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 131–140 of 1564 papers

Title	Date	Tasks	Status	Hype
Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained Vision Transformers	Apr 9, 2024	class-incremental learningClass Incremental Learning	CodeCode Available	1
CDF-RAG: Causal Dynamic Feedback for Adaptive Retrieval-Augmented Generation	Apr 17, 2025	RAGRetrieval	CodeCode Available	1
Automatic Generation of Topic Labels	May 29, 2020	DescriptiveInformation Retrieval	CodeCode Available	1
Catch-A-Waveform: Learning to Generate Audio from a Single Short Example	Jun 11, 2021	Audio GenerationSemantic Similarity	CodeCode Available	1
Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs	May 19, 2023	Question AnsweringSemantic Similarity	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
CmdCaliper: A Semantic-Aware Command-Line Embedding Model and Dataset for Security Research	Nov 2, 2024	Line DetectionSemantic Similarity	CodeCode Available	1
CODER: Knowledge infused cross-lingual medical term embedding for term normalization	Nov 5, 2020	Contrastive LearningKnowledge Graphs	CodeCode Available	1
ARMAN: Pre-training with Semantically Selecting and Reordering of Sentences for Persian Abstractive Summarization	Sep 9, 2021	Abstractive Text SummarizationDecoder	CodeCode Available	1
3D-AVS: LiDAR-based 3D Auto-Vocabulary Segmentation	Jun 13, 2024	Autonomous DrivingObject	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