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 1001–1025 of 1564 papers

TitleStatusHype
Improving Semantic Similarity Calculation of Japanese Text for MT Evaluation—0
Improving Semantic Similarity Measure Within a Recommender System Based-on RDF Graphs—0
Improving Text Semantic Similarity Modeling through a 3D Siamese Network—0
Improving Trace Link Recommendation by Using Non-Isotropic Distances and Combinations—0
In-Context Experience Replay Facilitates Safety Red-Teaming of Text-to-Image Diffusion Models—0
In-Context Learning for Few-Shot Nested Named Entity Recognition—0
Incorporating Contextual and Syntactic Structures Improves Semantic Similarity Modeling—0
Incorporating Word Embeddings into Open Directory Project based Large-scale Classification—0
IndoWordNet::Similarity- Computing Semantic Similarity and Relatedness using IndoWordNet—0
Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation—0
Inpatient2Vec: Medical Representation Learning for Inpatients—0
InsertRank: LLMs can reason over BM25 scores to Improve Listwise Reranking—0
InsightNet: Structured Insight Mining from Customer Feedback—0
Instance-aware Image and Sentence Matching with Selective Multimodal LSTM—0
Instance Cross Entropy for Deep Metric Learning—0
Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF—0
Interactive Variance Attention based Online Spoiler Detection for Time-Sync Comments—0
Internal Wasserstein Distance for Adversarial Attack and Defense—0
Interpretable Company Similarity with Sparse Autoencoders—0
Intrinsic vs. Extrinsic Evaluation of Czech Sentence Embeddings: Semantic Relevance Doesn't Help with MT Evaluation—0
Introducing two Vietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness—0
Investigating Antigram Behaviour using Distributional Semantics—0
Investigating Continual Pretraining in Large Language Models: Insights and Implications—0
Investigating Entropy for Extractive Document Summarization—0
Investigating Large Language Models for Financial Causality Detection in Multilingual Setup—0
Show:102550
← PrevPage 41 of 63Next →
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