Learning for Sequence Extraction Tasks

We consider the application of machine learning techniques for sequence modeling to Information Retrieval (IR) and surface Information Extraction (IE) tasks. We introduce a generic sequence model and show how it can be used for dealing with different closed-query tasks. Taking into account the sequential nature of texts allows for a finer analysis than what is usually done in IR with static text representations. The task we are focusing on is the retrieval and labeling of texts passages, also known as highlighting and surface information extraction. We describe different implementations of our model based on Hidden Markov Models and Neural Networks. Experiments are performed using the MUC6 corpus from the information extraction community.

• Type: Conference
• Year: 2000
• Where: RIAO
• Authors: Massih-Reza Amini, Hugo Zaragoza, Patrick Gallinari
• Keywords: Generic Sequence Model, Information Extraction, RIAO 2000, RIAO 2007, Surface Information Extraction