Design, Implementation and Evaluation of a New Semantic Similarity Metric Combining Features and Intrinsic Information Content

Pirrò, Giuseppe; Seco, Nuno

doi:10.1007/978-3-540-88873-4_25

Cited by 67 publications

(43 citation statements)

References 13 publications

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“…To correct this limitation, several authors refined the measure proposed by Resnik to incorporate specificities of compared concepts. We here present the measures proposed by Lin (1998a) 43 -sim Lin , (Jiang and Conrath, 1997) -dist JC , (Mazandu and Mulder, 2013) -sim N univers , (Pirró and Seco, 2008;Pirró, 2009) -sim P Sec and (Pirró and Euzenat, 2010b) -sim F aith :…”

Section: Information Theoretical Approachmentioning

confidence: 99%

Semantic Similarity from Natural Language and Ontology Analysis

Harispe

Ranwez

Janaqi

et al. 2015

Synthesis Lectures on Human Language Technologies

166

102

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Artificial Intelligence federates numerous scientific fields in the aim of developing machines able to assist human operators performing complex treatments -most of which demand high cognitive skills (e.g. learning or decision processes). Central to this quest is to give machines the ability to estimate the likeness or similarity between things in the way human beings estimate the similarity between stimuli.In this context, this book focuses on semantic measures: approaches designed for comparing semantic entities such as units of language, e.g. words, sentences, or concepts and instances defined into knowledge bases. The aim of these measures is to assess the similarity or relatedness of such semantic entities by taking into account their semantics, i.e. their meaning -intuitively, the words tea and coffee, which both refer to stimulating beverage, will be estimated to be more semantically similar than the words toffee (confection) and coffee, despite that the last pair has a higher syntactic similarity. The two state-of-theart approaches for estimating and quantifying semantic similarities/relatedness of semantic entities are presented in detail: the first one relies on corpora analysis and is based on Natural Language Processing techniques and semantic models while the second is based on more or less formal, computer-readable and workable forms of knowledge such as semantic networks, thesaurus or ontologies.Semantic measures are widely used today to compare units of language, concepts, instances, or even resources indexed by them (e.g., documents, genes). They are central elements of a large variety of Natural Language Processing applications and knowledge-based treatments, and have therefore naturally been subject to intensive and interdisciplinary research efforts during last decades. Beyond a simple inventory and categorization of existing measures, the aim of this monograph is to convey novices as well as researchers of these domains towards a better understanding of semantic similarity estimation and more generally semantic measures. To this end, we propose an in-depth characterisation of existing proposals by discussing their features, the assumptions on which they are based and empirical results regarding their performance in particular applications. By answering these questions and by providing a detailed discussion on the foundations of semantic measures, our aim is to give the reader key knowledge required to: (i) select the more relevant methods according to a particular usage context, (ii) understand the challenges offered to this field of study, (iii) distinguish room of improvements for state-of-the-art approaches and (iv) stimulate creativity towards the development of new approaches. In this aim, several definitions, theoretical and practical details, as well as concrete applications are presented.keywords: Semantic similarity, semantic relatedness, semantic measures, distributional measures, domain ontology, knowledge-based semantic measure.iii iv

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Section: Information Theoretical Approachmentioning

confidence: 99%

Semantic Similarity from Natural Language and Ontology Analysis

Harispe

Ranwez

Janaqi

et al. 2015

Synthesis Lectures on Human Language Technologies

166

102

View full text Add to dashboard Cite

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“…Given a taxonomy of concepts, a straightforward method to calculate similarity between two words (or concepts) is to find the length of the shortest path connecting the two words in the taxonomy [22]- [24]. A problem that is frequently acknowledged with this approach is that it relies on the notion that all links in the taxonomy represent a uniform distance [25].…”

Section: Related Workmentioning

confidence: 99%

Measuring the Degree of Synonymy between Words Using Relational Similarity between Word Pairs as a Proxy

Bollegala

Matsuo

Ishizuka

2012

IEICE Trans. Inf. & Syst.

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SUMMARYTwo types of similarities between words have been studied in the natural language processing community: synonymy and relational similarity. A high degree of similarity exist between synonymous words. On the other hand, a high degree of relational similarity exists between analogous word pairs. We present and empirically test a hypothesis that links these two types of similarities. Specifically, we propose a method to measure the degree of synonymy between two words using relational similarity between word pairs as a proxy. Given two words, first, we represent the semantic relations that hold between those words using lexical patterns. We use a sequential pattern clustering algorithm to identify different lexical patterns that represent the same semantic relation. Second, we compute the degree of synonymy between two words using an inter-cluster covariance matrix. We compare the proposed method for measuring the degree of synonymy against previously proposed methods on the Miller-Charles dataset and the WordSimilarity-353 dataset. Our proposed method outperforms all existing Web-based similarity measures, achieving a statistically significant Pearson correlation coefficient of 0.867 on the Miller-Charles dataset.

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“…Tags' relatedness can be constructed according to several metrics [18], [11]. In our case, we employ the following modified notion of co-occurrence of tags.…”

Section: A Automated Propagation Of Web-traveler Policiesmentioning

confidence: 99%

Privacy through Web-Traveler Policies in Social Network Environments

Sundareswaran¹,

Squicciarini²

2011

Proceedings of the 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing

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Abstract-Social networking sites are ingrained in the fabric of our day-to-day lives, with these sites being widely used to exchange personal information and content. Some forms of access control are provided by the sites themselves to protect the useruploaded content. However, these types of control are limited in that they require the user's input for the effective protection, otherwise a default policy which provides minimal protection is often applied. Towards providing extended content protection, in this paper we propose an approach for automated user-uploaded content control. Automatic enforcement allows us to extend the protection of content for unprotected files, preventing underage viewers from accessing adult content, tracking stolen or misused copyright-free images. This work builds upon the notion of ' Web-Traveler policies', previously introduced as a new class of content control policies. In the paper, we also provide a proof of concept implementation of our algorithms for automatic propagation for images in order to prove the applicability and strength of our approach.

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Design, Implementation and Evaluation of a New Semantic Similarity Metric Combining Features and Intrinsic Information Content

Cited by 67 publications

References 13 publications

Semantic Similarity from Natural Language and Ontology Analysis

Semantic Similarity from Natural Language and Ontology Analysis

Measuring the Degree of Synonymy between Words Using Relational Similarity between Word Pairs as a Proxy

Privacy through Web-Traveler Policies in Social Network Environments

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