Abstract
Ruidong Wu, Yuan Yao, Xu Han, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Highlights
Relation extraction (RE) aims to extract relational facts between two entities from plain texts
The model first extracts knowledge bases (KBs) types and named-entity recognition (NER) tags of entities as well as re-weighted word embeddings from sentences, adopts principal component analysis (PCA) to reduce feature dimensionality, and uses hierarchical agglomerative clustering (HAC) to cluster the concatenation of reduced feature representations
While the performance of supervised Relational Siamese Networks (RSNs) is very sensitive to pre-defined relation diversity, its semi-supervised counterparts suffer much less from the relation number limit
Summary
Relation extraction (RE) aims to extract relational facts between two entities from plain texts. With the sentence “Hayao Miyazaki is the director of the film ‘The Wind Rises’", we can extract a relation “director_of" between two entities “Hayao Miyazaki" and “The Wind Rises". Recent progress in supervised methods to RE has achieved great successes. Supervised methods can effectively learn significant relation semantic patterns based on existing labeled data, but the data constructions are time-consuming and human-intensive. To lower the level of supervision, several semi-supervised approaches have been developed, including bootstrapping, active learning, label propagation (Pawar et al, 2017)
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