2,561 research outputs found
Task-Oriented Query Reformulation with Reinforcement Learning
Search engines play an important role in our everyday lives by assisting us
in finding the information we need. When we input a complex query, however,
results are often far from satisfactory. In this work, we introduce a query
reformulation system based on a neural network that rewrites a query to
maximize the number of relevant documents returned. We train this neural
network with reinforcement learning. The actions correspond to selecting terms
to build a reformulated query, and the reward is the document recall. We
evaluate our approach on three datasets against strong baselines and show a
relative improvement of 5-20% in terms of recall. Furthermore, we present a
simple method to estimate a conservative upper-bound performance of a model in
a particular environment and verify that there is still large room for
improvements.Comment: EMNLP 201
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
In domains like bioinformatics, information retrieval and social network
analysis, one can find learning tasks where the goal consists of inferring a
ranking of objects, conditioned on a particular target object. We present a
general kernel framework for learning conditional rankings from various types
of relational data, where rankings can be conditioned on unseen data objects.
We propose efficient algorithms for conditional ranking by optimizing squared
regression and ranking loss functions. We show theoretically, that learning
with the ranking loss is likely to generalize better than with the regression
loss. Further, we prove that symmetry or reciprocity properties of relations
can be efficiently enforced in the learned models. Experiments on synthetic and
real-world data illustrate that the proposed methods deliver state-of-the-art
performance in terms of predictive power and computational efficiency.
Moreover, we also show empirically that incorporating symmetry or reciprocity
properties can improve the generalization performance
- …