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 1151–1160 of 1564 papers

Title	Date	Tasks	Status	Hype
Visualising WordNet Embeddings: some preliminary results	Jul 1, 2019	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Aiming beyond the Obvious: Identifying Non-Obvious Cases in Semantic Similarity Datasets	Jul 1, 2019	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	0
Using Semantic Similarity as Reward for Reinforcement Learning in Sentence Generation	Jul 1, 2019	NMTreinforcement-learning	—Unverified	0
Sentence Mover's Similarity: Automatic Evaluation for Multi-Sentence Texts	Jul 1, 2019	Reinforcement LearningSemantic Similarity	—Unverified	0
Beyond BLEU:Training Neural Machine Translation with Semantic Similarity	Jul 1, 2019	DiversityMachine Translation	—Unverified	0
Generating Natural Language Adversarial Examples through Probability Weighted Word Saliency	Jul 1, 2019	Adversarial AttackClassification	CodeCode Available	0
Uncovering the Semantics of Wikipedia Categories	Jun 28, 2019	Entity DisambiguationKnowledge Graphs	CodeCode Available	0
Making Fast Graph-based Algorithms with Graph Metric Embeddings	Jun 17, 2019	Semantic SimilaritySemantic Textual Similarity	CodeCode Available	0
Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets	Jun 13, 2019	BenchmarkingDocument Classification	CodeCode Available	1
Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking	Jun 9, 2019	Object TrackingSemantic Similarity	CodeCode Available	0

Show:10 25 50

← PrevPage 116 of 157Next →

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