Unifying Learning to Rank and Domain Adaptation: Enabling Cross-Task Document Scoring
published: Oct. 7, 2014, recorded: August 2014, views: 1848
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
For document scoring, although learning to rank and domain adaptation are treated as two different problems in previous works, we discover that they actually share the same challenge of adapting keyword contribution across different queries or domains. In this paper, we propose to study the cross-task document scoring problem, where a task refers to a query to rank or a domain to adapt to, as the first attempt to unify these two problems. Existing solutions for learning to rank and domain adaptation either leave the heavy burden of adapting keyword contribution to feature designers, or are difficult to be generalized. To resolve such limitations, we abstract the keyword scoring principle, pointing out that the contribution of a keyword essentially depends on, first, its importance to a task and, second, its importance to the document. For determining these two aspects of keyword importance, we further propose the concept of feature decoupling, suggesting using two types of easy-to-design features: meta-features and intra-features. Towards learning a scorer based on the decoupled features, we require that our framework fulfill inferred sparsity to eliminate the interference of noisy keywords, and employ distant supervision to tackle the lack of keyword labels. We propose the Tree-structured Boltzmann Machine (T-RBM), a novel two-stage Markov Network, as our solution. Experiments on three different applications confirm the effectiveness of T-RBM, which achieves significant improvement compared with four state-of-the-art baseline methods.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !