Dynamic Ensemble of Heterogeneous Encoding Models in Knowledge Extraction of Diverse Event Expressions

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  • Ishikawa Kai
    Data Science Research Laboratories, NEC Corporation
  • Takamura Hiroya
    Laboratory for Future Interdisciplinary Research of Science and Technology, Institute of Innovative Research, Tokyo Institute of Technology Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology
  • Okumura Manabu
    Laboratory for Future Interdisciplinary Research of Science and Technology, Institute of Innovative Research, Tokyo Institute of Technology

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  • 多様なイベント表現を対象とした知識抽出における異なるエンコーディングモデル群の動的アンサンブル
  • タヨウ ナ イベント ヒョウゲン オ タイショウ ト シタ チシキ チュウシュツ ニ オケル コトナル エンコーディングモデルグン ノ ドウテキ アンサンブル

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Abstract

<p>In this paper, we propose a novel ensemble approach for event nugget detection that consists of heterogeneous encoding models to handle diversifying linguistic expressions of events in text and a dynamic ensemble method to obtain an ensemble of reliable models for each input token dynamically. From a set of comparative evaluations in subtasks, we show that our proposed method exceeds each encoding model and soft voting in F1-score. Moreover, we prove the effectiveness of our proposal by comparing our evaluation system with the results of NIST TAC KBP2016 and KBP2017 participants in F1-scores. Lastly, we consider the usefulness of our proposed method in event nugget detection through a series of discussions on applying proposed method to recent neural network models.</p>

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