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

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Showing 701–725 of 1564 papers

TitleStatusHype
Text-Guided Unsupervised Latent Transformation for Multi-Attribute Image Manipulation—0
Modified Query Expansion Through Generative Adversarial Networks for Information Extraction in E-Commerce—0
Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval—0
Predicting the Score of Atomic Candidate OWL Class Axioms—0
MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders—0
MAViC: Multimodal Active Learning for Video Captioning—0
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning—0
Harnessing label semantics to extract higher performance under noisy label for Company to Industry matching—0
Multilingual Communication System with Deaf Individuals Utilizing Natural and Visual Languages—0
Soft Alignment Objectives for Robust Adaptation of Language GenerationCode0
Addressing Mistake Severity in Neural Networks with Semantic Knowledge—0
Embracing Ambiguity: Improving Similarity-oriented Tasks with Contextual Synonym Knowledge—0
Semantic Similarity-Based Clustering of Findings From Security Testing Tools—0
Investigating the Frequency Distortion of Word Embeddings and Its Impact on Bias MetricsCode0
Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems—0
Category-Adaptive Label Discovery and Noise Rejection for Multi-label Image Recognition with Partial Positive Labels—0
Semantic Similarity Models for Depression Severity Estimation—0
ALBERT with Knowledge Graph Encoder Utilizing Semantic Similarity for Commonsense Question Answering—0
Assistive Completion of Agrammatic Aphasic Sentences: A Transfer Learning Approach using Neurolinguistics-based Synthetic Dataset—0
Combining Contrastive Learning and Knowledge Graph Embeddings to develop medical word embeddings for the Italian language—0
Bridging Fairness and Environmental Sustainability in Natural Language Processing—0
Learning Semantic Textual Similarity via Topic-informed Discrete Latent VariablesCode0
H_eval: A new hybrid evaluation metric for automatic speech recognition tasks—0
Unsupervised Text Summarization of Long Documents using Dependency-based Noun Phrases and Contextual Order Arrangement—0
Sentiment Classification of Code-Switched Text using Pre-trained Multilingual Embeddings and Segmentation—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