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 1076–1100 of 1564 papers

Title	Date	Tasks	Status	Hype
Symmetrical Synthesis for Deep Metric Learning	Jan 31, 2020	ClusteringImage Retrieval	CodeCode Available	1
Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding	Jan 29, 2020	Document EmbeddingSemantic Similarity	—Unverified	0
Hybrid Semantic Recommender System for Chemical Compounds	Jan 21, 2020	Collaborative FilteringRecommendation Systems	CodeCode Available	0
Classifying Wikipedia in a fine-grained hierarchy: what graphs can contribute	Jan 21, 2020	ClassificationGeneral Classification	—Unverified	0
User-in-the-loop Adaptive Intent Detection for Instructable Digital Assistant	Jan 16, 2020	Intent DetectionNatural Language Understanding	CodeCode Available	0
Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension	Jan 16, 2020	Machine Reading ComprehensionMultiple-choice	—Unverified	0
Open Domain Question Answering Using Web Tables	Jan 10, 2020	Open-Domain Question AnsweringQuestion Answering	—Unverified	0
Ranking and Classification driven Feature Learning for Person Re_identification	Dec 25, 2019	ClassificationGeneral Classification	CodeCode Available	0
MALA: Cross-Domain Dialogue Generation with Action Learning	Dec 18, 2019	Dialogue GenerationResponse Generation	—Unverified	0
Semantic Similarity To Improve Question Understanding in a Virtual Patient	Dec 16, 2019	Medical DiagnosisSemantic Similarity	—Unverified	0
Legal document retrieval across languages: topic hierarchies based on synsets	Nov 28, 2019	RetrievalSemantic Similarity	—Unverified	0
Instance Cross Entropy for Deep Metric Learning	Nov 22, 2019	Metric LearningSemantic Similarity	—Unverified	0
Co-Attention Hierarchical Network: Generating Coherent Long Distractors for Reading Comprehension	Nov 20, 2019	DecoderDistractor Generation	—Unverified	0
SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word Embeddings	Nov 20, 2019	Community Question AnsweringQuestion Answering	CodeCode Available	0
Weakly-Supervised Video Moment Retrieval via Semantic Completion Network	Nov 19, 2019	Moment RetrievalRetrieval	—Unverified	0
Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines	Nov 10, 2019	Few-Shot LearningLanguage Modeling	CodeCode Available	0
A Bilingual Generative Transformer for Semantic Sentence Embedding	Nov 10, 2019	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	0
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation	Nov 8, 2019	Articlescounterfactual	—Unverified	0
What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning	Nov 8, 2019	Linguistic AcceptabilityNatural Language Inference	—Unverified	0
Explicit Pairwise Word Interaction Modeling Improves Pretrained Transformers for English Semantic Similarity Tasks	Nov 7, 2019	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Gextext: Disease Network Extraction from Biomedical Literature	Nov 6, 2019	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Machine Translation Evaluation using Bi-directional Entailment	Nov 2, 2019	Machine TranslationSemantic Similarity	—Unverified	0
Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach	Nov 1, 2019	Meta-LearningSemantic Similarity	—Unverified	0
Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling	Nov 1, 2019	Natural Language InferenceQuestion Answering	—Unverified	0
Team GPLSI. Approach for automated fact checking	Nov 1, 2019	ArticlesFact Checking	—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