Partial Parsing from Bitext Projections

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1597–1606, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Partial Parsing from Bitext Projections Prashanth Mannem and Aswarth Dara Language Technologies Research Center International Institute of Information Technology Hyderabad, AP, India - 500032 {prashanth,abhilash.d}@research.iiit.ac.in Abstract Recent work has shown how a parallel corpus can be leveraged to build syntac- tic parser for a target language by project- ing automatic source parse onto the target sentence using word alignments. The pro- jected target dependency parses are not al- ways fully connected to be useful for train- ing traditional dependency parsers. In this paper, we present a greedy non-directional parsing algorithm which doesn’t need a fully connected parse and can learn from partial parses by utilizing available struc- tural and syntactic information in them. Our parser achieved statistically signifi- cant improvements over a baseline system that trains on only fully connected parses for Bulgarian, Spanish and Hindi. It also gave a significant improvement over pre- viously reported results for Bulgarian and set a benchmark for Hindi. 1 Introduction Parallel corpora have been used to transfer in- formation from source to target languages for Part-Of-Speech (POS) tagging, word sense disam- biguation (Yarowsky et al., 2001), syntactic pars- ing (Hwa et al., 2005; Ganchev et al., 2009; Jiang and Liu, 2010) and machine translation (Koehn, 2005; Tiedemann, 2002). Analysis on the source sentences was induced onto the target sentence via projections across word aligned parallel corpora. Equipped with a source language parser and a word alignment tool, parallel data can be used to build an automatic treebank for a target language. The parse trees given by the parser on the source sentences in the parallel data are projected onto the target sentence using the word alignments from the alignment tool. Due to the usage of automatic source parses, automatic word alignments and dif- ferences in the annotation schemes of source and target languages, the projected parses are not al- ways fully connected and can have edges missing (Hwa et al., 2005; Ganchev et al., 2009). Non- literal translations and divergences in the syntax of the two languages also lead to incomplete pro- jected parse trees. Figure 1 shows an English-Hindi parallel sen- tence with correct source parse, alignments and target dependency parse. For the same sentence, Figure 2 is a sample partial dependency parse pro- jected using an automatic source parser on aligned text. This parse is not fully connected with the words banaa, kottaige and dikhataa left without any parents. para bahuta hai The cottage built on the hill looks very beautiful pahaada banaa huaa kottaige sundara dikhataa Figure 1: Word alignment with dependency parses for an English-Hindi parallel sentence To train the traditional dependency parsers (Ya- mada and Matsumoto, 2003; Eisner, 1996; Nivre, 2003), the dependency parse has to satisfy four constraints: connectedness, single-headedness, acyclicity and projectivity (Kuhlmann and Nivre, 2006). Projectivity can be relaxed in some parsers (McDonald et al., 2005; Nivre, 2009). But these parsers …

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