Memory-restricted latent semantic analysis to accumulate term-document co-occurrence events

This paper addresses a novel adaptive problem of obtaining a new type of term-document weight. In our problem, an input is given by a long sequence of co-occurrence events between terms and documents, namely, a stream of term-document co-occurrence events. Given a stream of term-document co-occurrences, we learn unknown latent vectors of terms and documents such that their inner product adaptively approximates the target query-based term-document weights resulting from accumulating co-occurrence events. To this end, we propose a new incremental dimensionality reduction algorithm for adaptively learning a latent semantic index of terms and documents over a collection. The core of our algorithm is its partial updating style, where only a small number of latent vectors are modified for each term-document co-occurrence, while most other latent vectors remain unchanged. Experimental results on small and large standard test collections demonstrate that the proposed algorithm can stably learn the latent semantic index of terms and documents, showing an improvement in the retrieval performance over the baseline method.

► The input of our task is given by a long sequence of term-document co-occurrence events.
► The goal of our task is to learn term-documents weights give input stream.
► The weight between term and document is proportional to their co-occurrence rate.
► We propose a dimensionality reduction that approximates target term-document weights.
► Experiment results show that our algorithm learns gradually the target weight.