Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference. This leads to a novel approach to semantic representation in which word meanings are represented in terms of a set of probabilistic topics. The topic model performs well in predicting word association and the effects of semantic association and ambiguity on a variety of language-processing and memory tasks. It also provides a foundation for developing more richly structured statistical models of language, as the generative process assumed in the topic model can easily be extended to incorporate other kinds of semantic and syntactic structure.

Charles and Miller propose that lexical associations between antonymous adjectives are formed via their co-occurrences within the same sentence (the co-occurrence hypothesis), rather than via their syntactic substitutability (the substitutability hypothesis), and that such cooccurrences must take place more often than expected by chance. This paper provides empirical support for the co-occurrence hypothesis, in a corpus analysis of all high-frequency adjectives and their antonyms and of a major group of morphologically derived antonyms (e.g., impossible, un-happy). We show that very high co-occurrence rates do appear to characterize all antonymous adjective pairs, supporting the precondition for the formation of the association; and we find that the syntactic contexts of these co-occurrences raise the intrinsic associability of antonyms when they do co-occur. We show that via one of these patterns, mutual substitution within otherwise repeated phrases in a sentence, the co-occurrence hypothesis captures the generalizations that were the basis for the substitutability hypothesis for the formation of antonymic associations. 1. Antonymic Association Much current research in linguistics is concerned with textual or discourse bases for

compared on their ability to extract syntactic, semantic and associative information from a corpus of text. On a measure of syntactic class (and subclass) information based on the WordNet lexical database (Miller 1990), the models performed similarly with a small advantage for the PAC model. On a measure of semantic structure based on the similarities produced by Latent Semantic Analysis (LSA; Landauer & Dumais 1997), the models performed equivalently with a small advantage for the SP model. On a measure of associative information based on the free association norms of Nelson, McEvoy & Schreiber (1999), the SP model shows a substantive advantage over the PAC model producing more than twice as many associates.

This paper presents the co-occurrence dictionary based on Thai phenomena. The theoretical background, the data structure, the dictionary development and word collocation information are described in details. At present, 75,000 word collocations have been added in the co-occurrence dictionary with the help of linguists who made much effort in encoding the linguistic information. Hopefully, the word collocation information presented in this paper will be the useful resources for the natural language processing studies, and second language acquisition.

The Syntagmatic Paradigmatic (SP) model is a distributed, instance-based account of sentence processing. Built on the Minerva II model of episodic memory (Hintzman 1988), it characterizes sentence processing as the retrieval of sets of associative constraints from long-term memory and the resolution of these constraints in working memory. In common with connectionist approaches, the SP model provides a data-driven account of language learning and does not make strong a priori assumptions concerning the nature of syntactic knowledge.