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 1001–1025 of 1564 papers

Title	Date	Tasks	Status	Hype
Time-Aware Evidence Ranking for Fact-Checking	Sep 10, 2020	ClusteringFact Checking	—Unverified	0
Why Not Simply Translate? A First Swedish Evaluation Benchmark for Semantic Similarity	Sep 7, 2020	Machine TranslationSemantic Similarity	CodeCode Available	1
SEEC: Semantic Vector Federation across Edge Computing Environments	Aug 30, 2020	Edge-computingFederated Learning	—Unverified	0
Linked Credibility Reviews for Explainable Misinformation Detection	Aug 28, 2020	MisinformationSemantic Similarity	CodeCode Available	1
MultiGBS: A multi-layer graph approach to biomedical summarization	Aug 27, 2020	Extractive Text SummarizationSemantic Similarity	—Unverified	0
TabSim: A Siamese Neural Network for Accurate Estimation of Table Similarity	Aug 25, 2020	ArticlesImputation	—Unverified	0
FAT ALBERT: Finding Answers in Large Texts using Semantic Similarity Attention Layer based on BERT	Aug 22, 2020	Multiple-choiceQuestion Answering	CodeCode Available	0
Top2Vec: Distributed Representations of Topics	Aug 19, 2020	LemmatizationSemantic Similarity	CodeCode Available	2
A Survey on Text Simplification	Aug 19, 2020	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity	Aug 11, 2020	DiversityMachine Translation	CodeCode Available	1
Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs	Aug 4, 2020	Question AnsweringQuestion Similarity	—Unverified	0
Measuring prominence of scientific work in online news as a proxy for impact	Jul 28, 2020	ArticlesSemantic Similarity	—Unverified	0
Hard negative examples are hard, but useful	Jul 24, 2020	Image RetrievalMetric Learning	CodeCode Available	1
Logic Constrained Pointer Networks for Interpretable Textual Similarity	Jul 15, 2020	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	0
CORD19STS: COVID-19 Semantic Textual Similarity Dataset	Jul 5, 2020	Information RetrievalLanguage Modelling	—Unverified	0
Unsupervised Paraphrasing via Deep Reinforcement Learning	Jul 5, 2020	Deep Reinforcement LearningDiversity	—Unverified	0
Language-agnostic BERT Sentence Embedding	Jul 3, 2020	Language ModelingLanguage Modelling	CodeCode Available	1
Unsupervised Semantic Hashing with Pairwise Reconstruction	Jul 1, 2020	DecoderSemantic Similarity	CodeCode Available	0
DeSpin: a prototype system for detecting spin in biomedical publications	Jul 1, 2020	ArticlesRelation Extraction	—Unverified	0
Text Classification with Negative Supervision	Jul 1, 2020	ClassificationGeneral Classification	—Unverified	0
tBERT: Topic Models and BERT Joining Forces for Semantic Similarity Detection	Jul 1, 2020	Natural Language UnderstandingSemantic Similarity	CodeCode Available	1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity Rewards	Jun 27, 2020	Machine Translationreinforcement-learning	CodeCode Available	1
Class-Similarity Based Label Smoothing for Confidence Calibration	Jun 24, 2020	Decision MakingSemantic Similarity	—Unverified	0
Exploiting Non-Taxonomic Relations for Measuring Semantic Similarity and Relatedness in WordNet	Jun 22, 2020	ClusteringInformation Retrieval	—Unverified	0
Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network	Jun 17, 2020	Graph Neural NetworkNoun Phrase Canonicalization	—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