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Machine Learning Ranking for Structured Information Retrieval


We consider the Structured Information Retrieval task which consists in ranking nested textual units according to their relevance for a given query, in a collection of structured documents. We propose to improve the performance of a baseline Information Retrieval system by using a learning ranking algorithm which operates on scores computed from document elements and from their local structural context. This model is trained to optimize a Ranking Loss criterion using a training set of annotated examples composed of queries and relevance judgments on a subset of the document elements. The model can produce a ranked list of documents elements which fulfills a given information need expressed in the query. We analyze the performance of our algorithm on the INEX collection and compare it to a baseline model which is an adaptation of Okapi to Structured Information Retrieval.

Dates and versions

hal-01337638 , version 1 (27-06-2016)



Jean-Noël Vittaut, Patrick Gallinari. Machine Learning Ranking for Structured Information Retrieval. European Conference on Information Retrieval (ECIR 2006), Apr 2006, London, United Kingdom. pp.338-349, ⟨10.1007/11735106_30⟩. ⟨hal-01337638⟩
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