Hedwig: A named entity linker

Marcus Klang; Pierre Nugues

Hedwig: A named entity linker

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review

Sammanfattning

Named entity linking is the task of identifying mentions of named things in text, such as “Barack Obama” or “New York”, and linking these mentions to unique identifiers. In this paper, we describe Hedwig, an end-to-end named entity linker, which uses a combination of word and character BILSTM models for mention detection, a Wikidata and Wikipedia-derived knowledge base with global information aggregated over nine language editions, and a PageRank algorithm for entity linking. We evaluated Hedwig on the TAC2017 dataset, consisting of news texts and discussion forums, and we obtained a final score of 59.9% on CEAFmC+, an improvement over our previous generation linker Ugglan, and a trilingual entity link score of 71.9%.

Originalspråk	engelska
Titel på värdpublikation	LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
Redaktörer	Nicoletta Calzolari, Frederic Bechet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Förlag	European Language Resources Association
Sidor	4501-4508
Antal sidor	8
ISBN (elektroniskt)	9791095546344
Status	Published - 2020
Evenemang	12th International Conference on Language Resources and Evaluation, LREC 2020 - Marseille, Frankrike Varaktighet: 2020 maj 11 → 2020 maj 16

Publikationsserier

Namn	LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings

Konferens

Konferens	12th International Conference on Language Resources and Evaluation, LREC 2020
Land/Territorium	Frankrike
Ort	Marseille
Period	2020/05/11 → 2020/05/16

Ämnesklassifikation (UKÄ)

Datavetenskap (datalogi)
Språkteknologi (språkvetenskaplig databehandling)

Tillgång till dokument

https://www.aclweb.org/anthology/2020.lrec-1.554.pdf

1 Doktorsavhandling (sammanläggning)

Building Knowledge Graphs: Processing Infrastructure and Named Entity Linking
Klang, M., 2019 sep. 17, Ole Römers väg 3, Lund: Department of Computer Science, Lund University. 164 s.
Forskningsoutput: Avhandling › Doktorsavhandling (sammanläggning)

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Fil

Citera det här

Klang, M., & Nugues, P. (2020). Hedwig: A named entity linker. I N. Calzolari, F. Bechet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, & S. Piperidis (Red.), LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings (s. 4501-4508). (LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings). European Language Resources Association. https://www.aclweb.org/anthology/2020.lrec-1.554.pdf

Klang, Marcus ; Nugues, Pierre. / Hedwig : A named entity linker. LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings. redaktör / Nicoletta Calzolari ; Frederic Bechet ; Philippe Blache ; Khalid Choukri ; Christopher Cieri ; Thierry Declerck ; Sara Goggi ; Hitoshi Isahara ; Bente Maegaard ; Joseph Mariani ; Helene Mazo ; Asuncion Moreno ; Jan Odijk ; Stelios Piperidis. European Language Resources Association, 2020. s. 4501-4508 (LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings).

@inproceedings{1bd6c3478ed24c32923fa352af1bff6a,

title = "Hedwig: A named entity linker",

abstract = "Named entity linking is the task of identifying mentions of named things in text, such as “Barack Obama” or “New York”, and linking these mentions to unique identifiers. In this paper, we describe Hedwig, an end-to-end named entity linker, which uses a combination of word and character BILSTM models for mention detection, a Wikidata and Wikipedia-derived knowledge base with global information aggregated over nine language editions, and a PageRank algorithm for entity linking. We evaluated Hedwig on the TAC2017 dataset, consisting of news texts and discussion forums, and we obtained a final score of 59.9% on CEAFmC+, an improvement over our previous generation linker Ugglan, and a trilingual entity link score of 71.9%.",

keywords = "Named entity annotation, Named entity linking, Named entity recognition",

author = "Marcus Klang and Pierre Nugues",

year = "2020",

language = "English",

series = "LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings",

publisher = "European Language Resources Association",

pages = "4501--4508",

editor = "Nicoletta Calzolari and Frederic Bechet and Philippe Blache and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Helene Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis",

booktitle = "LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings",

address = "France",

note = "12th International Conference on Language Resources and Evaluation, LREC 2020 ; Conference date: 11-05-2020 Through 16-05-2020",

