PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Neutralizing Linguistically Problematic Annotations in Unsupervised Dependency Parsing Evaluation
Roy Schwartz, Omri Abend, Roi Reichart and Ari Rappoport
In: ACL 2011(2011).

Abstract

Dependency parsing is a central NLP task. In this paper we show that the common evaluation for unsupervised dependency parsing is highly sensitive to problematic annotations. We show that for three leading unsupervised parsers (Klein and Manning, 2004; Cohen and Smith, 2009; Spitkovsky et al., 2010a), a small set of parameters can be found whose modification yields a significant improvement in standard evaluation measures. These parameters correspond to local cases where no linguistic consensus exists as to the proper gold annotation. Therefore, the standard evaluation does not provide a true indication of algorithm quality. We present a new measure, Neutral Edge Direction (NED), and show that it greatly reduces this undesired phenomenon.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:9270
Deposited By:Ari Rappoport
Deposited On:21 February 2012