Abstract

This work studies the effectiveness of query expansion for email search. Three state-of-the-art expansion methods are examined: 1) a global translation-based expansion model; 2) a personalized-based word embedding model; 3) the classical pseudo-relevance-feedback model. Experiments were conducted with two mail datasets extracted from a large query log of a Web mail service. Our results demonstrate the significant contribution of query expansion for measuring the similarity between the query and email messages. On the other hand, the contribution of expansion methods for a well trained learning-to-rank scoring function that exploits many relevance signals, was found to be modest.

Introduction

Searching over email data has attracted a lot of attention recently and several attempts have been made by the research community to apply up-to-date ranking paradigms for email search [2, 4]. In these paradigms, the relevance of the message to the query is estimated by a complicated scoring function that considers many signals of relevance, including the message freshness, the textual similarity to the query, the user interaction with the message, and many more signals [2]. However, email queries which are extremely short become a severe limitation for an accurate estimation of message relevance to the query. While the average query length on the Web is about three terms per query, the average length in the email domain is only 1.5 terms per query [2].