A Framework for Task-specific Short Document Expansion

Abstract

Collections that contain a large number of short texts are becoming increasingly common (eg., tweets, reviews, etc). Analytical tasks (such as classification, clustering, etc.) involving short texts could be challenging due to the lack of context and owing to their sparseness. An often encountered problem is low accuracy on the task. A standard technique used in the handling of short texts is expanding them before subjecting them to the task. However, existing works on short text expansion suffer from certain limitations: (i) they depend on domain knowledge to expand the text; (ii) they employ task-specific heuristics; and (iii) the expansion procedure is tightly coupled to the task. This makes it hard to adapt a procedure, designed for one task, into another. We present an expansion technique -- TIDE (Task-specIfic short Document Expansion) -- that can be applied on several Machine Learning, NLP and Information Retrieval tasks on short texts (such as short text classification, clustering, entity disambiguation, and the like) without using task specific heuristics and domain-specific knowledge for expansion. At the same time, our technique is capable of learning to expand short texts in a task-specific way. That is, the same technique that is applied to expand a short text in two different tasks is able to learn to produce different expansions depending upon what expansion benefits the task's performance. To speed up the learning process, we also introduce a technique called block learning. Our experiments with classification and clustering tasks show that our framework improves upon several baselines according to the standard evaluation metrics which includes the accuracy and normalized mutual information (NMI).