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 901–910 of 1564 papers

Title	Date	Tasks	Status	Hype
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase	Apr 16, 2021	Data Augmentationintent-classification	—Unverified	0
Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders	Apr 16, 2021	Contrastive LearningCross-Lingual Semantic Textual Similarity	CodeCode Available	1
ALF -- A Fitness-Based Artificial Life Form for Evolving Large-Scale Neural Networks	Apr 16, 2021	Artificial LifeForm	—Unverified	0
Adaptive Logit Adjustment Loss for Long-Tailed Visual Recognition	Apr 13, 2021	General ClassificationSemantic Similarity	—Unverified	0
SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds	Apr 11, 2021	Semantic SegmentationSemantic Similarity	CodeCode Available	1
Disentangling Semantics and Syntax in Sentence Embeddings with Pre-trained Language Models	Apr 11, 2021	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	1
Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification	Apr 5, 2021	ClusteringSemantic Similarity	—Unverified	0
Automating Transfer Credit Assessment in Student Mobility -- A Natural Language Processing-based Approach	Apr 5, 2021	Decision MakingSemantic Similarity	—Unverified	0
Improving Cross-Lingual Transfer for Event Argument Extraction with Language-Universal Sentence Structures	Apr 1, 2021	Cross-Lingual TransferEvent Argument Extraction	—Unverified	0
Character Set Construction for Chinese Language Learning	Apr 1, 2021	ClusteringSemantic Similarity	—Unverified	0

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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