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 221–230 of 1564 papers

Title	Date	Tasks	Status	Hype
Similarity-weighted Construction of Contextualized Commonsense Knowledge Graphs for Knowledge-intense Argumentation Tasks	May 15, 2023	Knowledge GraphsSemantic Similarity	CodeCode Available	1
SMATCH++: Standardized and Extended Evaluation of Semantic Graphs	May 11, 2023	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	1
Approximating Human-Like Few-shot Learning with GPT-based Compression	Aug 14, 2023	Data CompressionFew-Shot Learning	—Unverified	0
Approche supervis\'ee de calcul de similarit\'e s\'emantique entre paires de phrases (Supervised approach to compute semantic similarity between sentence pairs)	Jun 1, 2020	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
A Graph-theoretic Summary Evaluation for ROUGE	Oct 1, 2018	Abstractive Text SummarizationSemantic Similarity	—Unverified	0
Apport des ontologies pour le calcul de la similarité sémantique au sein d'un système de recommandation	May 25, 2022	Recommendation SystemsSemantic Similarity	—Unverified	0
Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension	Jan 16, 2020	Machine Reading ComprehensionMultiple-choice	—Unverified	0
A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit	Aug 1, 2017	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
A Comparison of Semantic Similarity Methods for Maximum Human Interpretability	Oct 21, 2019	Information RetrievalRetrieval	—Unverified	0
API Is Enough: Conformal Prediction for Large Language Models Without Logit-Access	Mar 2, 2024	Conformal PredictionOpen-Ended Question Answering	—Unverified	0

Show:10 25 50

← PrevPage 23 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 cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	89.3	—	Unverified
3	SciBERT uncased (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