We present an empirically grounded method for evaluating content selection in summarization. It incorporates the idea that no single best model summary for a collection of documen...
One of the first steps towards understanding natural multimodal language is aligning gesture and speech, so that the appropriate gestures ground referential pronouns in the speech...
We demonstrate a new research approach to the problem of predicting the reading difficulty of a text passage, by recasting readability in terms of statistical language modeling. W...
We present a technique for augmenting annotated training data with hierarchical word clusters that are automatically derived from a large unannotated corpus. Cluster membership is...
State-of-the-art story link detection systems, that is, systems that determine whether two stories are about the same event or linked, are usually based on the cosine-similarity m...
A given entity, representing a person, a location or an organization, may be mentioned in text in multiple, ambiguous ways. Understanding natural language requires identifying whe...
We present a coreference resolver called BABAR that uses contextual role knowledge to evaluate possible antecedents for an anaphor. BABAR uses information extraction patterns to i...
We consider the problem of modeling the content structure of texts within a specific domain, in terms of the topics the texts address and the order in which these topics appear. W...