Event № 235

A large number of Natural Language Processing applications, including  syntactic parsing, information extraction and discourse analysis,  involve the prediction of a linguistic structure. It is often times  challenging for standard feature-based machine learning algorithms to  perform well on these tasks due to modeling and computational reasons.
 Moreover, creating the large amounts of manually annotated data  required to train supervised models for such applications is usually  labor intensive and error prone.

 In this talk we describe a serious of works that integrate feature  based methods with declarative task and domain knowledge in a unified  framework. We address a wide variety of NLP tasks and domain
 knowledge: for syntactic parsing we show how to parse multiple  sentences together while imposing consistency constraints, for  information extraction we present a joint model that ties together a  number of related tasks through task and domain constraints and for  discourse analysis we present a model that exploit within and cross  document regularities in a collection of documents.
 Our models are implemented in the Markov Random Field (MRF) framework  and the resulted global hard optimization task is addressed by  approximate inference techniques based on linear programming (LP)  relaxations. We present improvements over state of the art models in  five languages and a wide range of supervision levels - from fully  unsupervised to fully supervised scenarios.


 Short bio:

 Roi Reichart is a post-doctoral researcher at the Computer Laboratory  of the University of Cambridge where he works with Dr. Anna Korhonen.
 Before that he completed his PhD (June 2010)  in the Hebrew University  under the supervision of Prof. Ari Rappoport and has been a  post-doctoral associate at the Computer Science and Artificial  Intelligence laboratory in the Massachusetts Institute of Technology  (MIT).

 His main research interests are unsupervised and semi-supervised  learning in NLP, especially for syntactic acquisition and lexical  semantics tasks. His paper on active learning for syntactic parsing  (together with Ari Rappoport) has won the best paper award in CoNLL  2009. He is a recipient of the ISF bikura fellowship for outstanding  Israeli post-docs..