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 1381–1390 of 1564 papers

Title	Date	Tasks	Status	Hype
No Fuss Distance Metric Learning using Proxies	Mar 21, 2017	Metric LearningSemantic Similarity	CodeCode Available	1
Story Cloze Ending Selection Baselines and Data Examination	Mar 13, 2017	Cloze TestSemantic Similarity	—Unverified	0
A Study of Metrics of Distance and Correlation Between Ranked Lists for Compositionality Detection	Mar 10, 2017	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
A Novel Comprehensive Approach for Estimating Concept Semantic Similarity in WordNet	Mar 6, 2017	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
A Knowledge-Based Approach to Word Sense Disambiguation by distributional selection and semantic features	Feb 27, 2017	Combinatorial OptimizationInformation Retrieval	—Unverified	0
Just an Update on PMING Distance for Web-based Semantic Similarity in Artificial Intelligence and Data Mining	Jan 9, 2017	Information RetrievalRetrieval	—Unverified	0
Dense Associative Memory is Robust to Adversarial Inputs	Jan 4, 2017	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Semantic Specialization of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints	Jan 1, 2017	Dialogue State TrackingRepresentation Learning	—Unverified	0
Web-based Semantic Similarity for Emotion Recognition in Web Objects	Dec 17, 2016	Emotion RecognitionSemantic Similarity	—Unverified	0
Multilingual Word Embeddings using Multigraphs	Dec 14, 2016	Machine TranslationMultilingual Word Embeddings	—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