This paper is concerned with the use of statistical information about dependency path length for sentence compression. The sentence compression method employed here requires a quantity called inter-phrase dependency strength. In the training process, original sentences are parsed, and the number of tokens is counted for each pair of phrases, connected with each other by a dependency path of certain length, that survive as a modifier-modified phrase pair in the corresponding compressed sentence in the training corpus. The statistics is exploited to estimate the interphrase dependency strength required in the sentence compression process. Results of subjective evaluation shows that the present method outperforms the conventional one of the same framework where the distribution of dependency distance is used to estimate the inter-phrase dependency strength. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Yamagata, K., Fukutomi, S., Takagi, K., & Ozeki, K. (2006). Sentence compression using statistical information about dependency path length. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4188 LNCS, pp. 127–134). Springer Verlag. https://doi.org/10.1007/11846406_16
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