Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Decreasing lexical data sparsity in statistical syntactic parsing - experiments with named entities

Hogan, Deirdre, Foster, Jennifer orcid logoORCID: 0000-0002-7789-4853 and van Genabith, Josef orcid logoORCID: 0000-0003-1322-7944 (2011) Decreasing lexical data sparsity in statistical syntactic parsing - experiments with named entities. In: Multiword Expressions: from Parsing and Generation to the Real World (MWE). Workshop at ACL 2011, 19-24 June 2011, Portland, Oregon.

In this paper we present preliminary experiments that aim to reduce lexical data sparsity in statistical parsing by exploiting information about named entities. Words in the WSJ corpus are mapped to named entity clusters and a latent variable constituency parser is trained and tested on the transformed corpus. We explore two different methods for mapping words to entities, and look at the effect of mapping various subsets of named entity types. Thus far, results show no improvement in parsing accuracy over the best baseline score; we identify possible problems and outline suggestions for future directions.
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Uncontrolled Keywords:language corpus; Lexicalisation
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:Research Institutes and Centres > National Centre for Language Technology (NCLT)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:16465
Deposited On:05 Aug 2011 12:59 by Shane Harper . Last Modified 19 Jan 2022 12:49

Full text available as:

[thumbnail of Decreasing_lexical_data_sparsity_in_statistical_syntactic_parsing_-_experiments_with_named_entities.pdf]
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader


Downloads per month over past year

Archive Staff Only: edit this record