Cascaded Models for Better Fine-Grained Named Entity Recognition release_3ovn7ljl2jedjgo7e6jxx63r3m

by Parul Awasthy and Taesun Moon and Jian Ni and Radu Florian

Released as a article .

2020  

Abstract

Named Entity Recognition (NER) is an essential precursor task for many natural language applications, such as relation extraction or event extraction. Much of the NER research has been done on datasets with few classes of entity types (e.g. PER, LOC, ORG, MISC), but many real world applications (disaster relief, complex event extraction, law enforcement) can benefit from a larger NER typeset. More recently, datasets were created that have hundreds to thousands of types of entities, sparking new lines of research (Sekine, 2008;Ling and Weld, 2012; Gillick et al., 2014; Choiet al., 2018). In this paper we present a cascaded approach to labeling fine-grained NER, applying to a newly released fine-grained NER dataset that was used in the TAC KBP 2019 evaluation (Ji et al., 2019), inspired by the fact that training data is available for some of the coarse labels. Using a combination of transformer networks, we show that performance can be improved by about 20 F1 absolute, as compared with the straightforward model built on the full fine-grained types, and show that, surprisingly, using course-labeled data in three languages leads to an improvement in the English data.
In text/plain format

Archived Files and Locations

application/pdf  382.8 kB
file_n4zv5fhxi5hd7lo2hcpwayt5du
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2020-09-15
Version   v1
Language   en ?
arXiv  2009.07317v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 110c304e-0fa1-405c-a8b9-59cc62a4a8ea
API URL: JSON