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 1301–1325 of 1564 papers

Title	Date	Tasks	Status
Calculating Semantic Similarity between Academic Articles using Topic Event and Ontology	Nov 30, 2017	Articlesdocument understanding	—Unverified
Addressing Cross-Lingual Word Sense Disambiguation on Low-Density Languages: Application to Persian	Nov 16, 2017	Semantic SimilaritySemantic Textual Similarity	—Unverified
The Challenge of Composition in Distributional and Formal Semantics	Nov 1, 2017	Natural Language InferenceSemantic Composition	—Unverified
SSAS: Semantic Similarity for Abstractive Summarization	Nov 1, 2017	Abstractive Text SummarizationNatural Language Inference	—Unverified
Semantic Similarity Analysis for Paraphrase Identification in Arabic Texts	Nov 1, 2017	Paraphrase IdentificationSemantic Similarity	—Unverified
Testing the limits of unsupervised learning for semantic similarity	Oct 23, 2017	Semantic SimilaritySemantic Textual Similarity	—Unverified
A Semantically Motivated Approach to Compute ROUGE Scores	Oct 20, 2017	Semantic SimilaritySemantic Textual Similarity	—Unverified
Specialising Word Vectors for Lexical Entailment	Oct 17, 2017	Lexical EntailmentRelation	CodeCode Available
Convolutional neural networks for structured omics: OmicsCNN and the OmicsConv layer	Oct 16, 2017	Deep LearningSemantic Similarity	—Unverified
A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification	Oct 6, 2017	DecoderSemantic Similarity	CodeCode Available
An\'alise de Medidas de Similaridade Sem\^antica na Tarefa de Reconhecimento de Implica \~ao Textual (Analysis of Semantic Similarity Measures in the Recognition of Textual Entailment Task)[In Portuguese]	Oct 1, 2017	Natural Language InferenceSemantic Similarity	—Unverified
An enhanced method to compute the similarity between concepts of ontology	Sep 26, 2017	Information RetrievalRetrieval	—Unverified
Retrofitting Concept Vector Representations of Medical Concepts to Improve Estimates of Semantic Similarity and Relatedness	Sep 21, 2017	Semantic SimilaritySemantic Textual Similarity	—Unverified
Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties [Extended Version]	Sep 20, 2017	Semantic SimilaritySemantic Textual Similarity	—Unverified
Methodology and Results for the Competition on Semantic Similarity Evaluation and Entailment Recognition for PROPOR 2016	Sep 19, 2017	Semantic SimilaritySemantic Textual Similarity	—Unverified
Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words	Sep 19, 2017	General ClassificationRelation Classification	CodeCode Available
Parameter Transfer across Domains for Word Sense Disambiguation	Sep 1, 2017	Lexical SimplificationMachine Translation	—Unverified
Czech Dataset for Semantic Similarity and Relatedness	Sep 1, 2017	Semantic SimilaritySemantic Textual Similarity	—Unverified
Distractor Generation for Chinese Fill-in-the-blank Items	Sep 1, 2017	Distractor GenerationSemantic Similarity	—Unverified
Identifying Cognate Sets Across Dictionaries of Related Languages	Sep 1, 2017	Semantic SimilaritySemantic Textual Similarity	CodeCode Available
Author-aware Aspect Topic Sentiment Model to Retrieve Supporting Opinions from Reviews	Sep 1, 2017	Semantic SimilaritySemantic Textual Similarity	—Unverified
The strange geometry of skip-gram with negative sampling	Sep 1, 2017	Semantic SimilaritySemantic Textual Similarity	—Unverified
Variational Inference for Logical Inference	Sep 1, 2017	Semantic SimilaritySemantic Textual Similarity	—Unverified
Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation	Sep 1, 2017	DecoderDocument Summarization	—Unverified
The Effect of Negative Sampling Strategy on Capturing Semantic Similarity in Document Embeddings	Sep 1, 2017	Answer SelectionCommunity Question Answering	—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