Neural Architectures for Nested NER through Linearization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10405609" target="_blank" >RIV/00216208:11320/19:10405609 - isvavai.cz</a>

Result on the web

<a href="https://www.aclweb.org/anthology/P19-1527.pdf" target="_blank" >https://www.aclweb.org/anthology/P19-1527.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/P19-1527" target="_blank" >10.18653/v1/P19-1527</a>

Result language

angličtina

Original language name

Neural Architectures for Nested NER through Linearization

Original language description

We propose two neural network architectures for nested named entity recognition (NER), a setting in which named entities may overlap and also be labeled with more than one label. We encode the nested labels using a linearized scheme. In our first proposed approach, the nested labels are modeled as multilabels corresponding to the Cartesian product of the nested labels in a standard LSTM-CRF architecture. In the second one, the nested NER is viewed as a sequence-to-sequence problem, in which the input sequence consists of the tokens and output sequence of the labels, using hard attention on the word whose label is being predicted. The proposed methods outperform the nested NER state of the art on four corpora: ACE-2004, ACE-2005, GENIA and Czech CNEC. We also enrich our architectures with the recently published contextual embeddings: ELMo, BERT and Flair, reaching further improvements for the four nested entity corpora. In addition, we report flat NER state-of-the-art results for CoNLL-2002 Dutch and S

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

ISBN

978-1-950737-48-2

ISSN

—

e-ISSN

—

Number of pages

6

Pages from-to

5326-5331

Publisher name

Association for Computational Linguistics

Place of publication

Stroudsburg, PA, USA

Event location

Firenze, Italy

Event date

Jul 28, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000493046107085