Evaluating semantic similarity and relatedness between concepts by combining taxonomic and non-taxonomic semantic features of WordNet and Wikipedia

Hussain, Muhammad Jawad; Bai, Heming; Wasti, Shahbaz Hassan; Huang, Guangjian; Jiang, Yuncheng

doi:10.1016/j.ins.2023.01.007

Cited by 10 publications

(1 citation statement)

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“…SQAS Challenge 08: The number of studies working on two-way, three-way, and five-way answer categories. Many machine learning algorithms still have difficulty dealing with answers that are semantically ambiguous [10,147] and answers that match the main reference answer in multiple senses.…”

Section: Performance Measurements Of Bert Elmo and Gpt-2mentioning

confidence: 99%

Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives

et al. 2023

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In natural language processing, short-text semantic similarity (STSS) is a very prominent field. It has a significant impact on a broad range of applications, such as question–answering systems, information retrieval, entity recognition, text analytics, sentiment classification, and so on. Despite their widespread use, many traditional machine learning techniques are incapable of identifying the semantics of short text. Traditional methods are based on ontologies, knowledge graphs, and corpus-based methods. The performance of these methods is influenced by the manually defined rules. Applying such measures is still difficult, since it poses various semantic challenges. In the existing literature, the most recent advances in short-text semantic similarity (STSS) research are not included. This study presents the systematic literature review (SLR) with the aim to (i) explain short sentence barriers in semantic similarity, (ii) identify the most appropriate standard deep learning techniques for the semantics of a short text, (iii) classify the language models that produce high-level contextual semantic information, (iv) determine appropriate datasets that are only intended for short text, and (v) highlight research challenges and proposed future improvements. To the best of our knowledge, we have provided an in-depth, comprehensive, and systematic review of short text semantic similarity trends, which will assist the researchers to reuse and enhance the semantic information.

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Section: Performance Measurements Of Bert Elmo and Gpt-2mentioning

confidence: 99%