A Hybrid Web-Based Measure for Computing Semantic Relatedness Between Words

Spanakis, Gerasimos; Siolas, Georgios; Stafylopatis, Andreas

doi:10.1109/ictai.2009.64

Cited by 13 publications

(22 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The basic approach behind AAHKPS2 (Agirre et al 2009) is the use of regression to combine similarity scores that were computed using different resources and corpora. A different approach was followed by the SSS system (Spanakis, Siolas and Stafylopatis 2009) according to which the WordNet was exploited to create thousands of word pairs denoting relations such as synonymy, meronymy, etc. A different approach was followed by the SSS system (Spanakis, Siolas and Stafylopatis 2009) according to which the WordNet was exploited to create thousands of word pairs denoting relations such as synonymy, meronymy, etc.…”

Section: Comparison With Other Approachesmentioning

confidence: 99%

Similarity computation using semantic networks created from web-harvested data

Iosif¹,

Potamianos²

2013

Nat. Lang. Eng.

View full text Add to dashboard Cite

We investigate language-agnostic algorithms for the construction of unsupervised distributional semantic models using web-harvested corpora. Specifically, a corpus is created from web document snippets, and the relevant semantic similarity statistics are encoded in a semantic network. We propose the notion of semantic neighborhoods that are defined using co-occurrence or context similarity features. Three neighborhood-based similarity metrics are proposed, motivated by the hypotheses of attributional and maximum sense similarity. The proposed metrics are evaluated against human similarity ratings achieving state-of-the-art results.

show abstract

Section: Comparison With Other Approachesmentioning

confidence: 99%

Similarity computation using semantic networks created from web-harvested data

Iosif¹,

Potamianos²

2013

Nat. Lang. Eng.

View full text Add to dashboard Cite

show abstract

“…Dice and Jaccard use co-occurrence and occurrence of each word to define similarity distance (Spanakis et al 2009, Iosif and Potamianos 2010,Bollegala et al 2011, Mei et al 2015.…”

Section: Corpus-basedmentioning

confidence: 99%

Automatic generation of ontologies: a hierarchical word clustering approach

Sellah¹,

Hilaire²

2018

IJCSIS

View full text Add to dashboard Cite

In the context of globalization, companies need to capitalize on their knowledge. The knowledge of a company is present in two forms tacit and explicit. Explicit knowledge represents all formalized information i.e all documents (pdf, words ...). Tacit knowledge is present in documents and mind of employees, this kind of knowledge is not formalized, it needs a reasoning process to discover it. The approach proposed focus on extracting tacit knowledge from textual documents. In this paper, we propose hierarchical word clustering as an improvement of word clusters generated in previous work, we also proposed an approach to extract relevant bigrams and trigrams. We use Reuters-21578 corpus to validate our approach. Our global work aims to ease the automatic building of ontologies.

show abstract

“…Some authors have benefited from snippets to measure SR. For instance, Spanakis et al . (2009) have proposed a hybrid Web-based measure for computing SR between words by automatically extracting lexico-syntactic patterns from snippets based on the idea that similar words should have similar usage patterns. Similarly, Bollegala et al .…”

Section: Dimensions Of the Frameworkmentioning

confidence: 99%

The state of the art in semantic relatedness: a framework for comparison

Feng

Bagheri

Ensan³

et al. 2017

The Knowledge Engineering Review

View full text Add to dashboard Cite

Semantic relatedness (SR) is a form of measurement that quantitatively identifies the relationship between two words or concepts based on the similarity or closeness of their meaning. In the recent years, there have been noteworthy efforts to compute SR between pairs of words or concepts by exploiting various knowledge resources such as linguistically structured (e.g. WordNet) and collaboratively developed knowledge bases (e.g. Wikipedia), among others. The existing approaches rely on different methods for utilizing these knowledge resources, for instance, methods that depend on the path between two words, or a vector representation of the word descriptions. The purpose of this paper is to review and present the state of the art in SR research through a hierarchical framework. The dimensions of the proposed framework cover three main aspects of SR approaches including the resources they rely on, the computational methods applied on the resources for developing a relatedness metric, and the evaluation models that are used for measuring their effectiveness. We have selected 14 representative SR approaches to be analyzed using our framework. We compare and critically review each of them through the dimensions of our framework, thus, identifying strengths and weaknesses of each approach. In addition, we provide guidelines for researchers and practitioners on how to select the most relevant SR method for their purpose. Finally, based on the comparative analysis of the reviewed relatedness measures, we identify existing challenges and potentially valuable future research directions in this domain.

show abstract

A Hybrid Web-Based Measure for Computing Semantic Relatedness Between Words

Cited by 13 publications

References 23 publications

Similarity computation using semantic networks created from web-harvested data

Similarity computation using semantic networks created from web-harvested data

Automatic generation of ontologies: a hierarchical word clustering approach

The state of the art in semantic relatedness: a framework for comparison

Contact Info

Product

Resources

About