Relation classification is the task to predict semantic relations between pairs of entities in a given text. In this paper, a novel Long Short Term Memory Network (LSTM)-based approach is proposed to extract relations between entities in Chinese text. The shortest dependency path (SDP) between two entities, together with the various selected features in the path, are first extracted, and then used as input of an LSTM model to predict the relation between them. The performance of the system was evaluated on the ACE 2005 Multilingual Training Corpus (Walker et al., 2006), and achieved a state-of-the-art F-measure of 87.87% on six general type relations and 83.40% on eighteen subtype relations in this corpus.
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Chinese Relation Classification using Long Short Term Memory Networks
Semantic Scholar · Computer Science · 2018
Abstract
Relation classification is the task to predict semantic relations between pairs of entities in a given text. In this paper, a novel Long Short Term Memory Network (LSTM)-based approach is proposed to extract relations between entities in Chinese text. The shortest dependency path (SDP) between two entities, together with the various selected features in the path, are first extracted, and then used as input of an LSTM model to predict the relation between them. The performance of the system was evaluated on the ACE 2005 Multilingual Training Corpus (Walker et al., 2006), and achieved a state-of-the-art F-measure of 87.87% on six general type relations and 83.40% on eighteen subtype relations in this corpus.