Word association norms, mutual information, and lexicography
Computational Linguistics
Visualisation Techniques for Analysing Meaning
TSD '02 Proceedings of the 5th International Conference on Text, Speech and Dialogue
Random graph model simulations of semantic networks for associative concept dictionaries
TextGraphs-3 Proceedings of the 3rd Textgraphs Workshop on Graph-Based Algorithms for Natural Language Processing
Graph-based term weighting for information retrieval
Information Retrieval
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Within the general enterprise of probing into the complexities of lexical knowledge, one particularly promising research focus is on word association knowledge. Given Deese's [1] and Cramer's [2] convictions that word association closely mirror the structured patterns of relations that exist among concepts, as largely echoed Hirst's [3] more recent comments about the close relationships between lexicons and ontologies, as well as Firth's [4] remarks about finding a word's meaning in the company it keeps, efforts to capture and unravel the rich networks of associations that connect words together are likely to yield interesting insights into the nature of lexical knowledge. Adopting such an approach, this paper applies a range of network analysis techniques in order to investigate the characteristics of network representations of word association knowledge in Japanese. Specifically, two separate association networks are constructed from two different large-scale databases of Japanese word associations: the Associative Concept Dictionary (ACD) by Okamoto and Ishizaki [5] and the Japanese Word Association Database (JWAD) by Joyce [6] [7] [8]. Results of basic statistical analyses of the association networks indicate that both are scale-free with smallworld properties and that both exhibit hierarchical organization. As effective methods of discerning associative structures with networks, some graph clustering algorithms are also applied. In addition to the basic Markov Clustering algorithm proposed by van Dongen [9], the present study also employs a recently proposed combination of the enhanced Recurrent Markov Cluster algorithm (RMCL) [10] with an index of modularity [11]. Clustering results show that the RMCL and modularity combination provides effective control over cluster sizes. The results also demonstrate the effectiveness of graph clustering approaches to capturing the structures within large-scale association knowledge resources, such as the two constructed networks of Japanese word associations.