• Calendar

December 22, Tuesday
12:00 – 14:00

Two of most serious and fundamental problems Natural Language Processing are data sparsity and non-local reasoning. For example, let's consider the task of identifying people, locations and organizations in the following text (taken from Reuters): "BLINKER BAN LIFTED. Dutch forward Reggie Blinker had his indefinite suspension lifted by FIFA on Friday and was set to make his Sheffield Wednesday comeback on Saturday… Blinker missed his club's last two games after FIFA slapped a worldwide ban on him for appearing to sign contracts for both Wednesday and Udinese …" Many of the words, like 'Udinese' and 'Sheffield' are rare words that are unlikely to appear in the training data. On the other hand, the words 'Blinker' and 'Wednesday' in this text refer to a player and to a soccer team, and successfully identifying them as such requires global understanding of the text. In this talk I will discuss algorithms for reducing data sparsity and making non-local decisions in NLP. the running example will be the task of named entity recognition. Bio. Lev Ratinov received his BSc and MSc from BGU, now he's a Phd student in University of Illinois at Urbana-Champaign. He has published at ACL, AAAI, and gave a tutorial at EACL