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 321–330 of 1564 papers

Title	Date	Tasks	Status	Score
How does BERT capture semantics? A closer look at polysemous words	Nov 1, 2020	Semanticity predictionSemantic Similarity	CodeCode Available	5
Hybrid Semantic Recommender System for Chemical Compounds	Jan 21, 2020	Collaborative FilteringRecommendation Systems	CodeCode Available	5
Hyperpolyglot LLMs: Cross-Lingual Interpretability in Token Embeddings	Nov 29, 2023	Cross-Lingual TransferLanguage Modeling	CodeCode Available	5
Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization	Jun 8, 2017	DecoderSemantic Similarity	CodeCode Available	5
Augmenting Neural Response Generation with Context-Aware Topical Attention	Nov 2, 2018	DecoderOpen-Domain Dialog	CodeCode Available	5
Hierarchy-based Image Embeddings for Semantic Image Retrieval	Sep 26, 2018	Few-Shot LearningImage Retrieval	CodeCode Available	5
Guiding and Diversifying LLM-Based Story Generation via Answer Set Programming	Jun 1, 2024	DiversitySemantic Similarity	CodeCode Available	5
A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic loss	Jun 20, 2021	DiversityLanguage Modeling	CodeCode Available	5
HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community Detection	Nov 4, 2024	Anomaly DetectionAttribute	CodeCode Available	5
Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment	Jan 1, 2025	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	5

Show:10 25 50

← PrevPage 33 of 157Next →

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