Publication Type

Conference Proceeding Article

Version

Publisher’s Version

Publication Date

7-2020

Abstract

To leverage on entity and word semantics in entity linking, embedding models have been developed to represent entities, words and their context such that candidate entities for each mention can be determined and ranked accurately using their embeddings. To leverage on entity and word semantics in entity linking, embedding models have been developed to represent entities, words and their context such that candidate entities for each mention can be determined and ranked accurately using their embeddings. In this paper, we leverage on human intelligence for embedding-based interactive entity linking. We adopt an active learning approach to select mentions for human annotation that can best improve entity linking accuracy at the same time updating the embedding model. We propose two mention selection strategies based on: (1) coherence of entities linked, and (2) contextual closeness of candidate entities with respect to mention. Our experiments show that our proposed interactive entity linking methods outperform their batch counterpart in all our experimented datasets with relatively small amount of human annotations.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

First Page

1801

Last Page

1804

Publisher

Association for Computing Machinery

City or Country

New York, USA

Embargo Period

8-13-2020

Additional URL

https://doi.org/10.1145/3397271.3401254

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