LSAfun - An R package for computations based on Latent Semantic Analysis

Abstract

In this article, the R package LSAfun is presented. This package enables a variety of functions and computations based on Vector Semantic Models such as Latent Semantic Analysis (LSA) Landauer, Foltz and Laham (Discourse Processes 25:259–284, 1998), which are procedures to obtain a high-dimensional vector representation for words (and documents) from a text corpus. Such representations are thought to capture the semantic meaning of a word (or document) and allow for semantic similarity comparisons between words to be calculated as the cosine of the angle between their associated vectors. LSAfun uses pre-created LSA spaces and provides functions for (a) Similarity Computations between words, word lists, and documents; (b) Neighborhood Computations, such as obtaining a word’s or document’s most similar words, (c) plotting such a neighborhood, as well as similarity structures for any word lists, in a two- or three-dimensional approximation using Multidimensional Scaling, (d) Applied Functions, such as computing the coherence of a text, answering multiple choice questions and producing generic text summaries; and (e) Composition Methods for obtaining vector representations for two-word phrases. The purpose of this package is to allow convenient access to computations based on LSA.