Interactive entity linking using entity-word representations

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 en...

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Main Authors: LO, Pei Chi, LIM, Ee-Peng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2020
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Online Access:https://ink.library.smu.edu.sg/sis_research/5269
https://ink.library.smu.edu.sg/context/sis_research/article/6272/viewcontent/11._Interactive_Entity_Linking_Using_Entity_Word_Representations__SIGIR__20_.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-62722020-08-14T02:48:24Z Interactive entity linking using entity-word representations LO, Pei Chi LIM, Ee-Peng 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. 2020-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5269 https://ink.library.smu.edu.sg/context/sis_research/article/6272/viewcontent/11._Interactive_Entity_Linking_Using_Entity_Word_Representations__SIGIR__20_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
spellingShingle Databases and Information Systems
LO, Pei Chi
LIM, Ee-Peng
Interactive entity linking using entity-word representations
description 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.
format text
author LO, Pei Chi
LIM, Ee-Peng
author_facet LO, Pei Chi
LIM, Ee-Peng
author_sort LO, Pei Chi
title Interactive entity linking using entity-word representations
title_short Interactive entity linking using entity-word representations
title_full Interactive entity linking using entity-word representations
title_fullStr Interactive entity linking using entity-word representations
title_full_unstemmed Interactive entity linking using entity-word representations
title_sort interactive entity linking using entity-word representations
publisher Institutional Knowledge at Singapore Management University
publishDate 2020
url https://ink.library.smu.edu.sg/sis_research/5269
https://ink.library.smu.edu.sg/context/sis_research/article/6272/viewcontent/11._Interactive_Entity_Linking_Using_Entity_Word_Representations__SIGIR__20_.pdf
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