Noun Sense Disambiguation with WordNet for Software Design Retrieval

Gomes, Paulo; Pereira, Francisco C.; Paiva, Paulo; Seco, Nuno; Carreiro, Paulo; Ferreira, José; Bento, Carlos

doi:10.1007/3-540-44886-1_47

Cited by 9 publications

(7 citation statements)

References 2 publications

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“…The main difference with our work is our method uses only named entities within the domain of a specific populated ontology. Finally, our approach is different to that of disambiguating word senses [2,12,15]. Instead, our focus is to disambiguate named entities such as peoples' names, which has recently gained attention for its applicability in Social Networks [3,1].…”

Section: Related Workmentioning

confidence: 99%

Ontology-Driven Automatic Entity Disambiguation in Unstructured Text

Hassell

Aleman-Meza

Arpinar

2006

Lecture Notes in Computer Science

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Abstract. Precisely identifying entities in web documents is essential for document indexing, web search and data integration. Entity disambiguation is the challenge of determining the correct entity out of various candidate entities. Our novel method utilizes background knowledge in the form of a populated ontology. Additionally, it does not rely on the existence of any structure in a document or the appearance of data items that can provide strong evidence, such as email addresses, for disambiguating person names. Originality of our method is demonstrated in the way it uses different relationships in a document as well as from the ontology to provide clues in determining the correct entity. We demonstrate the applicability of our method by disambiguating names of researchers appearing in a collection of DBWorld posts using a large scale, realworld ontology extracted from the DBLP bibliography website. The precision and recall measurements provide encouraging results.

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Section: Related Workmentioning

confidence: 99%

Ontology-Driven Automatic Entity Disambiguation in Unstructured Text

Hassell

Aleman-Meza

Arpinar

2006

Lecture Notes in Computer Science

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“…The first similarity metric compares two UML class diagrams based on the diagram synset (the package's synset, which is a semantic evaluation) and diagram structure (combines structural evaluation of the diagram with object synset distances, is both structural and semantical), for a detailed description of this metric, see [16].…”

Section: Candidate Selectionmentioning

confidence: 99%

“…In this paper, we present an approach to software design using analogy, which is integrated in a Computer Aided Software Engineering (CASE) tool named REBUILDER [16]. This tool is based on Case-Based Reasoning (CBR) [17] and comprises a Knowledge Base with several types of knowledge, including WordNet [18], a lexical resource used in REBUILDER as an ontology.…”

Section: Introductionmentioning

confidence: 99%

The importance of retrieval in creative design analogies

Gomes

Seco

Pereira

et al. 2006

Knowledge-Based Systems

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Analogy is an important reasoning process in creative design. It enables the generation of new design artifacts using ideas from semantically distant domains. Candidate selection is a crucial process in the generation of creative analogies. Without a good set of candidate sources, the success of subsequent phases can be compromised. Two main types of selection have been identified: semantics-based retrieval and structure-based retrieval. This paper presents an empirical study on the importance of the analogy retrieval strategy in the domain of software design. We argue that both types of selection are important, but they play different roles in the process.

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“…Gomes et al [12] present a word sense disambiguation method based on the semantic distance among the context words in WordNet. Given a set of context words, {W 1 , W 2 , …W n }, from WordNet, we can get a set of senses for each word.…”

Section: Word Sense Disambiguationmentioning

confidence: 99%

“…Gomes et al's context words-based sense disambiguation method is used to find the sense for each word [12]. A context window is set to control the size of context.…”

Section: Using Sense Of Wordsmentioning

confidence: 99%

Document Clustering with Semantic Analysis

Wang

Hodges

2006

Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06)

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Document clustering generates clusters from the whole document collection automatically and is used in many fields, including data mining and information retrieval. In the traditional vector space model, the unique words occurring in the document set are used as the features. But because of the synonym problem and the polysemous problem, such a bag of original words cannot represent the content of a document precisely. In this paper, we investigate using the sense disambiguation method to identify the sense of words to construct the feature vector for document representation. Our experimental results demonstrate that in most conditions, using sense can improve the performance of our document clustering system. But the comprehensive statistical analysis performed indicates that the differences between using original single words and using senses of words are not statistically significant. In this paper, we also provide an evaluation of several basic clustering algorithms for algorithm selection.

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Noun Sense Disambiguation with WordNet for Software Design Retrieval

Cited by 9 publications

References 2 publications

Ontology-Driven Automatic Entity Disambiguation in Unstructured Text

Ontology-Driven Automatic Entity Disambiguation in Unstructured Text

The importance of retrieval in creative design analogies

Document Clustering with Semantic Analysis

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