Experiments with a Multilanguage Non-Projective Dependency Parser

Parsing natural language is an essential step in several applications that involve document analysis, e.g. knowledge extraction, question answering, summarization, filtering. The best performing systems at the TREC Question Answering track employ parsing for analyzing sentences in order to identify the query focus, to extract relations and to disambiguate meanings of words. These are often demanding applications, which need to handle large collections and to provide results in a fraction of a second. Dependency parsers are promising for these applications since a dependency tree provides predicate-argument relations which are convenient for use in the later stages. Recently statistical dependency parsing techniques have been proposed which are deterministic and/or linear (Yamada and Matsumoto, 2003; Nivre and Scholz, 2004). These parsers are based on learning the correct sequence of Shift/Reduce actions used to construct the dependency tree. Learning is based on techniques like SVM (Vapnik 1998) or Memory Based Learning (Daelemans 2003), which provide high accuracy but are often computationally expensive. Kudo and Matsumoto (2002) report a two week learning time on a Japanese corpus of about 8000 sentences with SVM. Using Maximum Entropy (Berger, et al. 1996) classifiers I built a parser that achieves a throughput of over 200 sentences per second, with a small loss in accuracy of about 23 %. The efficiency of Maximum Entropy classifiers seems to leave a large margin that can be exploited to regain accuracy by other means. I performed a series of experiments to determine whether increasing the number of features or combining several classifiers could allow regaining the best accuracy. An experiment cycle in our setting requires less than 15 minutes for a treebank of moderate size like the Portuguese treebank (Afonso et al., 2002) and this allows evaluating the effectiveness of adding/removing features that hopefully might apply also when using other learning techniques. I extended the Yamada-Matsumoto parser to handle labeled dependencies: I tried two approaches: using a single classifier to predict pairs of actions and labels and using two separate classifiers, one for actions and one for labels. Finally, I extended the repertoire of actions used by the parser, in order to handle non-projective relations. Tests on the PDT (Böhmovà et al., 2003) show that the added actions are sufficient to handle all cases of non-projectivity. However, since the cases of non-projectivity are quite rare in the corpus, the general learner is not supplied enough of them to learn how to classify them accurately, hence it may be worthwhile to exploit a second classifier trained specifically in handling nonprojective situations.