A Deep Relevance Model for Zero-Shot Document Filtering
Chenliang Li, Wei Zhou, Feng Ji, Yu Duan, Haiqing Chen · 2018
In the era of big data, focused analysis for diverse topics with a short response time becomes an urgent demand.As a fundamental task, information filtering therefore becomes a critical necessity.In this paper, we propose a novel deep relevance model for zero-shot document filtering, named DAZER.DAZER estimates the relevance between a document and a category by taking a small set of seed words relevant to the category.With pre-trained word embeddings from a large external corpus, DAZER is devised to extract the relevance signals by modeling the hidden feature interactions in the word embedding space.The relevance signals are extracted through a gated convolutional process.The gate mechanism controls which convolution filters output the relevance signals in a category dependent manner.Experiments on two document collections of two different tasks (i.e., topic categorization and sentiment analysis) demonstrate that DAZER significantly outperforms the existing alternative solutions, including the state-of-the-art deep relevance ranking models.