PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing
Antoine Bordes, Xavier Glorot, Jason Weston and Yoshua Bengio
Proceedings of the 15th International Conference on Artificial Intelligence and Statistics. JMLR W&CP 2011.


Open-text (or open-domain) semantic parsers are designed to interpret any state- ment in natural language by inferring a corresponding meaning representation (MR). Unfortunately, large scale systems cannot be easily machine-learned due to lack of directly supervised data. We propose here a method that learns to assign MRs to a wide range of text (using a dictionary of more than 70,000 words, which are mapped to more than 40,000 entities) thanks to a training scheme that combines learning from knowledge bases (WordNet and ConceptNet) with learning from raw text. The model jointly learns representations of words, entities and MRs via a multi-task training process operating on these diverse sources of data. Hence, the system ends up providing methods for knowledge acquisition and word-sense disambiguation within the context of semantic parsing in a single elegant framework. Experiments on these various tasks indicate the promise of the approach.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
Theory & Algorithms
ID Code:8599
Deposited By:Antoine Bordes
Deposited On:13 February 2012