Iterative CKY parsing for probabilistic context-free grammars

Yoshimasa Tsuruoka, Jun'ichi Tsujii

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    This paper presents an iterative CKY parsing algorithm for probabilistic context-free grammars (PCFG). This algorithm enables us to prune unnecessary edges produced during parsing, which results in more efficient parsing. Since pruning is done by using the edge's inside Viterbi probability and the upper-bound of the outside Viterbi probability, this algorithm guarantees to output the exact Viterbi parse, unlike beam-search or best-first strategies. Experimental results using the Penn Treebank II corpus show that the iterative CKY achieved more than 60% reduction of edges compared with the conventional CKY algorithm and the run-time overhead is very small. Our algorithm is general enough to incorporate a more sophisticated estimation function, which should lead to more efficient parsing. © Springer-Verlag Berlin Heidelberg 2005.
    Original languageEnglish
    Title of host publicationLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)|Lect Notes Artif Intell
    EditorsK.-Y. Su, J. Tsujii, J.-H. Lee, O.Y. Kwong
    Pages52-60
    Number of pages8
    Volume3248
    DOIs
    Publication statusPublished - 2005
    EventFirst International Joint Conference on Natural Language Processing - IJCNLP 2004 - Hainan Island
    Duration: 1 Jul 2005 → …

    Conference

    ConferenceFirst International Joint Conference on Natural Language Processing - IJCNLP 2004
    CityHainan Island
    Period1/07/05 → …

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