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 651–660 of 1564 papers

Title	Date	Tasks	Status	Hype
Evolving the MCTS Upper Confidence Bounds for Trees Using a Semantic-inspired Evolutionary Algorithm in the Game of Carcassonne	Aug 29, 2022	Evolutionary AlgorithmsSemantic Similarity	—Unverified	0
Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approach	Aug 17, 2022	reinforcement-learningReinforcement Learning (RL)	—Unverified	0
MENLI: Robust Evaluation Metrics from Natural Language Inference	Aug 15, 2022	Adversarial AttackAdversarial Robustness	CodeCode Available	1
Paraphrasing, textual entailment, and semantic similarity above word level	Aug 10, 2022	Natural Language InferenceParaphrase Identification	—Unverified	0
Using Sentence Embeddings and Semantic Similarity for Seeking Consensus when Assessing Trustworthy AI	Aug 9, 2022	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	0
Towards Semantic Communications: Deep Learning-Based Image Semantic Coding	Aug 8, 2022	Deep LearningImage Reconstruction	—Unverified	0
Uncertainty-based Visual Question Answering: Estimating Semantic Inconsistency between Image and Knowledge Base	Jul 27, 2022	Question AnsweringSemantic Similarity	—Unverified	0
A Cognitive Study on Semantic Similarity Analysis of Large Corpora: A Transformer-based Approach	Jul 24, 2022	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Tackling Long-Tailed Category Distribution Under Domain Shifts	Jul 20, 2022	Domain GeneralizationMeta-Learning	CodeCode Available	1
PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search	Jul 19, 2022	Information RetrievalNatural Language Understanding	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