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 951–975 of 1564 papers

TitleStatusHype
Evaluating Contrastive Models for Instance-based Image Retrieval—0
Embedding Semantic Hierarchy in Discrete Optimal Transport for Risk Minimization—0
CAT: Cross-Attention Transformer for One-Shot Object Detection—0
More Than Meets The Eye: Semi-supervised Learning Under Non-IID DataCode0
Frequency-based Distortions in Contextualized Word Embeddings—0
Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase—0
ALF -- A Fitness-Based Artificial Life Form for Evolving Large-Scale Neural Networks—0
Adaptive Logit Adjustment Loss for Long-Tailed Visual Recognition—0
Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification—0
Automating Transfer Credit Assessment in Student Mobility -- A Natural Language Processing-based Approach—0
Improving Cross-Lingual Transfer for Event Argument Extraction with Language-Universal Sentence Structures—0
Semantic Oppositeness Assisted Deep Contextual Modeling for Automatic Rumor Detection in Social Networks—0
Character Set Construction for Chinese Language Learning—0
A Multi-View Framework to Detect Redundant Activity Labels for More Representative Event Logs in Process Mining—0
Learning from Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentation—0
Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning—0
An Unsupervised Sampling Approach for Image-Sentence Matching Using Document-Level Structural Information—0
Paint by Word—0
Optimal Embedding Calibration for Symbolic Music Similarity—0
Internal Wasserstein Distance for Adversarial Attack and Defense—0
ReinforceBug: A Framework to Generate Adversarial Textual Examples—0
A Discriminative Vectorial Framework for Multi-modal Feature Representation—0
Automatic Difficulty Classification of Arabic Sentences—0
Semantic Data Set Construction from Human Clustering and Spatial Arrangement—0
Unbiased Sentence Encoder For Large-Scale Multi-lingual Search Engines—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 cased (SciVocab, fine-tuned on "Annotated corpus for semantic similarity of clinical trial outcomes, original corpus")F189.3—Unverified
3SciBERT uncased (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