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 1226–1250 of 1564 papers

Title	Date	Tasks	Status
A mathematical theory of semantic development in deep neural networks	Oct 23, 2018	Semantic SimilaritySemantic Textual Similarity	CodeCode Available
Unsupervised Features Extraction for Binary Similarity Using Graph Embedding Neural Networks	Oct 23, 2018	Graph EmbeddingMalware Analysis	—Unverified
Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages	Oct 20, 2018	Machine TranslationSemantic Similarity	—Unverified
Controlling Length in Abstractive Summarization Using a Convolutional Neural Network	Oct 1, 2018	Abstractive Text SummarizationMachine Translation	CodeCode Available
Auto-Encoding Dictionary Definitions into Consistent Word Embeddings	Oct 1, 2018	Document ClassificationMachine Translation	CodeCode Available
Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations	Oct 1, 2018	DiversityGeneral Classification	—Unverified
A Graph-theoretic Summary Evaluation for ROUGE	Oct 1, 2018	Abstractive Text SummarizationSemantic Similarity	—Unverified
Hierarchy-based Image Embeddings for Semantic Image Retrieval	Sep 26, 2018	Few-Shot LearningImage Retrieval	CodeCode Available
The Wisdom of MaSSeS: Majority, Subjectivity, and Semantic Similarity in the Evaluation of VQA	Sep 12, 2018	Question AnsweringSemantic Similarity	—Unverified
Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints	Sep 4, 2018	Semantic SimilaritySemantic Textual Similarity	CodeCode Available
Hypernyms Through Intra-Article Organization in Wikipedia	Sep 3, 2018	ArticlesSemantic Similarity	—Unverified
Semi-Supervised Generative Adversarial Hashing for Image Retrieval	Sep 1, 2018	Deep HashingImage Retrieval	—Unverified
Xu: An Automated Query Expansion and Optimization Tool	Aug 28, 2018	ArticlesClustering	—Unverified
Learning Graph Embeddings from WordNet-based Similarity Measures	Aug 16, 2018	Graph EmbeddingSemantic Similarity	—Unverified
Cross-Lingual Cross-Platform Rumor Verification Pivoting on Multimedia Content	Aug 14, 2018	Semantic SimilaritySemantic Textual Similarity	CodeCode Available
A Joint Sequence Fusion Model for Video Question Answering and Retrieval	Aug 7, 2018	DecoderMultiple-choice	CodeCode Available
An Efficient Approach to Learning Chinese Judgment Document Similarity Based on Knowledge Summarization	Aug 6, 2018	Semantic SimilaritySemantic Textual Similarity	—Unverified
Neural Activation Semantic Models: Computational lexical semantic models of localized neural activations	Aug 1, 2018	Dimensionality ReductionSemantic Similarity	CodeCode Available
GenSense: A Generalized Sense Retrofitting Model	Aug 1, 2018	modelSemantic Similarity	CodeCode Available
Effective Parallel Corpus Mining using Bilingual Sentence Embeddings	Jul 31, 2018	Machine TranslationNMT	—Unverified
Clustering Prominent People and Organizations in Topic-Specific Text Corpora	Jul 27, 2018	ArticlesClustering	—Unverified
Large-Scale Multi-Domain Belief Tracking with Knowledge Sharing	Jul 17, 2018	Multi-domain Dialogue State TrackingSemantic Similarity	CodeCode Available
Pangloss: Fast Entity Linking in Noisy Text Environments	Jul 16, 2018	ArticlesEntity Disambiguation	—Unverified
Learning Distributional Token Representations from Visual Features	Jul 1, 2018	Machine TranslationNMT	—Unverified
Using pseudo-senses for improving the extraction of synonyms from word embeddings	Jul 1, 2018	Dimensionality ReductionSemantic 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 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