Application of Low Rank Online Multimodal Distance Metric Learning Technique for Semantic Hashing of Web Documents

Abstract: Semantic hashing for web documents is essential for effective information dissemination. This paper is a sincere effort towards application of a novel method which outputs a semantic hash for an input web document. The need of such method arises as a result of research search where user may be so na�ve that they are unaware of domain specific keywords or any labels for satisfying their search goals. The proposed technique in this paper assigns rank from 1 to n based on highly relevant modals of a web document. We have used six of such modals and duly considered their impact in finding semantics of a web document when hashing with a user input document. The algorithm is used in many information retrieval systems and employs a distance metric learning mechanism in practice.

Keywords: Semantic, Retrieval, Multi-Modal, Context, LOMDML.