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 981–990 of 1564 papers

Title	Date	Tasks	Status	Hype
Neural Passage Retrieval with Improved Negative Contrast	Oct 23, 2020	Open-Domain Question AnsweringPassage Retrieval	—Unverified	0
GiBERT: Introducing Linguistic Knowledge into BERT through a Lightweight Gated Injection Method	Oct 23, 2020	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers	Oct 22, 2020	Adversarial AttackSemantic Similarity	—Unverified	0
Latte-Mix: Measuring Sentence Semantic Similarity with Latent Categorical Mixtures	Oct 21, 2020	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters	Oct 20, 2020	Clinical Concept ExtractionDrug–drug Interaction Extraction	CodeCode Available	1
CIMON: Towards High-quality Hash Codes	Oct 15, 2020	Computational EfficiencyImage Augmentation	—Unverified	0
FILM: A Fast, Interpretable, and Low-rank Metric Learning Approach for Sentence Matching	Oct 12, 2020	Metric LearningSemantic Similarity	—Unverified	0
Meta-Context Transformers for Domain-Specific Response Generation	Oct 12, 2020	Dialogue GenerationLanguage Modeling	CodeCode Available	0
Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 Network	Oct 12, 2020	ColorizationDiversity	CodeCode Available	1
Webly Supervised Image Classification with Metadata: Automatic Noisy Label Correction via Visual-Semantic Graph	Oct 12, 2020	General ClassificationGraph Neural Network	CodeCode Available	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