PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Automatic Selection of High Quality Parses Created By a Fully Unsupervised Parser
Roi Reichart and Ari Rappoport
In: CoNLL 2009(2009).


The average results obtained by unsupervised statistical parsers have greatly improved in the last few years, but on many specific sentences they are of rather low quality. The output of such parsers is becoming valuable for various applications, and it is radically less expensive to create than manually annotated training data. Hence, automatic selection of high quality parses created by unsupervised parsers is an important problem. In this paper we present PUPA, a POS-based Unsupervised Parse Assessment algorithm. The algorithm assesses the quality of a parse tree using POS sequence statistics collected from a batch of parsed sentences. We evaluate the algorithm by using an unsupervised POS tagger and an unsupervised parser, selecting high quality parsed sentences from English (WSJ) and German (NEGRA) corpora. We show that PUPA outperforms the leading previous parse assessment algorithm for supervised parsers, as well as a strong unsupervised baseline. Consequently, PUPA allows obtaining high quality parses without any human involvement.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Natural Language Processing
ID Code:5569
Deposited By:Ari Rappoport
Deposited On:04 March 2010