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 1391–1400 of 1564 papers

Title	Date	Tasks	Status
Web-based Semantic Similarity for Emotion Recognition in Web Objects	Dec 17, 2016	Emotion RecognitionSemantic Similarity	—Unverified
Multilingual Word Embeddings using Multigraphs	Dec 14, 2016	Machine TranslationMultilingual Word Embeddings	—Unverified
Cross-Lingual Predicate Mapping Between Linked Data Ontologies	Dec 6, 2016	Semantic SimilaritySemantic Textual Similarity	—Unverified
Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF	Dec 1, 2016	General ClassificationSemantic Similarity	—Unverified
Task-Oriented Intrinsic Evaluation of Semantic Textual Similarity	Dec 1, 2016	Question AnsweringSemantic Similarity	—Unverified
Reddit Temporal N-gram Corpus and its Applications on Paraphrase and Semantic Similarity in Social Media using a Topic-based Latent Semantic Analysis	Dec 1, 2016	AttributeInformation Retrieval	—Unverified
A Paraphrase and Semantic Similarity Detection System for User Generated Short-Text Content on Microblogs	Dec 1, 2016	Semantic SimilaritySemantic Textual Similarity	—Unverified
Convolution-Enhanced Bilingual Recursive Neural Network for Bilingual Semantic Modeling	Dec 1, 2016	Machine TranslationSemantic Similarity	—Unverified
CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings	Dec 1, 2016	Binary ClassificationGeneral Classification	—Unverified
Book Reviews: Semantic Similarity from Natural Language and Ontology Analysis by S\'ebastien Harispe, Sylvie Ranwez, Stefan Janaqi, and Jacky Montmain	Dec 1, 2016	Information RetrievalSemantic Similarity	—Unverified

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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