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	Hype
Iterative Relevance Feedback for Answer Passage Retrieval with Passage-level Semantic Match	Dec 20, 2018	Passage RetrievalRetrieval	—Unverified	0
TextBugger: Generating Adversarial Text Against Real-world Applications	Dec 13, 2018	Adversarial TextMachine Translation	CodeCode Available	0
Temporal Analysis of Entity Relatedness and its Evolution using Wikipedia and DBpedia	Dec 12, 2018	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
A Knowledge Graph Based Solution for Entity Discovery and Linking in Open-Domain Questions	Dec 5, 2018	Entity LinkingLearning-To-Rank	—Unverified	0
Are you tough enough? Framework for Robustness Validation of Machine Comprehension Systems	Dec 5, 2018	Question AnsweringReading Comprehension	CodeCode Available	0
MedSim: A Novel Semantic Similarity Measure in Bio-medical Knowledge Graphs	Dec 5, 2018	Knowledge GraphsSemantic Similarity	—Unverified	0
Twitter-based traffic information system based on vector representations for words	Dec 4, 2018	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Learning semantic similarity in a continuous space	Dec 1, 2018	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Generative Models for Simulating Mobility Trajectories	Nov 30, 2018	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
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	0
Sequence Learning with RNNs for Medical Concept Normalization in User-Generated Texts	Nov 28, 2018	Medical Concept NormalizationSemantic Similarity	—Unverified	0
Generalised Differential Privacy for Text Document Processing	Nov 26, 2018	Authorship AttributionBIG-bench Machine Learning	—Unverified	0
Not just a matter of semantics: the relationship between visual similarity and semantic similarity	Nov 17, 2018	Image RetrievalRetrieval	—Unverified	0
Investigating the Effects of Word Substitution Errors on Sentence Embeddings	Nov 16, 2018	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	CodeCode Available	0
Deep Metric Learning by Online Soft Mining and Class-Aware Attention	Nov 4, 2018	Metric LearningPerson Re-Identification	CodeCode Available	1
Augmenting Neural Response Generation with Context-Aware Topical Attention	Nov 2, 2018	DecoderOpen-Domain Dialog	CodeCode Available	0
MindLab Neural Network Approach at BioASQ 6B	Nov 1, 2018	Information RetrievalPosition	—Unverified	0
Efficient learning of neighbor representations for boundary trees and forests	Oct 26, 2018	ClassificationGeneral Classification	—Unverified	0
Predicting the Semantic Textual Similarity with Siamese CNN and LSTM	Oct 24, 2018	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
A mathematical theory of semantic development in deep neural networks	Oct 23, 2018	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	0
Unsupervised Features Extraction for Binary Similarity Using Graph Embedding Neural Networks	Oct 23, 2018	Graph EmbeddingMalware Analysis	—Unverified	0
Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages	Oct 20, 2018	Machine TranslationSemantic Similarity	—Unverified	0
Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations	Oct 1, 2018	DiversityGeneral Classification	—Unverified	0
Controlling Length in Abstractive Summarization Using a Convolutional Neural Network	Oct 1, 2018	Abstractive Text SummarizationMachine Translation	CodeCode Available	0
Auto-Encoding Dictionary Definitions into Consistent Word Embeddings	Oct 1, 2018	Document ClassificationMachine Translation	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 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