SOTAVerified

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 AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1076–1100 of 1564 papers

TitleStatusHype
Learning Modality-Invariant Representations for Speech and Images—0
Learning Multilingual Word Embeddings Using Image-Text Data—0
Learning Object Semantic Similarity with Self-Supervision—0
Learning Representations by Contrasting Clusters While Bootstrapping Instances—0
Learning Robust Named Entity Recognizers From Noisy Data With Retrieval Augmentation—0
Learning Semantic Similarities for Prototypical Classifiers—0
Learning Semantic Similarity for Very Short Texts—0
Learning semantic similarity in a continuous space—0
Learning Thematic Similarity Metric from Article Sections Using Triplet Networks—0
Learning to embed semantic similarity for joint image-text retrieval—0
Learning to hash with semantic similarity metrics and empirical KL divergence—0
Learning to Localize Actions in Instructional Videos with LLM-Based Multi-Pathway Text-Video Alignment—0
Learning to segment with limited annotations: Self-supervised pretraining with regression and contrastive loss in MRI—0
Learning Tversky Similarity—0
Legal document retrieval across languages: topic hierarchies based on synsets—0
Legal Document Retrieval using Document Vector Embeddings and Deep Learning—0
Legal-ES: A Set of Large Scale Resources for Spanish Legal Text Processing—0
LegalGuardian: A Privacy-Preserving Framework for Secure Integration of Large Language Models in Legal Practice—0
Let Sense Bags Do Talking: Cross Lingual Word Semantic Similarity for English and Hindi—0
LEURN: Learning Explainable Univariate Rules with Neural Networks—0
Leveraging LLMs to Enable Natural Language Search on Go-to-market Platforms—0
Leveraging Multi-lingual Positive Instances in Contrastive Learning to Improve Sentence Embedding—0
Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Models—0
Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation—0
Lexical semantics enhanced neural word embeddings—0
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All datasetsAnnotated corpus for semantic similarity of clinical trial outcomes (expanded corpus)Annotated corpus for semantic similarity of clinical trial outcomes (original corpus)SICKBIOSSESCHIP-STSClinicalSTSMedSTS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F193.38—Unverified
2SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F191.51—Unverified
3SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F190.69—Unverified
4BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F189.16—Unverified
5BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, expanded corpus")F189.12—Unverified
#ModelMetricClaimedVerifiedStatus
1BioBERT (pre-trained on PubMed abstracts + PMC, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F189.75—Unverified
2SciBERT uncased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F189.3—Unverified
3SciBERT cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F189.3—Unverified
4BERT-Base uncased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F186.8—Unverified
5BERT-Base cased (fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F184.21—Unverified
#ModelMetricClaimedVerifiedStatus
1Doc2VecCMSE0.31—Unverified
2LSTM (Tai et al., 2015)MSE0.28—Unverified
3Bidirectional LSTM (Tai et al., 2015)MSE0.27—Unverified
4combine-skip (Kiros et al., 2015)MSE0.27—Unverified
5Dependency Tree-LSTM (Tai et al., 2015)MSE0.25—Unverified
#ModelMetricClaimedVerifiedStatus
1BioLinkBERT (large)Pearson Correlation0.94—Unverified
2BioLinkBERT (base)Pearson Correlation0.93—Unverified
3NCBI_BERT(base) (P+M)Pearson Correlation0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1MacBERT-largeMacro F185.6—Unverified
#ModelMetricClaimedVerifiedStatus
1CharacterBERT (base, medical, ensemble)Pearson Correlation85.62—Unverified
#ModelMetricClaimedVerifiedStatus
1NCBI_BERT(base) (P+M)Pearson Correlation0.85—Unverified