Abstract
In Natural Language Processing (NLP), the quality of a system depends to a great extent on the quality of the linguistic resources it uses. One area where precise information is particularly needed is valency. The unpredictable character of valency properties requires a reliable source of information for syntactic and semantic analysis. There are several (electronic) dictionaries that provide the necessary information. One such dictionary that contains especially detailed valency descriptions is the Valency Dictionary of English. We will discuss how the Valency Dictionary of English in machine-readable form can be used as a resource for NLP. We will use valency descriptions that are freely available online via the Erlangen Valency Pattern Bank which contains most of the information from the printed dictionary. We will show that the valency data can be used for accurately parsing natural language with a rule-based approach by integrating it into a Left-Associative Grammar. The Valency Dictionary of English can therefore be regarded as being well suited for NLP purposes.- Anthology ID:
- L10-1031
- Volume:
- Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)
- Month:
- May
- Year:
- 2010
- Address:
- Valletta, Malta
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2010/pdf/62_Paper.pdf
- DOI:
- Cite (ACL):
- Thomas Proisl and Besim Kabashi. 2010. Using High-Quality Resources in NLP: The Valency Dictionary of English as a Resource for Left-Associative Grammars. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).
- Cite (Informal):
- Using High-Quality Resources in NLP: The Valency Dictionary of English as a Resource for Left-Associative Grammars (Proisl & Kabashi, LREC 2010)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2010/pdf/62_Paper.pdf