PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Machine learning ranking for structured information retrieval
Jean-Noel Vittaut and Patrick Gallinari
In: 28th European Conference on IR Research, ECIR 2006, 10-12 Apr 2006, London, United Kingdom.

Abstract

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:2889
Deposited By:Jean-Noel Vittaut
Deposited On:23 November 2006