Combining Distributional Semantics and Structured Data to Study Lexical Change

Abstract. Statistical Natural Language Processing (NLP) techniques allow to quantify lexical semantic change using large text corpora. Wordlevel results of these methods can be hard to analyse in the context of sets of semantically or linguistically related words. On the other hand, structured knowledge sources represent such relationships explicitly, but ignore the problem of semantic change. We aim to address these limitations by combining the statistical and symbolic approach: we enrich WordNet, a structured lexical database, with quantitative lexical change scores provided by HistWords, a dataset produced by distributional NLP methods. We publish the result as Linked Open Data and demonstrate how queries on the combined dataset can provide new insights.