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Incremental parsing is the task of assigning a syntactic structure to an input sentence as it unfolds word by word. Incremental parsing is more difficult than fullsentence parsing, as incomplete input increases ambiguity. Intuitively, an incremental parser that has access to semantic information should be able to reduce ambiguity by ruling out semantically implausible analyses, even for incomplete input. In this paper, we test this hypothesis by combining an incremental TAG parser with an incremental semantic role labeller in a discriminative framework. We show a substantial improvement in parsing performance compared to the baseline parser, both in full-sentence F-score and in incremental F-score.
|Title of host publication||Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)|
|Place of Publication||Beijing, China|
|Publisher||Association for Computational Linguistics|
|Number of pages||11|
|Publication status||Published - Jul 2015|
|Event||53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing - Beijing, China|
Duration: 26 Jul 2015 → 31 Jul 2015
|Conference||53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing|
|Abbreviated title||ACL-IJCNLP 2015|
|Period||26/07/15 → 31/07/15|
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