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 726–750 of 1564 papers

Title	Date	Tasks	Status	Hype
RELIC: Retrieving Evidence for Literary Claims	Mar 18, 2022	Information RetrievalRetrieval	CodeCode Available	1
RoMe: A Robust Metric for Evaluating Natural Language Generation	Mar 17, 2022	Natural Language UnderstandingSemantic Similarity	CodeCode Available	1
Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval	Mar 16, 2022	Information RetrievalRetrieval	—Unverified	0
SimMatch: Semi-supervised Learning with Similarity Matching	Mar 14, 2022	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	0
MarkBERT: Marking Word Boundaries Improves Chinese BERT	Mar 12, 2022	Chinese Named Entity Recognitionnamed-entity-recognition	CodeCode Available	1
Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity	Mar 10, 2022	Adversarial AttackSemantic Similarity	CodeCode Available	1
RACE: Retrieval-Augmented Commit Message Generation	Mar 5, 2022	Information RetrievalRetrieval	CodeCode Available	1
Just Rank: Rethinking Evaluation with Word and Sentence Similarities	Mar 5, 2022	BenchmarkingSemantic Similarity	CodeCode Available	1
'Tis but Thy Name: Semantic Question Answering Evaluation with 11M Names for 1M Entities	Feb 28, 2022	Question AnsweringSemantic Similarity	—Unverified	0
Supervised Machine Learning Algorithm for Detecting Consistency between Reported Findings and the Conclusions of Mammography Reports	Feb 28, 2022	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Attention-based Cross-Layer Domain Alignment for Unsupervised Domain Adaptation	Feb 27, 2022	Domain AdaptationSemantic Similarity	—Unverified	0
SemSup: Semantic Supervision for Simple and Scalable Zero-shot Generalization	Feb 26, 2022	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	0
JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning	Feb 22, 2022	Logical ReasoningSemantic Similarity	—Unverified	0
Autoencoding Low-Resolution MRI for Semantically Smooth Interpolation of Anisotropic MRI	Feb 18, 2022	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Moving Other Way: Exploring Word Mover Distance Extensions	Feb 7, 2022	ClassificationDocument Classification	—Unverified	0
Semantic Similarity Computing Model Based on Multi Model Fine-Grained Nonlinear Fusion	Feb 5, 2022	modelSemantic Similarity	—Unverified	0
Language Models Explain Word Reading Times Better Than Empirical Predictability	Feb 2, 2022	Additive modelsRetrieval	—Unverified	0
Duplicate Detection in a Knowledge Base with PIKA	Feb 1, 2022	Graph Neural NetworkInformation Retrieval	—Unverified	0
AI-based Approach for Safety Signals Detection from Social Networks: Application to the Levothyrox Scandal in 2017 on Doctissimo Forum	Feb 1, 2022	PharmacovigilanceSemantic Similarity	—Unverified	0
Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation	Jan 28, 2022	Data AugmentationReinforcement Learning (RL)	—Unverified	0
Learning Deep Semantic Model for Code Search using CodeSearchNet Corpus	Jan 27, 2022	Code SearchInformation Retrieval	CodeCode Available	1
Discriminative Supervised Subspace Learning for Cross-modal Retrieval	Jan 26, 2022	Cross-Modal RetrievalRetrieval	—Unverified	0
Reading-strategy Inspired Visual Representation Learning for Text-to-Video Retrieval	Jan 23, 2022	Representation LearningRetrieval	CodeCode Available	1
Reinforcement Learning with Large Action Spaces for Neural Machine Translation	Jan 16, 2022	Machine TranslationNMT	—Unverified	0
Task Formulation Matters When Learning Continuously: A Case Study in Visual Question Answering	Jan 16, 2022	Continual LearningIncremental Learning	—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