Discovery of implicit and explicit connections between people using email utterance

Abstract

This paper is about finding explicit and implicit connections between people by mining semantic associations from their email communications. Following from a socio-cognitive stance, we propose a model called HALe which automatically derives dimensional representations of words in a high dimensional context space from an email corpus. These dimensional representations are used to discover a network of people based on a seed contextual description. Such a network represents useful connections between people not easily achievable by ‘normal’ retrieval means. Implicit connections are “lifted” by applying latent semantic analysis to the high dimensional context space. The discovery techniques are applied to a substantial corpus of real-life email utterance drawn from a small-to-medium size information technology organization. The techniques are computationally tractable, and evidence is presented that suggests appropriate explicit connections are being brought to light, as well as interesting, and perhaps serendipitous implicit connections. The ultimate goal of such techniques is to bring to light context-sensitive, ephemeral, and often hidden relationships between people, and between people and information, which pervade the enterprise.