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	Score
RoMe: A Robust Metric for Evaluating Natural Language Generation	Mar 17, 2022	Natural Language UnderstandingSemantic Similarity	CodeCode Available	1	5
Generalized Product Quantization Network for Semi-supervised Image Retrieval	Feb 26, 2020	Image RetrievalMetric Learning	CodeCode Available	1	5
Identifying Cognate Sets Across Dictionaries of Related Languages	Sep 1, 2017	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	0	5
Hyperpolyglot LLMs: Cross-Lingual Interpretability in Token Embeddings	Nov 29, 2023	Cross-Lingual TransferLanguage Modeling	CodeCode Available	0	5
Identifying Semantic Divergences in Parallel Text without Annotations	Mar 29, 2018	Machine TranslationSemantic Similarity	CodeCode Available	0	5
Effective and Imperceptible Adversarial Textual Attack via Multi-objectivization	Nov 2, 2021	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	0	5
Hybrid Semantic Recommender System for Chemical Compounds	Jan 21, 2020	Collaborative FilteringRecommendation Systems	CodeCode Available	0	5
Hypercube-RAG: Hypercube-Based Retrieval-Augmented Generation for In-domain Scientific Question-Answering	May 25, 2025	Question AnsweringRAG	CodeCode Available	0	5
Image Similarity using An Ensemble of Context-Sensitive Models	Jan 15, 2024	Dimensionality ReductionSemantic Similarity	CodeCode Available	0	5
HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text	Feb 2, 2024	Adversarial AttackHard-label Attack	CodeCode Available	0	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 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