Automatic Selection of High Quality Parses Created By a Fully Unsupervised Parser

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.