Measuring Semantic Similarity of Word Pairs Using Path and Information Content

Meng, Lijun; Huang, Ruixuan; Gu, Junzhong

doi:10.14257/ijfgcn.2014.7.3.17

Cited by 14 publications

(8 citation statements)

References 16 publications

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“…In [15], the authors provide a survey of different methods of applying lexical databases to improve these similarity measures in many different ways. An overview of a plethora of such measures is also given in [18].…”

Section: Related Workmentioning

confidence: 99%

Semi-Automatic Definition of Attribute Semantics for the Purpose of Ontology Integration

2020

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By ontology, we understand a knowledge structure which well reflects the complexity of a real world. Ontologies are built to store and process knowledge about objects and dependencies between them. Thus, ontologies not only structure raw data, but also contain the meaning of those data. So far, ontology developers have been forced to provide the semantics of modeled objects and relations between them manually. The goal of this paper is to address some still unresolved problems related to providing meanings in ontologies. A typical ontology consists of concepts with attributes, relations between them, and instances. In our research, we focus on concepts level where attributes must be interpretable because they are the primary carriers of the meaning of the entire ontology. In other words, we need to assign semantics for each attribute within a concept. For this purpose, we have proposed a semi-automatic method for defining attribute semantics based on WordNet. The attribute semantics designated by human experts and based on WordNet does not affect the integration result. However, the developed method allows reducing the time for preparing an ontology to the integration process. What is more, it is less sensitive to the subjective evaluations done by experts.

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Section: Related Workmentioning

confidence: 99%

Semi-Automatic Definition of Attribute Semantics for the Purpose of Ontology Integration

2020

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“…Therefore, the performance of applications can be greatly improved with a proper metric for measurement. Metrics are usually divided into two classes: Path Based Metrics and Information Content (IC) Based Metrics [16]. Semantic similarity has been successfully applied in [17,18,19,20,21].…”

Section: B) Word and Semantic Similarity Measuresmentioning

confidence: 99%

“…Li et al, uses multiple information sources to calculate the semantic similarity of concepts and proposes a metric based on the assumption that information sources are infinite to some extent while humans compare word similarity with a finite interval between completely similar and nothing similar [27]. Intuitively, the transformation between an infinite interval to a finite one is non-linear [16,27]. Li et al define local semantic density as a monotonically increasing function of wsim (w1, w2):…”

Section: B) Word and Semantic Similarity Measuresmentioning

confidence: 99%

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FUSE (Fuzzy Similarity Measure) - A measure for determining fuzzy short text similarity using Interval Type-2 fuzzy sets

Adel

Crockett

Crispin

et al. 2018

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Measurement of the semantic and syntactic similarity of human utterances is essential in developing language that is understandable when machines engage in dialogue with users. However, human language is complex and the semantic meaning of an utterance is usually dependent on context at a given time and also based on learnt experience of the meaning of the perception based words that are used. Limited work in terms of the representation and coverage has been done on the development of fuzzy semantic similarity measures. This paper proposes a new measure known as FUSE (FUzzy Similarity mEasure) which determines similarity using expanded categories of perception based words that have been modelled using Interval Type-2 fuzzy sets. The paper describes the method of obtaining the human ratings of these words based on Mendel's methodology and applies them within the FUSE algorithm. FUSE is then evaluated on three established datasets and is compared with two known semantic similarity algorithms. Results indicate FUSE provides higher correlations to human ratings.

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“…There have been many other similarity measures proposed based on WordNet, including path, lch (Leacock and Chodorow, 1998), wup (Wu and Palmer, 1994), res (Resnik, 1995), lin (Lin, 1998), jcn (Jiang and Conrath, 1997). Meng et al (2014) recent study suggested a new metric that combines information density and the path metric and Li et al (2006) earlier study proposed a semantic similarity combining the shortest path between two words, w 1 and w 2 and the depth of their Least Common Subsumer (LCS) in the taxonomy containing both words. The new metric proposed by Meng et al (2014) showed more accurate results and outperformed Li et al (2006) in terms of the similarity coefficient because Meng et al (2014) metric not only reflects the semantic density information but also the path information.…”

Section: Word Semantic Similarity Techniquesmentioning

confidence: 99%

Cross-Language Semantic Similarity of Arabic-English Short Phrases and Sentences

Alzahrani¹

2016

Journal of Computer Science

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Measuring cross-language semantic similarity between short texts is a task that is challenging in terms of human understanding. This paper addresses this problem by carrying out a study of Arabic-English semantic similarity in short phrases and sentences. Human-rated benchmark dataset was carefully constructed for this research. Dictionary and machine translation techniques were employed to determine the relatedness between the cross-lingual texts from a monolingual perspective. Three algorithms were developed to rate the semantic similarity and these were applied to the human-rated benchmark. An averaged maximum-translation similarity algorithm was proposed using the term sets produced by the dictionarybased technique. Noun-verb and term vectors obtained by the Machine Translation (MT) technique were also suggested to compute the semantic similarity. The results were compared with the human ratings in our benchmark using Pearson correlation coefficient and these were triangulated with the best, worst and mean for all human participants. MTbased term vector semantic similarity algorithm obtained the highest correlation (r = 0.8657) followed by averaged maximum-translation similarity algorithm (r = 0.7206). Further statistical analysis showed no significant difference between both algorithms and the humans' judgement.

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Measuring Semantic Similarity of Word Pairs Using Path and Information Content

Abstract: Abstract

Cited by 14 publications

References 16 publications

Semi-Automatic Definition of Attribute Semantics for the Purpose of Ontology Integration

Semi-Automatic Definition of Attribute Semantics for the Purpose of Ontology Integration

FUSE (Fuzzy Similarity Measure) - A measure for determining fuzzy short text similarity using Interval Type-2 fuzzy sets

Cross-Language Semantic Similarity of Arabic-English Short Phrases and Sentences

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