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 211–220 of 1564 papers

Title	Date	Tasks	Status	Hype
CgAT: Center-Guided Adversarial Training for Deep Hashing-Based Retrieval	Apr 18, 2022	Adversarial AttackAdversarial Defense	CodeCode Available	1
Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference	Jun 2, 2021	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	1
Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning	Nov 7, 2024	Decision MakingFairness	CodeCode Available	1
Semantic Novelty Detection via Relational Reasoning	Jul 18, 2022	Autonomous DrivingEdge-computing	CodeCode Available	1
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters	Oct 20, 2020	Clinical Concept ExtractionDrug–drug Interaction Extraction	CodeCode Available	1
Semantic similarity metrics for learned image registration	Apr 20, 2021	Image RegistrationSemantic Similarity	CodeCode Available	1
SemMT: A Semantic-based Testing Approach for Machine Translation Systems	Dec 3, 2020	Machine TranslationSemantic Similarity	CodeCode Available	1
SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languages	Feb 13, 2024	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	1
SGC-Net: Stratified Granular Comparison Network for Open-Vocabulary HOI Detection	Mar 1, 2025	Human-Object Interaction DetectionLarge Language Model	CodeCode Available	1
Why Not Simply Translate? A First Swedish Evaluation Benchmark for Semantic Similarity	Sep 7, 2020	Machine TranslationSemantic Similarity	CodeCode Available	1

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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