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 1151–1175 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
Modeling performance differences on cognitive tests using LSTMs and skip-thought vectors trained on reported media consumption.	Jun 1, 2019	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
Classification of Semantic Paraphasias: Optimization of a Word Embedding Model	Jun 1, 2019	ClassificationGeneral Classification	—Unverified	0
Toward a Computational Multidimensional Lexical Similarity Measure for Modeling Word Association Tasks in Psycholinguistics	Jun 1, 2019	General ClassificationSemantic Similarity	—Unverified	0
A Semantic Cover Approach for Topic Modeling	Jun 1, 2019	Document ClassificationGeneral Classification	—Unverified	0
GSI-UPM at SemEval-2019 Task 5: Semantic Similarity and Word Embeddings for Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter	Jun 1, 2019	POSPosition	—Unverified	0
Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval	May 31, 2019	Entity EmbeddingsLanguage Modeling	CodeCode Available	0
Audio Caption in a Car Setting with a Sentence-Level Loss	May 31, 2019	Audio captioningDecoder	CodeCode Available	0
Learning Multilingual Word Embeddings Using Image-Text Data	May 29, 2019	Multilingual Word EmbeddingsSemantic Similarity	—Unverified	0
MultiWiki: Interlingual Text Passage Alignment in Wikipedia	May 21, 2019	ArticlesSemantic Similarity	—Unverified	0
Semantic flow in language networks	May 18, 2019	Community DetectionPhilosophy	CodeCode Available	0
Don't Blame Distributional Semantics if it can't do Entailment	May 17, 2019	Semantic SimilaritySemantic Textual Similarity	—Unverified	0
DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs	May 9, 2019	Deep HashingSemantic Similarity	—Unverified	0
Knowledgeable Storyteller: A Commonsense-Driven Generative Model for Visual Storytelling	May 4, 2019	AI AgentKnowledge Graphs	CodeCode Available	0
Detecting Collocations Similarity via Logical-Linguistic Model	May 1, 2019	modelSemantic Similarity	—Unverified	0
Detecting Paraphrases of Standard Clause Titles in Insurance Contracts	May 1, 2019	Semantic SimilaritySemantic Textual Similarity	—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