@article{
 title = {Recognizing textual relatedness with predicate-argument structures},
 type = {article},
 year = {2009},
 identifiers = {[object Object]},
 pages = {784},
 volume = {2},
 websites = {http://portal.acm.org/citation.cfm?doid=1699571.1699615},
 publisher = {Association for Computational Linguistics},
 id = {4c77eadd-9bed-3693-8652-89c54347a8df},
 created = {2012-05-10T14:51:03.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {predicate argument structures,semantic relatedness},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Wang2009a},
 private_publication = {false},
 abstract = {In this paper, we first compare several strategies to handle the newly proposed three-way Recognizing Textual Entailment (RTE) task. Then we define a new measurement for a pair of texts, called Textual Relatedness, which is a weaker concept than semantic similarity or paraphrase. We show that an alignment model based on the predicate-argument structures using this measurement can help an RTE system to recognize the Unknown cases at the first stage, and contribute to the improvement of the overall performance in the RTE task. In addition, several heterogeneous lexical resources are tested, and different contributions from them are observed.},
 bibtype = {article},
 author = {Wang, Rui and Zhang, Yi},
 journal = {Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 2 EMNLP 09},
 number = {August}
}

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