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 1201–1225 of 1564 papers

Title	Date	Tasks	Status
Comparison of State-of-the-Art Deep Learning APIs for Image Multi-Label Classification using Semantic Metrics	Mar 21, 2019	General ClassificationMulti-Label Classification	CodeCode Available
Ranked List Loss for Deep Metric Learning	Mar 8, 2019	Image RetrievalMetric Learning	CodeCode Available
EL Embeddings: Geometric construction of models for the Description Logic EL ++	Feb 27, 2019	Graph EmbeddingKnowledge Graph Embedding	CodeCode Available
Investigating Antigram Behaviour using Distributional Semantics	Jan 15, 2019	Semantic SimilaritySemantic Textual Similarity	—Unverified
ParaBank: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-constrained Neural Machine Translation	Jan 11, 2019	DiversityMachine Translation	—Unverified
Hierarchy Neighborhood Discriminative Hashing for An Unified View of Single-Label and Multi-Label Image retrieval	Jan 10, 2019	Image RetrievalMulti-Label Image Retrieval	—Unverified
Improving Context-Aware Semantic Relationships in Sparse Mobile Datasets	Dec 23, 2018	Semantic SimilaritySemantic Textual Similarity	—Unverified
Iterative Relevance Feedback for Answer Passage Retrieval with Passage-level Semantic Match	Dec 20, 2018	Passage RetrievalRetrieval	—Unverified
TextBugger: Generating Adversarial Text Against Real-world Applications	Dec 13, 2018	Adversarial TextMachine Translation	CodeCode Available
Temporal Analysis of Entity Relatedness and its Evolution using Wikipedia and DBpedia	Dec 12, 2018	Semantic SimilaritySemantic Textual Similarity	—Unverified
MedSim: A Novel Semantic Similarity Measure in Bio-medical Knowledge Graphs	Dec 5, 2018	Knowledge GraphsSemantic Similarity	—Unverified
Are you tough enough? Framework for Robustness Validation of Machine Comprehension Systems	Dec 5, 2018	Question AnsweringReading Comprehension	CodeCode Available
A Knowledge Graph Based Solution for Entity Discovery and Linking in Open-Domain Questions	Dec 5, 2018	Entity LinkingLearning-To-Rank	—Unverified
Twitter-based traffic information system based on vector representations for words	Dec 4, 2018	Semantic SimilaritySemantic Textual Similarity	—Unverified
Learning semantic similarity in a continuous space	Dec 1, 2018	Semantic SimilaritySemantic Textual Similarity	—Unverified
Generative Models for Simulating Mobility Trajectories	Nov 30, 2018	Semantic SimilaritySemantic Textual Similarity	—Unverified
Sequence Learning with RNNs for Medical Concept Normalization in User-Generated Texts	Nov 28, 2018	Medical Concept NormalizationSemantic Similarity	—Unverified
The MeSH-gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for UMLS Semantic Similarity and Relatedness in the Biomedical Domain	Nov 28, 2018	Semantic SimilaritySemantic Textual Similarity	—Unverified
Generalised Differential Privacy for Text Document Processing	Nov 26, 2018	Authorship AttributionBIG-bench Machine Learning	—Unverified
Not just a matter of semantics: the relationship between visual similarity and semantic similarity	Nov 17, 2018	Image RetrievalRetrieval	—Unverified
Investigating the Effects of Word Substitution Errors on Sentence Embeddings	Nov 16, 2018	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available
Augmenting Neural Response Generation with Context-Aware Topical Attention	Nov 2, 2018	DecoderOpen-Domain Dialog	CodeCode Available
MindLab Neural Network Approach at BioASQ 6B	Nov 1, 2018	Information RetrievalPosition	—Unverified
Efficient learning of neighbor representations for boundary trees and forests	Oct 26, 2018	ClassificationGeneral Classification	—Unverified
Predicting the Semantic Textual Similarity with Siamese CNN and LSTM	Oct 24, 2018	Semantic SimilaritySemantic Textual Similarity	—Unverified

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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