Work

Tree Representations in Probabilistic Models for Extended Named Entities Detection

Year: 2012

Type: article

Abstract: In this paper we deal with Named Entity Recognition (NER) on transcriptions of French broadcast data. Two aspects make the task more difficult with respect to previous NER tasks: i) named entities annotated used in this work have a tree structure, thus the task cannot be tackled as a sequence labelling task; ii) the data used are more noisy than data used for previous NER tasks. We approach the task in two steps, involving Conditional Random Fields and Probabilistic Context-Free Grammars, integrated in a single parsing algorithm. We analyse the effect of using several tree representations. Our system outperforms the best system of the evaluation campaign by a significant margin. 1

Authors: Marco Dinarelli, Sophie Rosset

Institutions: Université Paris Sciences et Lettres, Sorbonne Paris Cité, Langues, Textes, Traitements Informatiques, Cognition, Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2111475098

FWCI: 0.581

Cites: 29

Cited by: 3

Related to: 10