similarity can be operationalized in many different ways. There is a rich variety of computational models implementing distributional semantics, including latent semantic analysis (LSA), Hyperspace Analogue to Language (HAL), syntax- or dependency-based models, random indexing, semantic folding and various variants of the topic model. Distributional semantic models differ primarily with respect to the following parameters: Context type (text regions vs. linguistic items) Context window (size, extension, etc.) Frequency weighting (e.g. entropy, pointwise mutual information, etc.) Dimension reduction (e.g. random indexing, singular value decomposition, etc.) Similarity measure (e.g. cosine similarity, Minkowski distance, etc.) Distributional semantic models that use linguistic items as context have also been referred to as word space, or vector space models. Beyond Lexical Semantics While distributional semantics typically has been applied to lexical items—words and multi-word terms—with considerable success, not least due to its applicability as an input layer for neurally inspired deep learning models, lexical semantics, i.e. the meaning of words, will only carry part of the semantics of an entire utterance. The meaning of a clause, e.g. "Tigers love rabbits.", can only partially be understood from examining the meaning of the three lexical items it consists of. Distributional semantics can straightforwardly be extended to cover larger linguistic item such as constructions, with and without non-instantiated items, but some of the base assumptions of the model need to be adjusted somewhat. Construction grammar and its formulation of the lexical-syntactic continuum offers one approach for including more elaborate constructions in a distributional semantic model and some experiments have been implemented using the Random Indexing approach. Compositional distributional semantic models extend distributional semantic models by explicit semantic functions that use syntactically based rules to combine the semantics of participating lexical units into a compositional model to characterize the semantics of entire phrases or sentences. This work was originally proposed by Stephen Clark, Bob Coecke, and Mehrnoosh Sadrzadeh of Oxford University in their 2008 paper, "A Compositional Distributional Model of Meaning". Different approaches to composition have been explored—including neural models—and are under discussion at established workshops such as SemEval. Applications Distributional semantic models have been applied successfully to the following tasks: finding semantic similarity between words and multi-word expressions; word clustering based on semantic similarity; automatic creation of thesauri and bilingual dictionaries; word sense disambiguation; expanding search requests using synonyms and associations; defining the topic of a document; document clustering for information retrieval; data mining and named entities recognition; creating