Abstract Almost all current feature modelling systems are parametric, history-based modelling systems. These systems suffer from a number of shortcomings with regard to the modelling process. In particular, they lack a complete specification of feature semantics, and therefore fail to maintain

information that cannot be elicited from (he existing dictionaries. TJie priority is given to semantic features influencing lexical or grammatical co-occurrence restrictions. Possibilities are discussed of predicting selection restrictions on the basis of semantic features of a word in the lexicon. 1. LEXICAL

This paper presents the strategy and design of a highly efficient semiautomatic method for labelling the semantic features of common nouns, using semantic relationships between words, and based on the information extracted from an electronic monolingual dictionary. The method, that uses genus data

Semantic retrieval Jewelry design a b s t r a c t Sketch-based modeling has attracted considerable attention in recent years. In this paper, we propose a semantic feature modeling system for sketch-based jewelry design, called blue Sketch2Jewelry. The newly devised semantic feature class encodes

We present a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame. Various lexical and syntactic features are derived from parse trees and used to derive statistical classifiers from hand-annotated training data. 1

Previous studies have examined semantic/pragmatic correlates of word order change in a number of languages (see Thrainsson 2001 for an overview). They attested that leftward movement of a direct object affects its interpretation: scrambled position is usually associated with a specific semantic

modeling of the objects, scenes and actions is critical for visual recognition. Recently, bag of visual words (BoVW) representation, in which the image patches or video cuboids are quantized into visual words (i.e., mid-level features) based on their appearance similarity using clustering, has been widely

A study explored word recognition and structural features of words as determinants of reading comprehension. Word recognition scores and comprehension scores for three age groups (6-7 years, 8-9 years, and 10-12 years) representing 1,200 children were used to examine the relationships of structural

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with “engineering rich ” dialogs aimed at the creation of feature instances. Most systems, however, fail to consistently maintain the meaning of the features throughout the modeling process. For example, a model-ing operation on one feature may affect the semantics of other features without the user being notified