}

Klang, M & Nugues, P 2020, Hedwig: A named entity linker. i N Calzolari, F Bechet, P Blache, K Choukri, C Cieri, T Declerck, S Goggi, H Isahara, B Maegaard, J Mariani, H Mazo, A Moreno, J Odijk & S Piperidis (red), LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings. LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings, European Language Resources Association, s. 4501-4508, 12th International Conference on Language Resources and Evaluation, LREC 2020, Marseille, Frankrike, 2020/05/11. <https://www.aclweb.org/anthology/2020.lrec-1.554.pdf>

Hedwig : A named entity linker. / Klang, Marcus ; Nugues, Pierre.

LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings. red. / Nicoletta Calzolari; Frederic Bechet; Philippe Blache; Khalid Choukri; Christopher Cieri; Thierry Declerck; Sara Goggi; Hitoshi Isahara; Bente Maegaard; Joseph Mariani; Helene Mazo; Asuncion Moreno; Jan Odijk; Stelios Piperidis. European Language Resources Association, 2020. s. 4501-4508 (LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings).

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review

TY - GEN

T1 - Hedwig

T2 - 12th International Conference on Language Resources and Evaluation, LREC 2020

AU - Klang, Marcus

AU - Nugues, Pierre

PY - 2020

Y1 - 2020

N2 - Named entity linking is the task of identifying mentions of named things in text, such as “Barack Obama” or “New York”, and linking these mentions to unique identifiers. In this paper, we describe Hedwig, an end-to-end named entity linker, which uses a combination of word and character BILSTM models for mention detection, a Wikidata and Wikipedia-derived knowledge base with global information aggregated over nine language editions, and a PageRank algorithm for entity linking. We evaluated Hedwig on the TAC2017 dataset, consisting of news texts and discussion forums, and we obtained a final score of 59.9% on CEAFmC+, an improvement over our previous generation linker Ugglan, and a trilingual entity link score of 71.9%.

AB - Named entity linking is the task of identifying mentions of named things in text, such as “Barack Obama” or “New York”, and linking these mentions to unique identifiers. In this paper, we describe Hedwig, an end-to-end named entity linker, which uses a combination of word and character BILSTM models for mention detection, a Wikidata and Wikipedia-derived knowledge base with global information aggregated over nine language editions, and a PageRank algorithm for entity linking. We evaluated Hedwig on the TAC2017 dataset, consisting of news texts and discussion forums, and we obtained a final score of 59.9% on CEAFmC+, an improvement over our previous generation linker Ugglan, and a trilingual entity link score of 71.9%.

KW - Named entity annotation

KW - Named entity linking

KW - Named entity recognition

M3 - Paper in conference proceeding

AN - SCOPUS:85096580434

T3 - LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings

SP - 4501

EP - 4508

BT - LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings

A2 - Calzolari, Nicoletta

A2 - Bechet, Frederic

A2 - Blache, Philippe

A2 - Choukri, Khalid

A2 - Cieri, Christopher

A2 - Declerck, Thierry

A2 - Goggi, Sara

A2 - Isahara, Hitoshi

A2 - Maegaard, Bente

A2 - Mariani, Joseph

A2 - Mazo, Helene

A2 - Moreno, Asuncion

A2 - Odijk, Jan

A2 - Piperidis, Stelios

PB - European Language Resources Association

Y2 - 11 May 2020 through 16 May 2020

ER -

Klang M , Nugues P. Hedwig: A named entity linker. I Calzolari N, Bechet F, Blache P, Choukri K, Cieri C, Declerck T, Goggi S, Isahara H, Maegaard B, Mariani J, Mazo H, Moreno A, Odijk J, Piperidis S, redaktörer, LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings. European Language Resources Association. 2020. s. 4501-4508. (LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings).

Hedwig: A named entity linker

Sammanfattning

Publikationsserier

Konferens

Ämnesklassifikation (UKÄ)

Tillgång till dokument

Forskningsoutput

Building Knowledge Graphs: Processing Infrastructure and Named Entity Linking

Citera det här