Geospatial clustering is an important method for geospatial information analysis. However, most clustering methods do not consider semantic information during the clustering process. In this paper, we present a formal geospatial clustering ontology framework, which can provide the background for geospatial clustering. Using the ontology, geospatial clustering can become a knowledge-driven process.

Similarity analysis is a substantial issue in both corpus-based researches and language usages. This paper focuses on the semantic usages of adjectives, and analyzes the similarities among adjectives. The adjective and the semantic tag of the head noun that it modifies in a noun phrase form a co-occurrence. A two-stage algorithm is applied to clustering the adjectives according to these co-occu...

Processing items for their survival relevance often produces a robust memory advantage. The current experiments assessed possible proximate mechanisms responsible for this advantage by assessing output strategies during free recall. Previous research has shown that item clustering during recall can provide diagnostic information about the structure of representations in episodic memory, particu...

The effect of spam filtering method based on statistics is not good in filtering the new-type spam with synonymous substitution and camouflage. So a new text clustering method based on Semantic Body for filtering Chinese spam is proposed. In this paper, the word sense disambiguation, lexical chain based on HowNet and statistic-based TFIDF are adopted to extract features of mails. The Semantic B...

This paper proposes some modest improvements to Extractor, a state-of-the-art keyphrase extraction system, by using a terabyte-sized corpus to estimate the informativeness and semantic similarity of keyphrases. We present two techniques to improve the organization and remove outliers of lists of keyphrases. The first is a simple ordering according to their occurrences in the corpus; the second ...

This paper reports on the experiments and results of a clustering approach used in the INEX 2008 Document Mining Challenge. The clustering approach utilizes both the structure and the content information of the XML documents in the Wikipedia collection. The content of the XML documents is measured using the latent semantic kernel (LSK). A well-known problem with the construction of latent seman...

This paper presents a novel algorithm for semantic decoding in spoken language understanding systems. Unlike conventional semantic parsers which either use hand-crafted rules or statistical models trained from fully annotated data, the proposed approach uses an unsupervised sentence clustering technique called Y-clustering to automatically select a set of exemplar sentences from a training corp...

This paper presents a novel algorithm for semantic decoding in spoken language understanding systems. Unlike conventional semantic parsers which either use hand-crafted rules or statistical models trained from fully annotated data, the proposed approach uses an unsupervised sentence clustering technique called Y-clustering to automatically select a set of exemplar sentences from a training corp...

Lexical semantic space models have recently been investigated to automatically derive the meaning (semantics) of information based on natural language usage. In a semantic space, a term can be considered as a concept represented geometrically as a vector, the components of which correspond to terms in a vocabulary. A primary way to perform reasoning in a semantic space is to categorize concepts...

This work presents a clustering method which can be applied to relational knowledge bases. Namely, it can be used to discover interesting groupings of semantically annotated resources in a wide range of concept languages. The method exploits a novel dissimilarity measure that is based on the resource semantics w.r.t. a number of dimensions corresponding to a committee of features, represented b...