Abstract
Syntactic parsers perform poorly in prediction of Argument-Cluster Coordination (ACC).We change the PTB representation of ACC to be more suitable for learning by a statistical PCFG parser, affecting 125 trees in the training set. Training on the modified trees yields a slight improvement in EVALB scores on sections 22 and 23. The main evaluation is on a corpus of 4th grade science exams, in which ACC structures are prevalent. On this corpus, we obtain an impressive ×2.7 improvement in recovering ACC structures compared to a parser trained on the original PTB trees.
| Original language | English |
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| Title of host publication | 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Short Papers |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 72-76 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781510827592 |
| DOIs | |
| State | Published - 2016 |
| Event | 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Berlin, Germany Duration: 7 Aug 2016 → 12 Aug 2016 |
Publication series
| Name | 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Short Papers |
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Conference
| Conference | 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 |
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| Country/Territory | Germany |
| City | Berlin |
| Period | 7/08/16 → 12/08/16 |
Bibliographical note
Publisher Copyright:© 2016 Association for Computational Linguistics.