Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations

Authors

  • Muhammad Asif Ali University of New South Wales
  • Yifang Sun University of New South Wales
  • Bing Li University of New South Wales
  • Wei Wang University of New South Wales

DOI:

https://doi.org/10.1609/aaai.v34i05.6234

Abstract

Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP). It aims at classifying an entity mention into a wide range of entity types. Due to a large number of entity types, distant supervision is used to collect training data for this task, which noisily assigns type labels to entity mentions irrespective of the context. In order to alleviate the noisy labels, existing approaches on FG-NET analyze the entity mentions entirely independent of each other and assign type labels solely based on mention's sentence-specific context. This is inadequate for highly overlapping and/or noisy type labels as it hinders information passing across sentence boundaries. For this, we propose an edge-weighted attentive graph convolution network that refines the noisy mention representations by attending over corpus-level contextual clues prior to the end classification. Experimental evaluation shows that the proposed model outperforms the existing research by a relative score of upto 10.2% and 8.3% for macro-f1 and micro-f1 respectively.

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Published

2020-04-03

How to Cite

Ali, M. A., Sun, Y., Li, B., & Wang, W. (2020). Fine-Grained Named Entity Typing over Distantly Supervised Data Based on Refined Representations. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 7391-7398. https://doi.org/10.1609/aaai.v34i05.6234

Issue

Section

AAAI Technical Track: Natural Language Processing