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Minimal-length linearizations for mildly context-sensitive dependency trees (2009)
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| Venue: | In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics |
| Citations: | 6 - 0 self |
BibTeX
@INPROCEEDINGS{Park09minimal-lengthlinearizations,
author = {Y. Albert Park and Roger Levy},
title = {Minimal-length linearizations for mildly context-sensitive dependency trees},
booktitle = {In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics},
year = {2009}
}
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Abstract
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 contextsensitive case of gap-degree 1 dependency trees, we prove several properties of minimallength linearizations which allow us to improve the efficiency of our algorithm to the point that it can be used on most naturallyoccurring 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 contextsensitivity 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. 1
Keyphrases
minimal-length linearizations context-sensitive dependency tree deep dependency contextsensitive case sentence length minimallength linearizations projective linearizations minimal dependency length first algorithm polynomial-time natural language grammar mild contextsensitivity affords several property similar result dependency length mild context sensitivity random sentence dependency length naturallyoccurring sentence dependency tree little reduction dependency tree subject minimal-length linearization
