Word Similarity Computation Based on WordNet and HowNet

2013 ◽  
Vol 336-338 ◽  
pp. 2115-2118
Author(s):  
Pei Ying Zhang
Keyword(s):  
Text Categorization ◽  
Computational Algorithm ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Word Similarity ◽  
Main Obstacle ◽  
Sense Disambiguation ◽  
Similarity Judgments ◽  
Similarity Computation ◽  
State Of Art

Word similarity computation is broadly used in many applications, such as information retrieval, information extraction, text categorization, word sense disambiguation and example-based machine translation and so on. The main obstacle of word similarity computation lie in that how to develop a computational algorithm that is capable of generating satisfactory results close to how human perceive. This paper proposed an approach of word similarity computation which combined WordNet and HowNet. Experiments on Chinese word pairs show that our method is closest to human similarity judgments when compared to the major state-of-art methods.

Data Mining and the Text Categorization Framework

2011 ◽  
pp. 394-399
Author(s):  
Paola Cerchiello
Keyword(s):  
Scientific Community ◽  
Text Categorization ◽  
Word Sense Disambiguation ◽  
Classification Problem ◽  
Word Sense ◽  
Relative Class ◽  
Author Identification ◽  
Sense Disambiguation ◽  
Text Filtering ◽  
Great Relevance

The aim of this contribution is to show one of the most important application of text mining. According to a wide part of the literature regarding the aforementioned field, great relevance is given to the classification task (Drucker et al., 1999, Nigam et al., 2000). The application contexts are several and multitask, from text filtering (Belkin & Croft, 1992) to word sense disambiguation (Gale et al., 1993) and author identification ( Elliot and Valenza, 1991), trough anti spam and recently also anti terrorism. As a consequence in the last decade the scientific community that is working on this task, has profuse a big effort in order to solve the different problems in the more efficient way. The pioneering studies on text categorization (TC, a.k.a. topic spotting) date back to 1961 (Maron) and are deeply rooted in the Information Retrieval context, so declaring the engineering origin of the field under discussion. Text categorization task can be briefly defined as the problem of assigning every single textual document into the relative class or category on the basis of the content and employing a classifier properly trained. In the following parts of this contribution we will formalize the classification problem detailing the main issues related.

A Method for Automatic Text Categorization Using Word Sense Disambiguation

2008 ◽  
pp. 1158-1169
Author(s):  
Azucena Montes Rendon ◽  
Rocio Vargas A. ◽  
Hugo Estrada Esquivel ◽  
Juan G. Gonzalez Serna ◽  
Jose Ruiz Ascencio
Keyword(s):  
Text Categorization ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Sense Disambiguation ◽  
Automatic Text

scholarly journals Word Sense Disambiguation Models Emerging Trends: A Comparative Analysis

2022 ◽  
Vol 2161 (1) ◽  
pp. 012035
Author(s):  
Nemika Tyagi ◽  
Sudeshna Chakraborty ◽  
Jyotsna ◽  
Aditya Kumar ◽  
Nzanzu Katasohire Romeo
Keyword(s):  
Comparative Analysis ◽  
Language Processing ◽  
Semantic Analysis ◽  
Unsolved Problem ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Natural Languages ◽  
Emerging Trends ◽  
Sense Disambiguation ◽  
State Of Art

Abstract Word Sense Disambiguation (WSD) arises due to the presence of ambiguity in the text during the semantic analysis of natural languages. It is a major unsolved problem in the area of Natural Language Processing (NLP) and its applications. This paper explores and reviews WSD algorithms that have contributed to, or created state-of-art solutions in recent years. Moreover, this paper also aims to analyze the recent technological trends in the domain of WSD which can give us leverage to identify the possible future trajectory of the search for better WSD solutions.

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