Umeå University's logo

umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Few-shot named entity recognition via Label-Attention Mechanism
Hefei University of Technology, Anhui, Hefei, China.
Hefei University of Technology, Anhui, Hefei, China.
Hefei University of Technology, Anhui, Hefei, China.
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-7788-3986
Show others and affiliations
2023 (English)In: ICCAI '23: proceedings of the 2023 9th international conference on computing and artificial intelligence, Association for Computing Machinery (ACM), 2023, p. 466-471Conference paper, Published paper (Refereed)
Abstract [en]

Few-shot named entity recognition aims to identify specific words with the support of very few labeled entities. Existing transfer-learning-based methods learn the semantic features of words in the source domain and migrate them to the target domain but ignore the different label-specific information. We propose a novel Label-Attention Mechanism (LAM) to utilize the overlooked label-specific information. LAM can separate label information from semantic features and learn how to obtain label information from a few samples through the meta-learning strategy. When transferring to the target domain, LAM replaces the source label information with the knowledge extracted from the target domain, thus improving the migration ability of the model. We conducted extensive experiments on multiple datasets, including OntoNotes, CoNLL'03, WNUT'17, GUM, and Few-Nerd, with two experimental settings. The results show that LAM is 7% better than the state-of-the-art baseline models by the absolute F1 scores.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023. p. 466-471
Series
ACM International Conference Proceeding Series
Keywords [en]
Few shot learning, Label-Attention, Named Entity Recognition
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-213934DOI: 10.1145/3594315.3594358Scopus ID: 2-s2.0-85168240049ISBN: 9781450399029 (electronic)OAI: oai:DiVA.org:umu-213934DiVA, id: diva2:1796015
Conference
9th International Conference on Computing and Artificial Intelligence, ICCAI 2023, Tianjin, China, March 17-20, 2023
Available from: 2023-09-11 Created: 2023-09-11 Last updated: 2023-09-11Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Jiang, Lili

Search in DiVA

By author/editor
Jiang, Lili
By organisation
Department of Computing Science
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 59 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf