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Nathanael chambers and dan jurafsky

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  • We describe an unsupervised system for learning narrative schemas, coherent sequences or sets of events (arrested(POLICE , SUSPECT), convicted( JUDGE , SUSPECT )) whose arguments are filled with participant semantic roles defined over words (J UDGE = {judge, jury, court}, P OLICE = {police, agent, authorities}). Unlike most previous work in event structure or semantic role learning, our system does not use supervised techniques, hand-built knowledge, or predefined classes of events or roles.

    pdf9p hongphan_1 14-04-2013 15 1   Download

  • Hand-coded scripts were used in the 1970-80s as knowledge backbones that enabled inference and other NLP tasks requiring deep semantic knowledge. We propose unsupervised induction of similar schemata called narrative event chains from raw newswire text. A narrative event chain is a partially ordered set of events related by a common protagonist. We describe a three step process to learning narrative event chains. The first uses unsupervised distributional methods to learn narrative relations between events sharing coreferring arguments.

    pdf9p hongphan_1 15-04-2013 24 1   Download

  • This paper improves the use of pseudowords as an evaluation framework for selectional preferences. While pseudowords originally evaluated word sense disambiguation, they are now commonly used to evaluate selectional preferences. A selectional preference model ranks a set of possible arguments for a verb by their semantic fit to the verb. Pseudo-words serve as a proxy evaluation for these decisions.

    pdf9p hongdo_1 12-04-2013 46 2   Download

  • This paper describes a fully automatic twostage machine learning architecture that learns temporal relations between pairs of events. The first stage learns the temporal attributes of single event descriptions, such as tense, grammatical aspect, and aspectual class. These imperfect guesses, combined with other linguistic features, are then used in a second stage to classify the temporal relationship between two events. We present both an analysis of our new features and results on the TimeBank Corpus that is 3% higher than previous work that used perfect human tagged features. ...

    pdf4p hongvang_1 16-04-2013 28 2   Download

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