A word at a time: computing word relatedness using temporal semantic analysis

Computing the degree of semantic relatedness of words is a key functionality of many language applications such as search, clustering, and disambiguation. Previous approaches to computing semantic relatedness mostly used static language resources, while essentially ignoring their temporal aspects. We believe that a considerable amount of relatedness information can also be found in studying patterns of word usage over time. Consider, for instance, a newspaper archive spanning many years. Two words such as “war” and “peace” might rarely co-occur in the same articles, yet their patterns of use over time might be similar. In this paper, we propose a new semantic relatedness model, Temporal Semantic Analysis (TSA), which captures this temporal information. The previous state of the art method, Explicit Semantic Analysis (ESA), represented word semantics as a vector of concepts. TSA uses a more refined representation, where each concept is no longer scalar, but is instead represen...

• Type: Journal
• Year: 2011
• Where: WWW
• Authors: Kira Radinsky, Eugene Agichtein, Evgeniy Gabrilovich, Shaul Markovitch
• Keywords: Internet Technology, Semantic, Semantic Relatedness, Temporal Semantics, WWW 2011