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 326–350 of 1564 papers

TitleStatusHype
An Attention-Based Word-Level Interaction Model: Relation Detection for Knowledge Base Question Answering—0
DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs—0
Automating Transfer Credit Assessment in Student Mobility -- A Natural Language Processing-based Approach—0
Automating the Compilation of Potential Core-Outcomes for Clinical Trials—0
Anaphora Resolution in Dialogue: Description of the DFKI-TalkingRobots System for the CODI-CRAC 2021 Shared-Task—0
Automatic Visual Theme Discovery from Joint Image and Text Corpora—0
Automatic Real-word Error Correction in Persian Text—0
Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach—0
Adversarial Contrastive Learning by Permuting Cluster Assignments—0
Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings—0
Automatic Difficulty Classification of Arabic Sentences—0
Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Events—0
Analysis of Word Embeddings Using Fuzzy Clustering—0
A Semi-supervised Multi-channel Graph Convolutional Network for Query Classification in E-commerce—0
Detecting Redundant Health Survey Questions Using Language-agnostic BERT Sentence Embedding (LaBSE)—0
Xu: An Automated Query Expansion and Optimization Tool—0
Analogies in Complex Verb Meaning Shifts: the Effect of Affect in Semantic Similarity Models—0
Automated Feedback Loops to Protect Text Simplification with Generative AI from Information Loss—0
An\'alise de Medidas de Similaridade Sem\^antica na Tarefa de Reconhecimento de Implica \~ao Textual (Analysis of Semantic Similarity Measures in the Recognition of Textual Entailment Task)[In Portuguese]—0
A Discriminative Vectorial Framework for Multi-modal Feature Representation—0
Detecting Singleton Review Spammers Using Semantic Similarity—0
Accelerating LLaMA Inference by Enabling Intermediate Layer Decoding via Instruction Tuning with LITE—0
Detecting Backdoor Attacks via Similarity in Semantic Communication Systems—0
Autoencoding Low-Resolution MRI for Semantically Smooth Interpolation of Anisotropic MRI—0
A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization—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