mlsanityData to 7 Oct 2026

Estimating Text Similarity based on Semantic Concept Embeddings

arXiv 2401.04422 ↗First posted 9 Jan 2024cs.CL cs.AIcited by 1

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Abstract (arXiv)

Due to their ease of use and high accuracy, Word2Vec (W2V) word embeddings enjoy great success in the semantic representation of words, sentences, and whole documents as well as for semantic similarity estimation. However, they have the shortcoming that they are directly extracted from a surface representation, which does not adequately represent human thought processes and also performs poorly for highly ambiguous words. Therefore, we propose Semantic Concept Embeddings (CE) based on the MultiNet Semantic Network (SN) formalism, which addresses both shortcomings. The evaluation on a marketing target group distribution task showed that the accuracy of predicted target groups can be increased by combining traditional word embeddings with semantic CEs.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 28 references that are in the map. First posted on arXiv 9 Jan 2024.

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13 of 28 references.

References · 13

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1 more
  1. 2018Learning Graph Embeddings from WordNet-based Similarity Measurescited by 2

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Citing papers per year: 2025: 1.

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  1. 2025Advances and Challenges in Semantic Textual Similarity: A Comprehensive Surveycited by 1