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We present the S-Space Package, an open source framework for developing and evaluating word space algorithms. The package implements well-known word space algorithms, such as LSA, and provides a comprehensive set of matrix utilities and data structures for extending new or existing models. The package also includes word space benchmarks for evaluation. Both algorithms and libraries are designed for high concurrency and scalability. We demonstrate the efficiency of the reference implementations and also provide their results on six benchmarks. . | The S-Space Package An Open Source Package for Word Space Models David Jurgens University of California Los Angeles 4732 Boelter Hall Los Angeles CA 90095 jurgens@cs.ucla.edu Keith Stevens University of California Los Angeles 4732 Boelter Hall Los Angeles CA 90095 kstevens@cs.ucla.edu Abstract We present the S-Space Package an open source framework for developing and evaluating word space algorithms. The package implements well-known word space algorithms such as LSA and provides a comprehensive set of matrix utilities and data structures for extending new or existing models. The package also includes word space benchmarks for evaluation. Both algorithms and libraries are designed for high concurrency and scalability. We demonstrate the efficiency of the reference implementations and also provide their results on six benchmarks. 1 Introduction Word similarity is an essential part of understanding natural language. Similarity enables meaningful comparisons entailments and is a bridge to building and extending rich ontologies for evaluating word semantics. Word space algorithms have been proposed as an automated approach for developing meaningfully comparable semantic representations based on word distributions in text. Many of the well known algorithms such as Latent Semantic Analysis Landauer and Dumais 1997 and Hyperspace Analogue to Language Burgess and Lund 1997 have been shown to approximate human judgements of word similarity in addition to providing computational models for other psychological and linguistic phenomena. More recent approaches have extended this approach to model phenomena such as child language acquisition Baroni et al. 2007 or semantic priming Jones et al. 2006 . In addition these models have provided insight in fields outside of linguistics such as information retrieval natural language processing and cognitive psychology. For a recent survey of word space approaches and applications see Turney and Pantel 2010 . The parallel development of .
