OBJECTIVE To semi-automatically induce semantic categories of eligibility criteria from text and to automatically classify eligibility criteria based on their semantic similarity. DESIGN The UMLS semantic types and a set of previously developed semantic preference rules were utilized to create an unambiguous semantic feature representation to induce eligibility criteria categories through hie...

Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. I...

a bilingual corpus is considered as a very important knowledge source and an inevitable requirement for many natural language processing (nlp) applications in which two languages are involved. for some languages such as persian, lack of such resources is much more significant. several applications, including statistical and example-based machine translation needs bilingual corpora, in which lar...

Two well-known databases of semantic relationships between pairs of words used in psycholinguistics, feature-based and association-based, are studied as complex networks. We propose an algorithm to disentangle feature based relationships from free association semantic networks. The algorithm uses the rich topology of the free association semantic network to produce a new set of relationships be...

This paper proposes a new technique for auto-annotation and semantic retrieval based upon the idea of linearly mapping an image feature space to a keyword space. The new technique is compared to several related techniques, and a number of salient points about each of the techniques are discussed and contrasted. The paper also discusses how these techniques might actually scale to a real-world r...

This paper proposed a unified framework to generate consistent segmentation and annotate part semantic for 3D mesh simultaneously. The core idea of the proposed framework is to bridge the semantic gap between low-level geometry feature and high-level semantic feature by structure ontology. The structure ontology makes a consistence representation for mesh structure, geometry feature, semantic c...

Problem statement: Text documents are the unstructured databases that contain raw data collection. The clustering techniques are used group up the text documents with reference to its similarity. Approach: The feature selection techniques were used to improve the efficiency and accuracy of clustering process. The feature selection was done by eliminate the redundant and irrelevant items from th...

In content-based image retrieval (CBIR), the effectiveness of the low-level features depends on their capabilities in describing the high-level semantic concepts. How to properly evaluate such an effectiveness remains a challenge. In this paper, we address the evaluation problem by using the decisive feature patterns of the low-level features. Intuitively, a decisive feature pattern is a combin...

Semantic feature norms (e.g., STIMULUS: car → RESPONSE: ) are commonly used in cognitive psychology to look into salient aspects of given concepts. Semantic features are typically collected in experimental settings and then manually annotated by the researchers into feature types (e.g., perceptual features, taxonomic features, etc.) by means of content analyses-that is, by usin...