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 751–775 of 1564 papers

Title	Date	Tasks	Status	Hype
Task Formulation Matters When Learning Continuously: A Case Study in Visual Question Answering	Jan 16, 2022	Continual LearningIncremental Learning	—Unverified	0
SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples	Jan 16, 2022	Contrastive LearningData Augmentation	CodeCode Available	1
Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Replication Study	Jan 14, 2022	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	0
PromptBERT: Improving BERT Sentence Embeddings with Prompts	Jan 12, 2022	Contrastive LearningDenoising	CodeCode Available	2
Structure and Semantics Preserving Document Representations	Jan 11, 2022	Metric LearningRetrieval	—Unverified	0
Graph-Based Recommendation System Enhanced with Community Detection	Jan 10, 2022	Community DetectionRecommendation Systems	—Unverified	0
Semantic and sentiment analysis of selected Bhagavad Gita translations using BERT-based language framework	Jan 9, 2022	Deep LearningLanguage Modelling	CodeCode Available	0
Embeddings Evaluation Using a Novel Measure of Semantic Similarity	Jan 8, 2022	Embeddings EvaluationSemantic Similarity	CodeCode Available	0
HybridCR: Weakly-Supervised 3D Point Cloud Semantic Segmentation via Hybrid Contrastive Regularization	Jan 1, 2022	DiversitySemantic Segmentation	CodeCode Available	0
Traffic event description based on Twitter data using Unsupervised Learning Methods for Indian road conditions	Dec 23, 2021	ManagementSemantic Similarity	—Unverified	0
TFW2V: An Enhanced Document Similarity Method for the Morphologically Rich Finnish Language	Dec 23, 2021	BenchmarkingClustering	CodeCode Available	0
Cognition-aware Cognate Detection	Dec 15, 2021	Cross-Lingual Information RetrievalInformation Retrieval	CodeCode Available	0
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings	Dec 9, 2021	Contrastive LearningGraph Embedding	CodeCode Available	1
Superpixel-Based Building Damage Detection from Post-earthquake Imagery Using Deep Neural Networks	Dec 9, 2021	DenoisingRAG	—Unverified	0
DIBERT: Dependency Injected Bidirectional Encoder Representations from Transformers	Dec 5, 2021	Language ModelingLanguage Modelling	CodeCode Available	0
Cross-lingual Alignment of Knowledge Graph Triples with Sentences	Dec 1, 2021	Data-to-Text GenerationKnowledge Graph Completion	—Unverified	0
Contrastive Learning of Sentence Representations	Dec 1, 2021	Contrastive LearningSemantic Similarity	—Unverified	0
Classifying Verses of the Quran using Doc2vec	Dec 1, 2021	regressionSemantic Similarity	—Unverified	0
Cross-Domain Language Modeling: An Empirical Investigation	Dec 1, 2021	Language ModelingLanguage Modelling	—Unverified	0
Bilingual Topic Models for Comparable Corpora	Nov 30, 2021	Cross-Lingual Word EmbeddingsRetrieval	—Unverified	0
A Simple Long-Tailed Recognition Baseline via Vision-Language Model	Nov 29, 2021	Contrastive LearningLanguage Modeling	CodeCode Available	1
3D Compositional Zero-shot Learning with DeCompositional Consensus	Nov 29, 2021	BenchmarkingCompositional Zero-Shot Learning	—Unverified	0
Similarity Contrastive Estimation for Self-Supervised Soft Contrastive Learning	Nov 29, 2021	Contrastive LearningLinear evaluation	CodeCode Available	1
Code Clone Detection based on Event Embedding and Event Dependency	Nov 28, 2021	Clone DetectionSemantic Similarity	—Unverified	0
Using Distributional Principles for the Semantic Study of Contextual Language Models	Nov 23, 2021	Semantic SimilaritySemantic Textual Similarity	—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 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