Classification of Semantic Similarity Technique between Word Pairs using Word Net

Gupta, Atul; Goyal, Krishan Kumar

doi:10.35940/ijeat.b2961.129219

Cited by 4 publications

(6 citation statements)

References 6 publications

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“…This paper tries to present an approach, which computes the similarity between two pair of verbs on knowingly the limitation of verb hierarchy of WordNet. The discussed approach is based on the noun-based similarity (Gupta & Goyal, 2019) and almost similar to the approach discussed in (Thanh et al). The novelty in this paper is by considering the derivational mapping into noun hierarchy while supplementing it into verb hierarchy, use of glosses definition, and the stemming effect.…”

Section: A Multi-strategic Verb Methodsmentioning

confidence: 99%

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Measuring of similarity between verb pairs in the biomedical domain

Gupta¹,

Sharma²,

Goyal³

2022

ijhs

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Finding the similarity Ontology-based between two verbs in bio-medical ontology is a difficult task as there is no standard dataset available. This paper is focused on verb-based similarity. So, the similarity between two nouns is taken as the benchmark for working on the verb similarity. There is no exact idea that the verb hierarchy of wordnet is capable to calculate the verb similarity between two verbs. The finding of similarity considers three parameters such as path, link, and depth. But in this paper, in addition to the path, link, and depth parameters, we also considered parameters such as stem-similarity weighting, derivation nouns weighting, and gloss similarity weighting. Moreover, we implemented two algorithms namely Rich Hierarchy Exploration and Shallow Hierarchy Exploration on a dataset and found that there is no significant difference in its performance.

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Section: A Multi-strategic Verb Methodsmentioning

confidence: 99%

“…The expansion of the work done in (Gupta & Goyal, 2019) on verb similarity where the performance of noun similarity is taken as a benchmark. In this paper, the calculation of the verb is done by two methodologies, the tuning method, and the evaluation partitioning method.…”

Section: Introductionmentioning

confidence: 99%

Measuring of similarity between verb pairs in the biomedical domain

Gupta¹,

Sharma²,

Goyal³

2022

ijhs

Self Cite

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“…The Path-length measure finds the shortest path between two synsets in the WordNet graph and assigns a similarity score based on the length of that path [7]. The Leacock & Chodorow measure considers the depth of the nodes in the graph in addition to the path length [8], [9], while the Wu & Palmer measure normalizes the path length based on the depth of the nodes in the graph to address the issue of path-length measures favoring longer paths over shorter ones [8], [9]. In contrast, the Resnik measure calculates similarity based on the frequency of each synset in a large corpus of text [9].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The Leacock & Chodorow measure considers the depth of the nodes in the graph in addition to the path length [8], [9], while the Wu & Palmer measure normalizes the path length based on the depth of the nodes in the graph to address the issue of path-length measures favoring longer paths over shorter ones [8], [9]. In contrast, the Resnik measure calculates similarity based on the frequency of each synset in a large corpus of text [9]. The Lin measure uses both similarity and relatedness between concepts to compute similarity [9].…”

Section: Literature Reviewmentioning

confidence: 99%

WordNet-based Computation of Semantic Similarity Between Two Vietnamese Nouns

Phan

Duong

Tran

2023

TSSJ

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Abstract. This study aimed to measure the semantic distance between Vietnamese nouns to determine their similarities and differences in terms of semantics. The study utilized a dataset of 1000 randomly selected noun pairs from the Vietnamese WordNet and applied different semantic distance measurement formulas, such as Path-length, Leacock & Chodorow, Wu & Palmer, Resnik, Lin, and Jiang-Conrath measures. The results showed that the Resnik measure is suitable for evaluating noun similarity among primitive concepts of the same class and for measuring proximity between two classes of primitive concepts. On the other hand, the Leacock & Chodorow measure is appropriate for measuring the semantic distance between nouns belonging to two distant classes of primitive concepts. The methods used in this study have various potential applications in different fields of linguistics, such as determining synonyms, identifying semantically similar words, defining lexical-semantic fields, and detecting plagiarism.

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“…It is not possible to find and remove duplicate questions manually. The duplicate inquiries or sentences should be identified automatically using some autodetection approaches [4,5,6]. In order to find similarities between two sentences, we www.ijacsa.thesai.org conducted our research using Quora's question-pair dataset.…”

Section: Introductionmentioning

confidence: 99%

Strengthening Sentence Similarity Identification Through OpenAI Embeddings and Deep Learning

Korade,

Salunke,

Bhosle

et al. 2024

IJACSA

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Discovering similarity between sentences can be beneficial to a variety of systems, including chatbots for customer support, educational platforms, e-commerce customer inquiries, and community forums or question-answering systems. One of the primary issues that online question-answering platforms and customer service chatbots have is the large number of duplicate inquiries that are placed on the platform. In addition to cluttering up the platform, these repetitive queries degrade the content's quality and make it harder for visitors to locate pertinent information. Therefore, it is necessary to automatically detect sentence similarity in order to improve the user experience and quickly match user expectations. The present study makes use of the Quora dataset to construct a framework for similarity discovery in sentence pairs. As part of our research, we have built additional attributes based on textual data for improving the accuracy of similarity prediction. The study investigates several vectorization methods and their influence on accuracy. To convert preprocess text input to a numerical vector, we implemented Word2Vec, FastText, Term Frequency-Inverse Document Frequency (TF-IDF), CountVectorizer (CV), and OpenAI embedding. In order to judge sentence similarity, the embedding offered by several approaches was used with various models, including cosine similarity, Random Forest (RF), AdaBoost, XGBoost, LSTM, and CNN. The result demonstrates that all algorithms trained on OpenAI embedding yield excellent outcomes. The OpenAI-created embedding offers excellent information to models trained on it and has significant potential for capturing sentence similarity.

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Classification of Semantic Similarity Technique between Word Pairs using Word Net

Cited by 4 publications

References 6 publications

Measuring of similarity between verb pairs in the biomedical domain

Measuring of similarity between verb pairs in the biomedical domain

WordNet-based Computation of Semantic Similarity Between Two Vietnamese Nouns

Strengthening Sentence Similarity Identification Through OpenAI Embeddings and Deep Learning

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