Creating a Similarity Graph from WordNet

Stanchev, Lubomir

doi:10.1145/2611040.2611055

Cited by 15 publications

(38 citation statements)

References 12 publications

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“…In this section, we review how the similarity graph can be created using information from WordNet [25]. The algorithm that creates the graph is previously published in [38]. The novelty is that we use part of the Reuters-21578 benchmark to ¯ne-tune the algorithm and ¯nd the best value for the di®erent parameters.…”

Section: Creating and Fine-tuning The Similarity Graphmentioning

confidence: 99%

“…We will therefore be left with two words: ``position" and ``professor". As a result, we will create the following edges according to the algorithm from [38].…”

Section: Processing the De¯nitions Of The Sensesmentioning

confidence: 99%

“…In [38], we assumed that the ¯rst words in the de¯nition of a sense are far more important than the later words. We therefore multiplied the edge weight by coef ¼ 1:0 for the ¯rst non-noise term and kept decreasing this coe±cient by 0:2 for each sequential term until the value of the coe±cient reached 0:2.…”

Section: Processing the De¯nitions Of The Sensesmentioning

confidence: 99%

“…Table 1 shows the value for the f-score (/ ¼ 1) with and without using the coef multiplier for di®erent values of a 1 . In this and sequential tables, we will use bold font for the highest value and italic value for the value for the initial algorithm that is described in [38]. Note that the results are for our training set, which includes only the ¯rst 2,000 documents in the Reuters collection.…”

Section: Processing the De¯nitions Of The Sensesmentioning

confidence: 99%

“…Note that the results are for our training set, which includes only the ¯rst 2,000 documents in the Reuters collection. The ¯rst column in the table shows the results of running the algorithm from [38] and only changing the value for a 1 . The second column shows the result of running the same algorithm for di®erent values of a 1 , but this time we did not multiply by the coef multiplier.…”

Section: Processing the De¯nitions Of The Sensesmentioning

confidence: 99%

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Fine-Tuning an Algorithm for Semantic Document Clustering Using a Similarity Graph

Stanchev

2016

Int. J. Semantic Computing

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In this article, we examine an algorithm for document clustering using a similarity graph. The graph stores words and common phrases from the English language as nodes and it can be used to compute the degree of semantic similarity between any two phrases. One application of the similarity graph is semantic document clustering, that is, grouping documents based on the meaning of the words in them. Since our algorithm for semantic document clustering relies on multiple parameters, we examine how fine-tuning these values affects the quality of the result. Specifically, we use the Reuters-21578 benchmark, which contains [Formula: see text] newswire stories that are grouped in 82 categories using human judgment. We apply the k-means clustering algorithm to group the documents using a similarity metric that is based on keywords matching and one that uses the similarity graph. We evaluate the results of the clustering algorithms using multiple metrics, such as precision, recall, f-score, entropy, and purity.

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Section: Creating and Fine-tuning The Similarity Graphmentioning

confidence: 99%

“…We will therefore be left with two words: ``position" and ``professor". As a result, we will create the following edges according to the algorithm from [38].…”

Section: Processing the De¯nitions Of The Sensesmentioning

confidence: 99%

Section: Processing the De¯nitions Of The Sensesmentioning

confidence: 99%

Section: Processing the De¯nitions Of The Sensesmentioning

confidence: 99%

Section: Processing the De¯nitions Of The Sensesmentioning

confidence: 99%

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Fine-Tuning an Algorithm for Semantic Document Clustering Using a Similarity Graph

Stanchev

2016

Int. J. Semantic Computing

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Multi-label Classification of Short Text Based on Similarity Graph and Restart Random Walk Model

Xiao-hong

Yang

et al. 2020

IFIP Advances in Information and Communication Technology

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A multi-label classification method of short text based on similarity graph and restart random walk model is proposed. Firstly, the similarity graph is created by using data and labels as the node, and the weights on the edges are calculated through an external knowledge, so the initial matching degree of between the sample and the label set is obtained. After that, we build a label dependency graph with labels as vertices, and using the previous matching degree as the initial prediction value to calculate the relationship between the sample and each node until the probability distribution becomes stable. Finally, the obtained relationship vector is the label probability distribution vector of the sample predicted by the method in this paper. Experimental results show that we provides a more efficient and reliable multi-label short-text classification algorithm.

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