scholarly journals An overview of textual semantic similarity measures based on web intelligence

2017 ◽  
Author(s):  
Jorge Martinez-Gil
Keyword(s):  
Semantic Similarity ◽  
Similarity Measures ◽  
Brand Names ◽  
Frequent Pattern ◽  
Web Intelligence ◽  
Short Text ◽  
Proper Nouns ◽  
Semantic Similarity Measurement ◽  
Traditional Approaches ◽  
Degree Of Similarity

Computing the semantic similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is a key challenge in many computer related fields. The problem is that traditional approaches to semantic similarity measurement are not suitable for all situations, for example, many of them often fail to deal with terms not covered by synonym dictionaries or are not able to cope with acronyms, abbreviations, buzzwords, brand names, proper nouns, and so on. In this paper, we present and evaluate a collection of emerging techniques developed to avoid this problem. These techniques use some kinds of web intelligence to determine the degree of similarity between text expressions. These techniques implement a variety of paradigms including the study of co-occurrence, text snippet comparison, frequent pattern finding, or search log analysis. The goal is to substitute the traditional techniques where necessary.

scholarly journals Comparable Evaluation of Contemporary Corpus-Based and Knowledge-Based Semantic Similarity Measures of Short Texts

2011 ◽  
Vol 1 (1) ◽  
Author(s):  
Bojan Furlan ◽  
Vladimir Sivački ◽  
Davor Jovanović ◽  
Boško Nikolić
Keyword(s):  
Semantic Similarity ◽  
Large Fraction ◽  
Similarity Measures ◽  
The Other ◽  
Similarity Measurement ◽  
Data Set ◽  
Word Similarity ◽  
Short Text ◽  
Knowledge Based ◽  
Semantic Similarity Measurement

This paper presents methods for measuring the semantic similarity of texts, where we evaluated different approaches based on existing similarity measures. On one side word similarity was calculated by processing large text corpuses and on the other, commonsense knowledgebase was used. Given that a large fraction of the information available today, on the Web and elsewhere, consists of short text snippets (e.g. abstracts of scientific documents, image captions or product descriptions), where commonsense knowledge has an important role, in this paper we focus on computing the similarity between two sentences or two short paragraphs by extending existing measures with information from the ConceptNet knowledgebase. On the other hand, an extensive research has been done in the field of corpus-based semantic similarity, so we also evaluated existing solutions by imposing some modifications. Through experiments performed on a paraphrase data set, we demonstrate that some of proposed approaches can improve the semantic similarity measurement of short text.

scholarly journals CoTO: A Novel Approach for Fuzzy Aggregation of Semantic Similarity Measures

2017 ◽  
Author(s):  
Jorge Martinez-Gil
Keyword(s):  
Fuzzy Logic ◽  
Semantic Similarity ◽  
Similarity Measures ◽  
Similarity Measurement ◽  
Trade Off ◽  
Novel Approach ◽  
Fuzzy Aggregation ◽  
Semantic Similarity Measurement ◽  
Traditional Approaches ◽  
Statistical Functions

Semantic similarity measurement aims to determine the likeness between two text expressions that use different lexicographies for representing the same real object or idea. There are a lot of semantic similarity measures for addressing this problem. However, the best results have been achieved when aggregating a number of simple similarity measures. This means that after the various similarity values have been calculated, the overall similarity for a pair of text expressions is computed using an aggregation function of these individual semantic similarity values. This aggregation is often computed by means of statistical functions. In this work, we present CoTO (Consensus or Trade-Off) a solution based on fuzzy logic that is able to outperform these traditional approaches.

Semantic Similarity for English and Arabic Texts: A Review

2020 ◽  
Vol 19 (04) ◽  
pp. 2050033
Author(s):  
Marwah Alian ◽  
Arafat Awajan
Keyword(s):  
Semantic Similarity ◽  
Language Processing ◽  
Pearson Correlation ◽  
Plagiarism Detection ◽  
Embedding Technique ◽  
Semantic Similarity Measurement ◽  
Feature Based ◽  
Arabic And English ◽  
Descriptive Feature ◽  
Degree Of Similarity

Semantic similarity is the task of measuring relations between sentences or words to determine the degree of similarity or resemblance. Several applications of natural language processing require semantic similarity measurement to achieve good results; these applications include plagiarism detection, text entailment, text summarisation, paraphrasing identification, and information extraction. Many researchers have proposed new methods to measure the semantic similarity of Arabic and English texts. In this research, these methods are reviewed and compared. Results show that the precision of the corpus-based approach exceeds 0.70. The precision of the descriptive feature-based technique is between 0.670 and 0.86, with a Pearson correlation coefficient of over 0.70. Meanwhile, the word embedding technique has a correlation of 0.67, and its accuracy is in the range 0.76–0.80. The best results are achieved by the feature-based approach.

