Multilingual Semantic Parsing with a Pipeline of Linear Classifiers
2009 (English)In: Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009): Shared Task, Boulder, Colorado: Association for Computational Linguistics , 2009, 103-108 p.Conference paper (Refereed)
I describe a fast multilingual parser for semantic dependencies. The parser is implemented as a pipeline of linear classifiers trained with support vector machines. I use only first order features, and no pair-wise feature combinations in order to reduce training and prediction times. Hyper-parameters are carefully tuned for each language and sub-problem. The system is evaluated on seven different languages: Catalan, Chinese, Czech, English, German, Japanese and Spanish. An analysis of learning rates and of the reliance on syntactic parsing quality shows that only modest improvements could be expected for most languages given more training data; Better syntactic parsing quality, on the other hand, could greatly improve the results. Individual tuning of hyper-parameters is crucial for obtaining good semantic parsing quality.
Place, publisher, year, edition, pages
Boulder, Colorado: Association for Computational Linguistics , 2009. 103-108 p.
Semantic Role Labelling
Language Technology (Computational Linguistics) Language Technology (Computational Linguistics)
Research subject Computational Linguistics
IdentifiersURN: urn:nbn:se:uu:diva-120414OAI: oai:DiVA.org:uu-120414DiVA: diva2:303304
Thirteenth Conference on Computational Natural Language Learning (CoNLL 2009): Shared Task