REVIEW 1 cited by
Description and Evaluation of Semantic Similarity Measures Approaches
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
In recent years, semantic similarity measure has a great interest in Semantic Web and Natural Language Processing (NLP). Several similarity measures have been developed, being given the existence of a structured knowledge representation offered by ontologies and corpus which enable semantic interpretation of terms. Semantic similarity measures compute the similarity between concepts/terms included in knowledge sources in order to perform estimations. This paper discusses the existing semantic similarity methods based on structure, information content and feature approaches. Additionally, we present a critical evaluation of several categories of semantic similarity approaches based on two standard benchmarks. The aim of this paper is to give an efficient evaluation of all these measures which help researcher and practitioners to select the measure that best fit for their requirements.
Forward citations
Cited by 1 Pith paper
-
Validation of GPU Computation in Decentralized, Trustless Networks
Semantic similarity between LLM responses, with a fitted threshold of 0.5, separates same-model outputs from random responses and yields 76.5 percent held-out verification accuracy in a trusted-node setting.
Discussion (0). Continue with ORCID to comment.