SimDoc: Topic Sequence Alignment based Document Similarity Framework

TL;DR

SimDoc introduces a novel framework that models documents as topic sequences and uses sequence alignment to accurately measure semantic similarity, outperforming traditional bag-of-words methods in clustering tasks.

Contribution

The paper presents a new semantic similarity framework based on topic-sequence modeling and sequence alignment, capturing thematic flow often ignored by existing methods.

Findings

SimDoc outperforms bag-of-words techniques in accuracy

Effective in document clustering applications

Introduces a novel topic-topic similarity measure

Abstract

Document similarity is the problem of estimating the degree to which a given pair of documents has similar semantic content. An accurate document similarity measure can improve several enterprise relevant tasks such as document clustering, text mining, and question-answering. In this paper, we show that a document's thematic flow, which is often disregarded by bag-of-word techniques, is pivotal in estimating their similarity. To this end, we propose a novel semantic document similarity framework, called SimDoc. We model documents as topic-sequences, where topics represent latent generative clusters of related words. Then, we use a sequence alignment algorithm to estimate their semantic similarity. We further conceptualize a novel mechanism to compute topic-topic similarity to fine tune our system. In our experiments, we show that SimDoc outperforms many contemporary bag-of-words techniques in accurately computing document similarity, and on practical applications such as document clustering.