Accounting for Language Changes over Time in Document Similarity Search

Abstract

Given a query document, ranking the documents in a collection based on how similar they are to the query is an essential task with extensive applications. For collections that contain documents whose creation dates span several decades, this task is further complicated by the fact that the language changes over time. For example, many terms add or lose one or more senses to meet people's evolving needs. To address this problem, we present methods that take advantage of two types of information in order to account for the language change. The first is the citation network that often exists within the collection, which can be used to link related documents with significantly different creation dates (and hence different language use). The second is the changes in the usage frequency of terms that occur over time, which can indicate changes in their senses and uses. These methods utilize the above information while estimating the representation of both documents and terms within the context of non-probabilistic static and dynamic topic models. Our experiments on two real-world datasets that span more than 40 years show that our proposed methods improve the retrieval performance of existing models and that these improvements are statistically significant.