1 Abstract In this paper we will present an evaluation of current state-of-the-art algorithms for Anaphora Resolution based on a segment of Susanne corpus (itself a portion of Brown Corpus), a much more comparable text type to what is usually required at an international level for such application domains as Question/Answering, Information Extraction, Text Understanding, Language Learning. The portion of text chosen has an adequate size which lends itself to significant statistical measurements: it is portion A, counting 35,000 tokens and some 1000 third person pronominal expressions. The algorithms will then be compared to our system, GETARUNS, which incorporates an AR algorithm at the end of a pipeline of interconnected modules that instantiate standard architectures for NLP. F- measure values reached by our system are significantly higher (75%) than the other ones.

Another Evaluation of Anaphora Resolution Algorithms and a Comparison with GETARUNS' Knowledge Rich Approach

DELMONTE, Rodolfo;BRISTOT, Antonella;TONELLI, Sara
2006

Abstract

1 Abstract In this paper we will present an evaluation of current state-of-the-art algorithms for Anaphora Resolution based on a segment of Susanne corpus (itself a portion of Brown Corpus), a much more comparable text type to what is usually required at an international level for such application domains as Question/Answering, Information Extraction, Text Understanding, Language Learning. The portion of text chosen has an adequate size which lends itself to significant statistical measurements: it is portion A, counting 35,000 tokens and some 1000 third person pronominal expressions. The algorithms will then be compared to our system, GETARUNS, which incorporates an AR algorithm at the end of a pipeline of interconnected modules that instantiate standard architectures for NLP. F- measure values reached by our system are significantly higher (75%) than the other ones.
Robust Methods in Analysis of Natural Language Data - ROMAND 2006 - 11th EACL
File in questo prodotto:
File Dimensione Formato  
W06-2302.pdf

non disponibili

Tipologia: Abstract
Licenza: Licenza non definita
Dimensione 157.93 kB
Formato Adobe PDF
157.93 kB Adobe PDF   Visualizza/Apri

I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/11163
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact