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 191–200 of 1564 papers

Title	Date	Tasks	Status	Hype	Score
Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors	Oct 8, 2021	Language ModelingLanguage Modelling	CodeCode Available	1	5
DriveDiTFit: Fine-tuning Diffusion Transformers for Autonomous Driving	Jul 22, 2024	Autonomous DrivingDiversity	CodeCode Available	1	5
DuSSS: Dual Semantic Similarity-Supervised Vision-Language Model for Semi-Supervised Medical Image Segmentation	Dec 17, 2024	Contrastive LearningImage Segmentation	CodeCode Available	1	5
High Temporal Consistency through Semantic Similarity Propagation in Semi-Supervised Video Semantic Segmentation for Autonomous Flight	Mar 19, 2025	Image SegmentationKnowledge Distillation	CodeCode Available	1	5
3D-AVS: LiDAR-based 3D Auto-Vocabulary Segmentation	Jun 13, 2024	Autonomous DrivingObject	CodeCode Available	1	5
Efficient Mask Correction for Click-Based Interactive Image Segmentation	Jan 1, 2023	Image SegmentationSegmentation	CodeCode Available	1	5
Improving word mover's distance by leveraging self-attention matrix	Nov 11, 2022	Paraphrase IdentificationSemantic Similarity	CodeCode Available	1	5
Retrieval-Augmented Open-Vocabulary Object Detection	Apr 8, 2024	Language ModelingLanguage Modelling	CodeCode Available	1	5
R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering	May 24, 2018	Question AnsweringRelation	CodeCode Available	1	5
Benchmarking large language models for biomedical natural language processing applications and recommendations	May 10, 2023	BenchmarkingDocument Classification	CodeCode Available	1	5

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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 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