The present study presents a semi-automatic method for parsing and filtering of noun phrases from citation contexts. The purpose of the method is to extract contextual, agreed upon, and pertinent noun phrases, to be used in visualization studies for naming clusters (concept groups) or concept symbols. The method is applied in a case study, which forms part of a larger dissertation work concerning the applicability of bibliometric methods for thesaurus construction. The case study is carried out within periodontology, a specialty area of dentistry. The result of the case study indicates that the method is able to identify highly important noun phrases, and that these phrases accurately describe their parent clusters. Hence, the method is abl...
Abstract Document clustering has many important applications in the area of data mining and informat...
We consider a challenging clustering task: the clustering of muti-word terms without document co-occ...
In this paper, we present a novel word clustering technique to capture contextual similarity among t...
The hybrid clustering approach combining lexical and link-based similarities suffered for a long tim...
Discovering synonyms and other related words among the words in a document collection can be seen as...
This paper describes a semantic clustering method for data extracted from machine readable dictionar...
The hybrid clustering approach combining lexical and link-based similarities suffered for a long tim...
Clustering of hybrid document networks combining citation based links with lexical similarities suff...
We propose a comprehensive methodology for thesaurus construction and maintenance combining shallow ...
International audienceWe consider a challenging clustering task: the clustering of multi-word terms ...
Coreference resolution is the task of determining whether two noun phrases refer to the same entity ...
Mapping a research domain can be of great significance for understanding and structuring the state-o...
In this paper, we present four clustering algorithms for noun phrase coreference resolution. We deve...
This paper presents an exploratory data analysis in lexical acquisition for adjec-tive classes using...
Artificial Intelligence Lab, Department of MIS, Univeristy of ArizonaIn an effort to assist medical ...
Abstract Document clustering has many important applications in the area of data mining and informat...
We consider a challenging clustering task: the clustering of muti-word terms without document co-occ...
In this paper, we present a novel word clustering technique to capture contextual similarity among t...
The hybrid clustering approach combining lexical and link-based similarities suffered for a long tim...
Discovering synonyms and other related words among the words in a document collection can be seen as...
This paper describes a semantic clustering method for data extracted from machine readable dictionar...
The hybrid clustering approach combining lexical and link-based similarities suffered for a long tim...
Clustering of hybrid document networks combining citation based links with lexical similarities suff...
We propose a comprehensive methodology for thesaurus construction and maintenance combining shallow ...
International audienceWe consider a challenging clustering task: the clustering of multi-word terms ...
Coreference resolution is the task of determining whether two noun phrases refer to the same entity ...
Mapping a research domain can be of great significance for understanding and structuring the state-o...
In this paper, we present four clustering algorithms for noun phrase coreference resolution. We deve...
This paper presents an exploratory data analysis in lexical acquisition for adjec-tive classes using...
Artificial Intelligence Lab, Department of MIS, Univeristy of ArizonaIn an effort to assist medical ...
Abstract Document clustering has many important applications in the area of data mining and informat...
We consider a challenging clustering task: the clustering of muti-word terms without document co-occ...
In this paper, we present a novel word clustering technique to capture contextual similarity among t...