Structured Named Entity Recognition by Cascading CRFs

被引：0

作者：

Dupont, Yoann ^{[1
,2
]}

Dinarelli, Marco ^{[1
]}

Tellier, Isabelle ^{[1
]}

Lautier, Christian ^{[2
]}

机构：

[1] CNRS, UMR 8094, Lab Lattice, 1 Rue Maurice Arnoux, F-92120 Montrouge, France

[2] Expert Syst France, 207 Rue Bercy, F-75012 Paris, France

来源：

COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING (CICLING 2017), PT I | 2018年 / 10761卷

关键词：

Machine learning; Structured named entity recognition; CRF; Quaero;

D O I：

10.1007/978-3-319-77113-7_20

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

NER is an important task in NLP, often used as a basis for further treatments. A new challenge has emerged in the last few years: structured named entity recognition, where not only named entities must be identified but also their hierarchical components. In this article, we describe a cascading CRFs approach to address this challenge. It reaches the state of the art while remaining very simple on a structured NER challenge. We then offer an error analysis of our system based on a detailed, yet simple, error classification.

引用

页码：249 / 263

页数：15

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