Using WordNet for Word Sense Disambiguation to Support Concept Map Construction

Cañas, Alberto J.; Valerio, Alejandro A.; Pulido, Juan Guillermo Lalinde; Carvalho, Marco; Arguedas, Marco

doi:10.1007/978-3-540-39984-1_27

Cited by 37 publications

(26 citation statements)

References 4 publications

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“…The method A.J. Cañas [7] proposed is similar with ours. He also wants to map the keywords to WordNet synset and clustering the synset and present the content of a document with a map structure, but the difference between his method and ours is the document type aiming at and the distance measure in the clustering algorithm.…”

Section: Related Workmentioning

confidence: 65%

Word Sense Disambiguation of semantic document

Shi

Fang

Yan

et al. 2010

2010 2nd International Conference on Future Computer and Communication

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A Max-Probability Density based Clustering (MPDC) algorithm is proposed in this paper to resolve the problem of Word Sense Disambiguation in semantic document. MPDC take the context information of a keyword based on WordNet into account and select the max probability sense by measuring the density of the concept. We also do experiment on semantic documents retrieving from Swoogle and Watson, two famous semantic web searching engines. The result shows MPDC get a good efficiency.

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Section: Related Workmentioning

confidence: 65%

Word Sense Disambiguation of semantic document

Shi

Fang

Yan

et al. 2010

2010 2nd International Conference on Future Computer and Communication

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“…In [8] it is presented an algorithm that uses the WordNet to perfonn the word sense disambiguation using a map that provides the context. The algorithm is based on 6 steps: key concept selection, words related with synset, hypemym sequence creation, clustering, best cluster selection and finally, word sense disambiguation.…”

Section: Concept Mapsmentioning

confidence: 99%

Concept map construction applying natural language processing on text extracted from e-commerce web pages

Caputo

Ebecken

2011

2011 Third World Congress on Nature and Biologically Inspired Computing

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Ahstract-To understand the theory behind a given subject can be an expensive task depending on the domain complexity.In the products case, the quantity of functionalities and variants can be a big impediment for the user take a decision, since he/she has to understand each detail before choose from an offers variety. Moreover, there is a huge amount of information available in the internet and tutorials to explain each concept that is involved in each subject. The concepts are related to each other to create the main idea of the subject. This paper presents a methodology to extract these concepts in a way that it is easier to an end user to rapidly understand the subject using concept maps. It also aims to extract the relationships between the concepts showing how they are related. Many techniques were combined to provide this knowledge mechanism, like natural language processing and information extraction. In the web documents case the information is presented with particularities, like tables, than it should be maintained to better access the associations.

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“…A concept map is a list of concepts connected each other with linking phrases forming meaningful proposition between concepts. The algorithm is fully described in Canas et al (2003). Let w be the word for which we want to find a sense and c a given concept map.…”

Section: Disambiguation Strategiesmentioning

confidence: 99%

A new unsupervised method for document clustering by using WordNet lexical and conceptual relations

Recupero

2007

Inf Retrieval

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Text document clustering provides an effective and intuitive navigation mechanism to organize a large amount of retrieval results by grouping documents in a small number of meaningful classes. Many well-known methods of text clustering make use of a long list of words as vector space which is often unsatisfactory for a couple of reasons: first, it keeps the dimensionality of the data very high, and second, it ignores important relationships between terms like synonyms or antonyms. Our unsupervised method solves both problems by using ANNIE and WordNet lexical categories and WordNet ontology in order to create a well structured document vector space whose low dimensionality allows common clustering algorithms to perform well. For the clustering step we have chosen the bisecting k-means and the Multipole tree, a modified version of the Antipole tree data structure for, respectively, their accuracy and speed.

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Using WordNet for Word Sense Disambiguation to Support Concept Map Construction

Cited by 37 publications

References 4 publications

Word Sense Disambiguation of semantic document

Word Sense Disambiguation of semantic document

Concept map construction applying natural language processing on text extracted from e-commerce web pages

A new unsupervised method for document clustering by using WordNet lexical and conceptual relations

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