Context identification of sentences in related work sections using a conditional random field

Abstract

Identification of contexts associated with sentences is becoming increasingly necessary for developing intelligent information retrieval systems. This article describes a supervised learning mechanism employing a conditional random field (CRF) for context identification and sentence classification. Specifically, we focus on sentences in related work sections in research articles. Based on a generic rhetorical pattern, a framework for modelling the sequential flow in these sections is proposed. Adopting a generalization strategy, each of these sentences is transformed into a set of features, which forms our dataset. We distinguish between two kinds of features for each of these sentences viz., citation features and sentence features. While an overall accuracy of 96.51% is achieved by using a combination of both citation and sentence features, the use of sentence features alone yields an accuracy of 93.22%. The results also show F-Scores ranging from 0.99 to 0.90 for various classes indicating the robustness of our application.