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

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Showing 1451–1460 of 1564 papers

Title	Date	Tasks	Status
UMBCLU at SemEval-2024 Task 1A and 1C: Semantic Textual Relatedness with and without machine translation	Feb 20, 2024	Machine TranslationNatural Language Understanding	CodeCode Available
LyricSIM: A novel Dataset and Benchmark for Similarity Detection in Spanish Song LyricS	Jun 2, 2023	Semantic SimilaritySemantic Textual Similarity	CodeCode Available
Textual analysis of artificial intelligence manuscripts reveals features associated with peer review outcome	Oct 21, 2019	Semantic SimilaritySemantic Textual Similarity	CodeCode Available
Urban Traffic Accident Risk Prediction Revisited: Regionality, Proximity, Similarity and Sparsity	Jul 29, 2024	Semantic SimilaritySemantic Textual Similarity	CodeCode Available
Machine Translation Hallucination Detection for Low and High Resource Languages using Large Language Models	Jul 23, 2024	HallucinationMachine Translation	CodeCode Available
Investigating the Frequency Distortion of Word Embeddings and Its Impact on Bias Metrics	Nov 15, 2022	Semantic SimilaritySemantic Textual Similarity	CodeCode Available
Concept-Level Explainability for Auditing & Steering LLM Responses	May 12, 2025	Prompt EngineeringSemantic Similarity	CodeCode Available
Making Fast Graph-based Algorithms with Graph Metric Embeddings	Jun 17, 2019	Semantic SimilaritySemantic Textual Similarity	CodeCode Available
Rematch: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity	Apr 2, 2024	Abstract Meaning RepresentationFact Checking	CodeCode Available
Representation learning for very short texts using weighted word embedding aggregation	Jul 2, 2016	Event DetectionNews Recommendation	CodeCode Available

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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