A semantic classification model for e-catalogs

2004 ◽  
Author(s):  
Dongkyuk Kim ◽  
Sang-goo Lee ◽  
Jonghoon Chun ◽  
Juhnyoung Lee
Keyword(s):  
Classification Model ◽  
Semantic Classification

scholarly journals Assessing theory with practice: an evaluation of two aspectual-semantic classification models of gerundive nominalizations

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Lauren Fonteyn
Keyword(s):  
Historical Data ◽  
Corpus Analysis ◽  
Classification Model ◽  
Clear Distinction ◽  
Classification Models ◽  
Semantic Classification ◽  
Potential Problems ◽  
The One ◽  
Late Modern ◽  
Semantic Aspect

AbstractThis study present a corpus-based comparison of two aspectual-sematic classification models proposed in theoretical literature (unidimensional vs. bidimensional) by applying them to a set of nominal and verbal gerunds from the Modern English period. It (i) summarises the differences between unidimensional and bidimensional classification models and (ii) the potential problems associated with them. Despite the difficulties of studying semantic aspect in Present-day as well as historical data, this study will argue that, (iii) at least for deverbal nominalization patterns, it is possible to take a bidimensional approach and maintain a clear distinction between, on the one hand, aspect features of the nominalized situation (stativity/dynamicity, durativity/punctuality, and telicity/atelicity), and temporal boundedness of that situation. The question of which semantic classification model to use, then, is not so much one of which one is practically feasible in a corpus analysis, but rather which one is best suited to describe the attested variation. In order to determine the best model (in terms of parsimony and descriptive accuracy), (iv) the models were compared by means of ‘akaike weights’. To describe the variation between nominal and verbal gerunds in Early and Late Modern English, the bidimensional model outperformed the unidimensional one, showing that (v) the aspectual-semantic distinctions between Modern English nominal and verbal gerunds are a matter of both aspect and temporal boundedness.

scholarly journals Context-Aware Text Matching Algorithm for Korean Peninsula Language Knowledge Base Based on Density Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Li Xiang ◽  
Li ZongXun
Keyword(s):  
Knowledge Base ◽  
Korean Peninsula ◽  
Clustering Algorithm ◽  
Classification Model ◽  
Semantic Features ◽  
Matching Algorithm ◽  
Semantic Classification ◽  
Language Knowledge ◽  
Density Clustering ◽  
Text Matching

The majority of the traditional methods deal with text matching at the word level which remains uncertain as the text semantic features are ignored. This also leads to the problems of low recall and high space utilization of text matching while the comprehensiveness of matching results is poor. The resultant method, thus, cannot process long text and short text simultaneously. The current study proposes a text matching algorithm for Korean Peninsula language knowledge base based on density clustering. Using the deep multiview semantic document representation model, the semantic vector of the text to be matched is captured for semantic dependency which is utilized to extract the text semantic features. As per the feature extraction outcomes, the text similarity is calculated by subtree matching method, and a semantic classification model based on SWEM and pseudo-twin network is designed for semantic text classification. Finally, the text matching of Korean Peninsula language knowledge base is carried out by applying density clustering algorithm. Experimental results show that the proposed method has high matching recall rate with low space requirements and can effectively match long and short texts concurrently.

The Sound of Enemies and Friends in the Neighborhood

2011 ◽  
Vol 58 (6) ◽  
pp. 454-463 ◽  
Author(s):  
Diane Pecher ◽  
Inge Boot ◽  
Saskia van Dantzig ◽  
Carol J. Madden ◽  
David E. Huber ◽  
...  
Keyword(s):  
Word Recognition ◽  
Visual Word Recognition ◽  
Neighborhood Effects ◽  
Classification Performance ◽  
Visual Word ◽  
Orthographic Neighborhood ◽  
Semantic Classification ◽  
Semantic Class ◽  
Orthographic Neighbors ◽  
Target Words

Previous studies (e.g., Pecher, Zeelenberg, & Wagenmakers, 2005) found that semantic classification performance is better for target words with orthographic neighbors that are mostly from the same semantic class (e.g., living) compared to target words with orthographic neighbors that are mostly from the opposite semantic class (e.g., nonliving). In the present study we investigated the contribution of phonology to orthographic neighborhood effects by comparing effects of phonologically congruent orthographic neighbors (book-hook) to phonologically incongruent orthographic neighbors (sand-wand). The prior presentation of a semantically congruent word produced larger effects on subsequent animacy decisions when the previously presented word was a phonologically congruent neighbor than when it was a phonologically incongruent neighbor. In a second experiment, performance differences between target words with versus without semantically congruent orthographic neighbors were larger if the orthographic neighbors were also phonologically congruent. These results support models of visual word recognition that assume an important role for phonology in cascaded access to meaning.

Sign in / Sign up

Export Citation Format

Share Document