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...
Saved in:
Main Authors: | , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2020
|
Subjects: | |
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 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
id |
sg-smu-ink.sis_research-6272 |
---|---|
record_format |
dspace |
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 |
_version_ |
1770575365948833792 |