantly improve the baseline.
3) ‘Multilingual’ is an important topic in NLP. Many high-quality Japanese TRC corpora have been constructed in recent years, such as BCCWJ-Timebank. We performed the additional experiments to evaluate the feasibility of our models for the Japanese BCCWJ-Timebank. Our model outperforms the existing SOTA models by a large margin.
In summary, we manage to develop the SOTA performance TRC models. The paper is accepted by the top international conference for natural language processing: EMNLP (findings volume). We also explored 'end-to-end information extraction' and 'temporal question-answering' tasks, which is helpful to understanding the TRC task from different aspects. These two papers are accepted by EMNLP-findings and ACL-RepL4NLP.
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| 現在までの達成度 (段落) |
令和2年度が最終年度であるため、記入しない。
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| 今後の研究の推進方策 |
令和2年度が最終年度であるため、記入しない。
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