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Inducing Meaning from Text

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Online models of word meaning (like dictionaries and thesauri) or world knowledge (like scripts or narratives) are crucial for natural language understanding. Could we learn these meanings automatically from text? I first report on joint work with Rion Snow and Andrew Ng on inducing the meaning of words from text on the Web in the context of augmenting WordNet, a large online thesaurus of English. These include a semi-supervised method for learning when a new word is a `hypernym’ or in the ‘is-a’ relation with another word, a new probabilistic algorithm for combining evidence from multiple relation detectors, and a algorithm for clustering the induced word senses. I then report on joint work with Nate Chambers on inducing `narratives’, a script-like sequence of events that follow a protagonist. This work includes inducing the relations between events, ordering the relations and clustering them into prototype narratives.

This talk is part of the Machine Intelligence Laboratory Speech Seminars series.

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