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 611–620 of 1564 papers

Title	Date	Tasks	Status	Hype
Improving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting	Oct 26, 2022	Adversarial RobustnessModel Optimization	CodeCode Available	0
A practical method for occupational skills detection in Vietnamese job listings	Oct 26, 2022	named-entity-recognitionNamed Entity Recognition	—Unverified	0
Pointly-Supervised Panoptic Segmentation	Oct 25, 2022	Panoptic SegmentationSegmentation	CodeCode Available	1
Describing Sets of Images with Textual-PCA	Oct 21, 2022	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	1
Multi-view Semantic Matching of Question retrieval using Fine-grained Semantic Representations	Oct 21, 2022	Keyword ExtractionQuestion Answering	—Unverified	0
Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation	Oct 18, 2022	Abstract Meaning RepresentationSemantic Similarity	CodeCode Available	0
Like a bilingual baby: The advantage of visually grounding a bilingual language model	Oct 11, 2022	Language ModelingLanguage Modelling	—Unverified	0
Learning to embed semantic similarity for joint image-text retrieval	Oct 7, 2022	Image-text RetrievalMetric Learning	—Unverified	0
GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene Flow	Oct 7, 2022	Autonomous DrivingMotion Segmentation	CodeCode Available	0
Reinforcement Learning with Large Action Spaces for Neural Machine Translation	Oct 6, 2022	Machine TranslationNMT	—Unverified	0

Show:10 25 50

← PrevPage 62 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 uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")	F1	89.3	—	Unverified
3	SciBERT cased (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