In this paper we describe an unsupervised method for semantic role induction which holds promise for relieving the data acquisition bottleneck associated with supervised role labelers. We present an algorithm that iteratively splits and merges clusters representing semantic roles, thereby leading from an initial clustering to a final clustering of better quality. The method is simple, surprisin...

Semantic clustering is important to various fields in the modern information society. In this work we applied the Independent Component Analysis method to the extraction of the features of latent concepts. We used verb and object noun information and formulated a concept as a linear combination of verbs. The proposed method is shown to be suitable for our framework and it performs better than a...

Two components of verbal fluency performance--clustering (i.e., generating words within subcategories) and switching (i.e., shifting between subcategories)--were examined in patients with dementia of the Alzheimer type (DAT), patients with dementia with Parkinson's disease (DPD), nondemented patients with Parkinson's disease (NPD), and demographically matched controls. The DAT and DPD groups we...

Clustering text data streams is an important issue in data mining community and has a number of applications such as news group filtering, text crawling, document organization and topic detection and tracing etc. However, most methods are similarity-based approaches and use the TF*IDF scheme to represent the semantics of text data and often lead to poor clustering quality. In this paper, we fir...

In traditional document clustering methods, a document is considered a bag of words. The fact that the words may be semantically relateda crucial information for clusteringis not taken into account. In this paper we describe a new method for generating feature vectors, using the semantic relations between the words in a sentence. The semantic relations are captured by the Universal Networking L...

The tools to analyze and visualize information from multiple, inhomogeneous sources have traditionally relied on improvements in statistical methods. The results from statistical methods, however, overlook relevant semantic features present within natural language and text-based information. Emerging research in ontology languages (e.g. RDF, RDFS, SUOKIF, and OWL) offers promising avenues for o...

Although verbal fluency is a frequently used neuropsychological test, little is known about the underlying cognitive processes. The authors proposed that 2 important components of fluency performance are clustering (i.e., the production of words within semantic or phonemic subcategories) and switching (i.e., the ability to shift between clusters). In Experiment 1, correlational data from 54 old...

<p><span>Semantic similarity is the process of identifying relevant data semantically. The traditional way document by using synonymous keywords and syntactician. In comparison, semantic to find similar meaning words semantics. Clustering a concept grouping objects that have same features properties as cluster separate from those different properties. clustering, documents are clust...

Measuring semantic similarity between words is vital for various applications in natural language processing, such as language modeling, information retrieval, and document clustering. We propose a method that utilizes the information available on the Web to measure semantic similarity between a pair of words or entities. We integrate page counts for each word in the pair and lexico-syntactic p...

In this paper, we present a semi-supervised semantic clustering method based on Support Vector Machines (SVM) to organize the 3D models semantically. Ground truth data is used to identify the pattern of each semantic category by supervised learning. Unknown data is then automatically classified and clustered based on the resulting pattern. We also propose a unified search strategy which applies...