Can the human association norm evaluate machine-made association lists (original) (raw)

2017

Abstract

Abstract: For more than three decades, there has been a commonly shared belief that word occurrences retrieved from a large text collection may define the lexical meaning of a word. Although there are some suggestions that co-occurrences retrieved from texts reflect the text’s contiguities, there also exist suggestions that algorithms, such as the LSA, are unable to distinguish between co-occurrences which are corpus-independent semantic dependencies (elements of a semantic prototype) and co-occurrences which are corpus-dependent factual dependencies. We shall adopt the second view to show that existing statistical algorithms use mechanisms which improperly filter word co-occurrences retrieved from texts. To prove this supposition, we shall compare the human association list to the association list retrieved from a text by three different algorithms, i.e. the Church–Hanks algorithm, the Latent Semantic Analysis (LSA) algorithm and the Latent Dirichlet Allocation (LDA) algorithm.

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