Elimination of Spurious Ambiguity in Transition-Based Dependency Parsing

We present a novel technique to remove spurious ambiguity from transition systems for dependency parsing. Our technique chooses a canonical sequence of transition operations (computation) for a given dependency tree. Our technique can be applied to a large class of bottom-up transition systems, including for instance Nivre (2004) and Attardi (2006)

Spurious ambiguity and focalization

Spurious ambiguity is the phenomenon whereby distinct derivations in grammar may assign the same structural reading, resulting in redundancy in the parse search space and inefficiency in parsing. Understanding the problem depends on identifying the essential mathematical structure of derivations. This is trivial in the case of context free grammar, where the parse structures are ordered trees; in the case of type logical categorial grammar, the parse structures are proof nets. However, with respect to multiplicatives, intrinsic proof nets have not yet been given for displacement calculus, and proof nets for additives, which have applications to polymorphism, are not easy to characterize. In this context we approach here multiplicative-additive spurious ambiguity by means of the proof-theoretic technique of focalization.Peer ReviewedPostprint (published version

Improvements in Transition Based Systems for Dependency Parsing

This thesis investigates transition based systems for parsing of natural language using dependency grammars. Dependency parsing provides a good and simple syntactic representation of the grammatical relations in a sentence. In the last years, this basic task has become a fundamental step in many applications that deal with natural language processing. Specifically, transition based systems have strong practical and psycholinguistic motivations. From a practical point of view, these systems are the only parsing systems that are fast enough to be used in web-scale applications. From a psycholinguistic point of view, they very closely resemble how humans incrementally process the language. However, these systems fall back in accuracy when compared with graph-based parsing, a family of parsing techniques that are based on a more traditional graph theoretic / dynamic programming approach, and that are more demanding on a computational perspective. Recently, some techniques have been developed in order to improve the accuracy of transition based systems. Most successful techniques are based on beam search or on the combination of the output of different parsing algorithms. However, all these techniques have a negative impact on parsing time. In this thesis, I will explore an alternative approach for transition based parsing, one that improves the accuracy without sacrificing computational efficiency. I will focus on greedy transition based systems and I will show how it is possible to improve the accuracy by using a dynamic oracle and a flexible parsing strategy. Dynamic oracles allow to reduce the error propagation at parsing time. Dynamic oracles may have some impact on training time, but there is no efficiency loss at parsing time. A flexible parsing strategy allows to reduce constraints over the parsing process and the time impact in both training and parsing time is almost negligible. Finally, these two techniques work really well when combined together, and they are orthogonal to previously explored proposals such as beam search or system combinations. As far as I know, the obtained experimental results are still state-of-the-art for greedy transition based parsing based on dependency grammars