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 526–550 of 1564 papers

TitleStatusHype
Contrastive Word Embedding Learning for Neural Machine Translation—0
A Massive Scale Semantic Similarity Dataset of Historical English—0
Contrastive Semantic Similarity Learning for Image Captioning Evaluation with Intrinsic Auto-encoder—0
Contrastive Learning Subspace for Text Clustering—0
A text autoencoder from transformer for fast encoding language representation—0
Friend Recommendation based on Hashtags Analysis—0
Contrastive Learning of Sentence Representations—0
ATEB: Evaluating and Improving Advanced NLP Tasks for Text Embedding Models—0
Addressing Mistake Severity in Neural Networks with Semantic Knowledge—0
ConTFV: A Contrastive Learning Framework for Table-based Fact Verification—0
Contextualizing the Limits of Model & Evaluation Dataset Curation on Semantic Similarity Classification Tasks—0
ALOHa: A New Measure for Hallucination in Captioning Models—0
From Disjoint Sets to Parallel Data to Train Seq2Seq Models for Sentiment Transfer—0
Generalised Differential Privacy for Text Document Processing—0
GrFormer: A Novel Transformer on Grassmann Manifold for Infrared and Visible Image Fusion—0
Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AI—0
Context Effects on Human Judgments of Similarity—0
A Survey on Text Simplification—0
Context-Dependent Translation Selection Using Convolutional Neural Network—0
A Study of Metrics of Distance and Correlation Between Ranked Lists for Compositionality Detection—0
Context-Aware Human Behavior Prediction Using Multimodal Large Language Models: Challenges and Insights—0
Assistive Completion of Agrammatic Aphasic Sentences: A Transfer Learning Approach using Neurolinguistics-based Synthetic Dataset—0
Constructing a Norm for Children's Scientific Drawing: Distribution Features Based on Semantic Similarity of Large Language Models—0
Aligning Sentences from Standard Wikipedia to Simple Wikipedia—0
Addressing Cross-Lingual Word Sense Disambiguation on Low-Density Languages: Application to Persian—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