Learning for Sequence Extraction Tasks.

Abstract : 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 liner 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.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01572583
Contributor : Lip6 Publications <>
Submitted on : Monday, August 7, 2017 - 5:34:56 PM
Last modification on : Thursday, March 21, 2019 - 1:09:33 PM

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  • HAL Id : hal-01572583, version 1

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Massih-Reza Amini, Hugo Zaragoza, Patrick Gallinari. Learning for Sequence Extraction Tasks.. 6th Conference on "Content-Based Multimedia Information Access" (RIAO'2000), Apr 2000, Paris, France. pp.476-490. ⟨hal-01572583⟩

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