• Calendar

January 1, Tuesday
12:00 – 13:00

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.