Semantic Similarity

The main objective Semantic Similarity is to measure the distance between the semantic meanings of a pair of words, phrases, sentences, or documents. For example, the word “car” is more similar to “bus” than it is to “cat”. The two main approaches to measuring Semantic Similarity are knowledge-based approaches and corpus-based, distributional methods.

Source: Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 541–550 of 1564 papers

Title	Date	Tasks	Status
Evaluating Deduplication Techniques for Economic Research Paper Titles with a Focus on Semantic Similarity using NLP and LLMs	Oct 2, 2024	Semantic SimilaritySemantic Textual Similarity	—Unverified
Evaluating Contrastive Models for Instance-based Image Retrieval	Apr 30, 2021	Image RetrievalRetrieval	—Unverified
Apport des ontologies pour le calcul de la similarité sémantique au sein d'un système de recommandation	May 25, 2022	Recommendation SystemsSemantic Similarity	—Unverified
Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning	Sep 9, 2024	AttributeFraud Detection	—Unverified
\'Etat de l'art : mesures de similarit\'e s\'emantique locales et algorithmes globaux pour la d\'esambigu\" lexicale \`a base de connaissances (State of the art : Local Semantic Similarity Measures and Global Algorithmes for Knowledge-based Word Sense Disambiguation) [in French]	Jun 1, 2012	Semantic SimilaritySemantic Textual Similarity	—Unverified
Estimating Text Similarity based on Semantic Concept Embeddings	Jan 9, 2024	MarketingSemantic Similarity	—Unverified
Calculating Semantic Similarity between Academic Articles using Topic Event and Ontology	Nov 30, 2017	Articlesdocument understanding	—Unverified
Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension	Jan 16, 2020	Machine Reading ComprehensionMultiple-choice	—Unverified
A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit	Aug 1, 2017	Semantic SimilaritySemantic Textual Similarity	—Unverified
A Comparison of Semantic Similarity Methods for Maximum Human Interpretability	Oct 21, 2019	Information RetrievalRetrieval	—Unverified

Show:10 25 50

← PrevPage 55 of 157Next →

All datasets Annotated corpus for semantic similarity of clinical trial outcomes (expanded corpus)Annotated corpus for semantic similarity of clinical trial outcomes (original corpus)SICK BIOSSES CHIP-STS ClinicalSTS MedSTS

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")	F1	93.38	—	Unverified
2	SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")	F1	91.51	—	Unverified
3	SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")	F1	90.69	—	Unverified
4	BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")	F1	89.16	—	Unverified
5	BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")	F1	89.12	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	89.75	—	Unverified
2	SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	89.3	—	Unverified
3	SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	89.3	—	Unverified
4	BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	86.8	—	Unverified
5	BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	84.21	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Doc2VecC	MSE	0.31	—	Unverified
2	LSTM (Tai et al., 2015)	MSE	0.28	—	Unverified
3	Bidirectional LSTM (Tai et al., 2015)	MSE	0.27	—	Unverified
4	combine-skip (Kiros et al., 2015)	MSE	0.27	—	Unverified
5	Dependency Tree-LSTM (Tai et al., 2015)	MSE	0.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BioLinkBERT (large)	Pearson Correlation	0.94	—	Unverified
2	BioLinkBERT (base)	Pearson Correlation	0.93	—	Unverified
3	NCBI_BERT(base) (P+M)	Pearson Correlation	0.92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MacBERT-large	Macro F1	85.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CharacterBERT (base, medical, ensemble)	Pearson Correlation	85.62	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	NCBI_BERT(base) (P+M)	Pearson Correlation	0.85	—	Unverified