Accurate Semantic Similarity Measurement of Biomedical Nomenclature by Means of Fuzzy Logic

2016 ◽  
Vol 24 (02) ◽  
pp. 291-305 ◽  
Author(s):  
Jorge Martinez-Gil
Keyword(s):  
Fuzzy Logic ◽  
Semantic Similarity ◽  
Similarity Measures ◽  
Similarity Measurement ◽  
Data Sets ◽  
Aggregation Method ◽  
Benchmark Data ◽  
Semantic Similarity Measurement ◽  
Incremental Improvement

Semantic similarity measurement of biomedical nomenclature aims to determine the likeness between two biomedical expressions that use different lexicographies for representing the same real biomedical concept. There are many semantic similarity measures for trying to address this issue, many of them have represented an incremental improvement over the previous ones. In this work, we present yet another incremental solution that is able to outperform existing approaches by using a sophisticated aggregation method based on fuzzy logic. Results show us that our strategy is able to consistently beat existing approaches when solving well-known biomedical benchmark data sets.

Similarity of Sentences With Contradiction Using Semantic Similarity Measures

2020 ◽  
Author(s):  
M Krishna Siva Prasad ◽  
Poonam Sharma
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Word Order ◽  
Similarity Measures ◽  
Semantic Features ◽  
Short Text ◽  
Sentence Similarity ◽  
Expert Ratings

Abstract Short text or sentence similarity is crucial in various natural language processing activities. Traditional measures for sentence similarity consider word order, semantic features and role annotations of text to derive the similarity. These measures do not suit short texts or sentences with negation. Hence, this paper proposes an approach to determine the semantic similarity of sentences and also presents an algorithm to handle negation. In sentence similarity, word pair similarity plays a significant role. Hence, this paper also discusses the similarity between word pairs. Existing semantic similarity measures do not handle antonyms accurately. Hence, this paper proposes an algorithm to handle antonyms. This paper also presents an antonym dataset with 111-word pairs and corresponding expert ratings. The existing semantic similarity measures are tested on the dataset. The results of the correlation proved that the expert ratings are in order with the correlation obtained from the semantic similarity measures. The sentence similarity is handled by proposing two algorithms. The first algorithm deals with the typical sentences, and the second algorithm deals with contradiction in the sentences. SICK dataset, which has sentences with negation, is considered for handling the sentence similarity. The algorithm helped in improving the results of sentence similarity.

scholarly journals Semantic similarity measurement using historical google search patterns

2017 ◽  
Author(s):  
Jorge Martinez-Gil ◽  
José F. Aldana Montes
Keyword(s):  
Semantic Similarity ◽  
Information Integration ◽  
General Purpose ◽  
Similarity Measurement ◽  
Short Text ◽  
Search Patterns ◽  
Benchmark Datasets ◽  
Semantic Similarity Measurement ◽  
Important Challenge ◽  
Google Search

Computing the semantic similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is an important challenge in the information integration field. The problem is that techniques for textual semantic similarity measurement often fail to deal with words not covered by synonym dictionaries. In this paper, we try to solve this problem by determining the semantic similarity for terms using the knowledge inherent in the search history logs from the Google search engine. To do this, we have designed and evaluated four algorithmic methods for measuring the semantic similarity between terms using their associated history search patterns. These algorithmic methods are: a) frequent co-occurrence of terms in search patterns, b) computation of the relationship between search patterns, c) outlier coincidence on search patterns, and d) forecasting comparisons. We have shown experimentally that some of these methods correlate well with respect to human judgment when evaluating general purpose benchmark datasets, and significantly outperform existing methods when evaluating datasets containing terms that do not usually appear in dictionaries.

Pembobotan Berdasarkan Tingkat Kesamaan Semantik pada Metode Fuzzy Semi-Supervised Co-Clustering untuk Pengelompokkan Dokumen Teks

2014 ◽  
Vol 6 (2) ◽  
pp. 46-51
Author(s):  
Galang Amanda Dwi P. ◽  
Gregorius Edwadr ◽  
Agus Zainal Arifin
Keyword(s):  
Supervised Learning ◽  
Semantic Similarity ◽  
The Other ◽  
Classification Result ◽  
Document Similarity ◽  
The Matrix ◽  
Index Terms ◽  
Membership Value ◽  
Degree Of Similarity

Nowadays, a large number of information can not be reached by the reader because of the misclassification of text-based documents. The misclassified data can also make the readers obtain the wrong information. The method which is proposed by this paper is aiming to classify the documents into the correct group.  Each document will have a membership value in several different classes. The method will be used to find the degree of similarity between the two documents is the semantic similarity. In fact, there is no document that doesn’t have a relationship with the other but their relationship might be close to 0. This method calculates the similarity between two documents by taking into account the level of similarity of words and their synonyms. After all inter-document similarity values obtained, a matrix will be created. The matrix is then used as a semi-supervised factor. The output of this method is the value of the membership of each document, which must be one of the greatest membership value for each document which indicates where the documents are grouped. Classification result computed by the method shows a good value which is 90 %. Index Terms - Fuzzy co-clustering, Heuristic, Semantica Similiarity, Semi-supervised learning.

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