A procedure that processes a corpus of text and produces numeric vectors containing information about its meanings for each word is presented. This procedure is applied to a large corpus of natural language text taken from Usenet, and the resulting vectors are examined to determine what information is contained within them. These vectors provide the coordinates in a high-dimensional space in which word relationships can be analyzed. Analyses of both vector similarity and multidimensional scaling demonstrate that there is significant semantic information carried in the vectors. A comparison of vector similarity with human reaction times in a single-word priming experiment is presented. These vectors provide the basis for a representational model of semantic memory, hyperspace analogue to language (HAL).
Object-Based SemanticsMany models of semantics have attempted to address the preceding questions. These models can be categorized A specification of the structural characteristicsof the mental lexicon is a central goal in word recognition research. Of various word-level characteristics,semantics remains the most resistant to this endeavor. Although there are several theoretically distinct models of lexical semantics with fairly clear operational definitions (e.g., in terms of feature sharing, category membership, associations, or cooccurrences), attempts to empirically ad judicate between these different models have been scarce. In this paper, we present several experiments in which we examined the effects of semantic neighborhood size as defined by two models of lexical semantics-one that defines semantics in terms of associations, and another that defines it in terms of global co-occurrences. We present data that address the question of whether these measures can be fruitfully applied to examinations of lexical activation during visual word recognition. The findings demonstrate that semantic neighborhood can predict performance on both lexical decision and word naming.
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