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JMLR
2012

Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing

11 years 5 months ago
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing
Open-text semantic parsers are designed to interpret any statement in natural language by inferring a corresponding meaning representation (MR – a formal representation of its sense). Unfortunately, large scale systems cannot be easily machine-learned due to a lack of directly supervised data. We propose a method that learns to assign MRs to a wide range of text (using a dictionary of more than 70,000 words mapped to more than 40,000 entities) thanks to a training scheme that combines learning from knowledge bases (e.g. WordNet) 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 wordsense disambiguation within the context of semantic parsing in a single elegant framework. Experiments on these various tasks indicate the promise of the approach.
Antoine Bordes, Xavier Glorot, Jason Weston, Yoshu
Added 27 Sep 2012
Updated 27 Sep 2012
Type Journal
Year 2012
Where JMLR
Authors Antoine Bordes, Xavier Glorot, Jason Weston, Yoshua Bengio
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