A Chinese character embedded in different compound words may carry different meanings. In this paper, we aim at semantic clustering of a given family of morphologically related Chinese words. In Experiment 1, we employed linguistic features at the word, syntactic, semantic, and contextual levels in aggregated computational linguistics methods to handle the clustering task. In Experiment 2, we r...

In earlier work, we have shown how clustering techniques can transform semantic markup into discourse, which style sheets can transform into hypermedia presentations. This paper discusses the different clustering techniques for deriving discourse from semantics. These techniques use properties, relations and numerical properties of information objects and semantic networks. We show how these cl...

SUMMARY Despite the importance of using the semantic distance to improve the performance of conventional expression-based clustering, there are few freely available software that provides a clustering algorithm using the ontology-based semantic distances as prior knowledge. Here, we present the SICAGO (SemI-supervised Cluster Analysis using semantic distance between gene pairs in Gene Ontology)...

BACKGROUND/AIMS Verbal fluency is impaired in patients with frontotemporal dementia (FTD) and primary progressive aphasia (PPA). This study explored qualitative differences in verbal fluency (clustering of words, switching between strategies) between FTD and PPA variants. METHODS Twenty-nine patients with behavioral variant FTD (bvFTD) and 50 with PPA (13 nonfluent/agrammatic, 14 semantic, an...

A semantics-based method for density-based clustering with constraints imposed by geographical background knowledge is proposed. In this paper, we apply an ontological approach to the DBSCAN (Density-Based Geospatial Clustering of Applications with Noise) algorithm in the form of knowledge representation for constraint clustering. When used in the process of clustering geographic information, s...

Suffix Tree Clustering (STC) uses the suffix tree structure to find a set of snippets that share a common phrase and uses this information to propose clusters. As a result, STC is a fast incremental algorithm for automatic clustering and labeling but it cannot cluster semantically similar snippets. However, the meaning of the words is indeed an important property that relates them to other word...

This paper describes the analysis of possible differentiation of the author’s idiolect in the space of semantic fields; it also analyzes the clustering of text documents in the vector space of semantic fields and in the semantic space with orthogonal basis. The analysis showed that using the vector space model on the basis of semantic fields is efficient in cluster analysis algorithms of author...

We enhance the accuracy of the currently available semantic hashtag clustering method, which leverages hashtag semantics extracted from dictionaries such as Wordnet and Wikipedia. While immune to the uncontrolled and often sparse usage of hashtags, the current method distinguishes hashtag semantics only at the word-level. Unfortunately, a word can have multiple senses representing the exact sem...

We enhance the accuracy of the currently available semantic hashtag clustering method, which leverages hashtag semantics extracted from dictionaries such as Wordnet and Wikipedia. While immune to the uncontrolled and often sparse usage of hashtags, the current method distinguishes hashtag semantics only at the word level. Unfortunately, a word can have multiple senses representing the exact sem...

Importance of document clustering is now widely acknowledged by researchers for better management, smart navigation, efficient filtering, and concise summarization of large collection of documents like World Wide Web (WWW). The next challenge lies in semantically performing clustering based on the semantic contents of the document. The problem of document clustering has two main components: (1)...