Weighting-based semantic similarity measure based on topological parameters in semantic taxonomy

Saif, Abdulgabbar; Zainodin, Ummi Zakiah; Omar, Nazlia; Ghareb, Abdullah Saeed

doi:10.1017/s1351324918000190

Cited by 4 publications

(4 citation statements)

References 38 publications

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“…Finally, in comparison with the weight-based method proposed by Saif et al [38], our model combined with Li or Liu-1 is equal to or exceeds it on MC30, surpass it on the RG65 and SimLex666 datasets, but defeat it on AG203. Overall, our model is slightly superior to Saif's method in terms of measurement accuracy.…”

Section: Discussion a Discussion On Wordnet Edge Weight Modelmentioning

confidence: 78%

“…Saif et al [38] considered the semantic representation of a concept as a set of concepts that are extracted from its hypernym-concepts in a semantic taxonomy, and they then proposed four weight mechanisms to weigh the degree of relevance of features by using topological parameters (edge, depth, descendants, and density) in a semantic taxonomy. The weight mechanism with descendants has achieved the best results in their experiments, so we just show this one.…”

Section: ) Weighting-based Approachesmentioning

confidence: 99%

“…To reduce the timeconsumption, we cannot directly measure the semantic similarity between concepts via Eq. (38); hence, it is necessary to reduce the number of terms of interest for a concept via a word disambiguation strategy. Both of our proposed word disambiguation strategies contain a parameter for which the value must be determined.…”

Section: F Parameter Value For Our Wikipedia Disambiguation Strategymentioning

confidence: 99%

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An Efficient Approach for Measuring Semantic Similarity Combining WordNet and Wikipedia

Liao

Zhang

et al. 2020

IEEE Access

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The measurement of semantic similarity between concepts is an important research topic in natural language processing. In the past, several approaches for measuring the semantic similarity between concepts have been proposed based on WordNet or Wikipedia. However, improvements in the measurement accuracy of most methods have led to a dramatic increase in time complexity, and the existing methods do not effectively integrate WordNet and Wikipedia. In this paper, we focus on designing an efficient semantic similarity method based on WordNet and Wikipedia. To improve the accuracy of WordNet edgebased measures, we propose an edge weight model for combining edge and density information, which assigns a weight to each edge adaptively based on the number of direct hyponyms of the subsumer. Second, to improve the computational efficiencies of the existing Wikipedia link vector-based measures, we propose a new Wikipedia link feature-based semantic similarity method that converts Wikipedia links into semantic knowledge and replaces the TF-IDF statistical weight model in the existing measures. In addition, we propose two new word disambiguation strategies to further improve the accuracy of Wikipedia linkbased measures. Finally, to fully exploit the advantages of WordNet and Wikipedia, we propose two new aggregation schemas for combining WordNet "is-a" semantics and Wikipedia link semantics to replace the current aggregation schemas that combine WordNet "is-a" semantics with category semantics in Wikipedia. The experimental results show that our aggregation models are outstanding in terms of accuracy, efficiency and word coverage compared to state-of-the-art similarity measures.

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Section: Discussion a Discussion On Wordnet Edge Weight Modelmentioning

confidence: 78%

Section: ) Weighting-based Approachesmentioning

confidence: 99%

Section: F Parameter Value For Our Wikipedia Disambiguation Strategymentioning

confidence: 99%

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An Efficient Approach for Measuring Semantic Similarity Combining WordNet and Wikipedia

Liao

Zhang

et al. 2020

IEEE Access

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“…Weighted Kernel Density Estimation (WKDE) [43,44] based on point weights has proved effective. For the semantic similarity measuring task, constant weighting assumption-based semantic similarity [45] measure between two concepts/words holds better performance for the semantic representation of the concept/words but holds the same weighting relevance. Later, it found that the weight propagation mechanism [46,47] for augmenting input with semantic information achieves desired performance and removes the same weighting curse for concepts/words.…”

Section: Literature Reviewmentioning

confidence: 99%

Corpus Statistics Empowered Document Classification

et al. 2022

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In natural language processing (NLP), document classification is an important task that relies on the proper thematic representation of the documents. Gaussian mixture-based clustering is widespread for capturing rich thematic semantics but ignores emphasizing potential terms in the corpus. Moreover, the soft clustering approach causes long-tail noise by putting every word into every cluster, which affects the natural thematic representation of documents and their proper classification. It is more challenging to capture semantic insights when dealing with short-length documents where word co-occurrence information is limited. In this context, for long texts, we proposed Weighted Sparse Document Vector (WSDV), which performs clustering on the weighted data that emphasizes vital terms and moderates the soft clustering by removing outliers from the converged clusters. Besides the removal of outliers, WSDV utilizes corpus statistics in different steps for the vectorial representation of the document. For short texts, we proposed Weighted Compact Document Vector (WCDV), which captures better semantic insights in building document vectors by emphasizing potential terms and capturing uncertainty information while measuring the affinity between distributions of words. Using available corpus statistics, WCDV sufficiently handles the data sparsity of short texts without depending on external knowledge sources. To evaluate the proposed models, we performed a multiclass document classification using standard performance measures (precision, recall, f1-score, and accuracy) on three long- and two short-text benchmark datasets that outperform some state-of-the-art models. The experimental results demonstrate that in the long-text classification, WSDV reached 97.83% accuracy on the AgNews dataset, 86.05% accuracy on the 20Newsgroup dataset, and 98.67% accuracy on the R8 dataset. In the short-text classification, WCDV reached 72.7% accuracy on the SearchSnippets dataset and 89.4% accuracy on the Twitter dataset.

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A Semantic Taxonomy for Weighting Assumptions to Reduce Feature Selection from Social Media and Forum Posts

Hasan

Rassem

Noor

et al. 2019

Advances in Intelligent Systems and Computing

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Weighting-based semantic similarity measure based on topological parameters in semantic taxonomy

Cited by 4 publications

References 38 publications

An Efficient Approach for Measuring Semantic Similarity Combining WordNet and Wikipedia

An Efficient Approach for Measuring Semantic Similarity Combining WordNet and Wikipedia

Corpus Statistics Empowered Document Classification

A Semantic Taxonomy for Weighting Assumptions to Reduce Feature Selection from Social Media and Forum Posts

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