非表示:
キーワード:
Computer Science, Information Retrieval, cs.IR,Computer Science, Computation and Language, cs.CL
要旨:
In order to adopt deep learning for information retrieval, models are needed
that can capture all relevant information required to assess the relevance of a
document to a given user query. While previous works have successfully captured
unigram term matches, how to fully employ position-dependent information such
as proximity and term dependencies has been insufficiently explored. In this
work, we propose a novel neural IR model named PACRR (Position-Aware
Convolutional-Recurrent Relevance), aiming at better modeling
position-dependent interactions between a query and a document via
convolutional layers as well as recurrent layers. Extensive experiments on six
years' TREC Web Track data confirm that the proposed model yields better
results under different benchmarks.
