document: 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 represented as time series over a corpus of temporally-ordered documents . To the best of our knowledge , this is the first attempt to incorporate temporal evidence into models of semantic relatedness . Empirical evaluation shows that TSA provides consistent improvements over the state of the art ESA results on multiple benchmarks .
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