Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Characterizing structural descriptions produced by various grammatical formalisms
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Online large-margin training of dependency parsers
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Mildly non-projective dependency structures
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
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The extent to which the organization of natural language grammars reflects a drive to minimize dependency length remains little explored. We present the first algorithm polynomial-time in sentence length for obtaining the minimal-length linearization of a dependency tree subject to constraints of mild context sensitivity. For the minimally context-sensitive case of gap-degree 1 dependency trees, we prove several properties of minimal-length linearizations which allow us to improve the efficiency of our algorithm to the point that it can be used on most naturally-occurring sentences. We use the algorithm to compare optimal, observed, and random sentence dependency length for both surface and deep dependencies in English and German. We find in both languages that analyses of surface and deep dependencies yield highly similar results, and that mild context-sensitivity affords very little reduction in minimal dependency length over fully projective linearizations; but that observed linearizations in German are much closer to random and farther from minimal-length linearizations than in English.