***Apologies for multiple postings***

Reminder of the Cognitive Area Seminar this week:

Automated Interpretation of Idioms and Their Variants Fernando Sa-Pereira McGill University

Idioms pose an ongoing but stubborn challenge in Natural Language Processing (NLP). An idiom is taken here to mean a relatively fixed multiword expression whose meaning is different from what the literal meaning of its components would suggest, as in "that name rings a bell". The figurative nature of idioms is one aspect that makes idioms difficult for machines to interpret. To further complicate the problem, idioms often appear in some variant form as well, as in "it rings a faint bell". Corpus studies show that three out of ten sentences contain an idiomatic expression, and one half of idioms can appear in variant forms. So NLP applications, such as machine translation, sentiment analysis and information retrieval, must deal with idioms and their variants or misinterpret a large portion of the data. We tackle this problem by borrowing approaches used in distributional semantics, that is, methods to create mathematical representations of individual words based on the contexts in which the words appear. Since words that appear in similar contexts have related meanings, these representations capture some of the semantic information of a word. Correctly combining these representations can do the same for idiomatic expressions. In this presentation, I will provide a brief introduction to distributional semantics, and then show how we use these techniques to build representations of idiomatic expressions. I will then show how such representations can be deployed in three tasks: paraphrasing idiomatic expressions; distinguishing literal and figurative uses of these phrases (e.g., did she remember something or did she ring an actual bell?); and suggesting an appropriate idiomatic expression for a given context.

Friday, November 6th, 15h30-17h00 Stewart Biology Building, S 3/4

Please forward this message to your students and other interested parties. Thanks,

Esther

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