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 1301–1350 of 1564 papers

TitleStatusHype
Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor—0
Dense Associative Memory is Robust to Adversarial Inputs—0
Deploying Deep Ranking Models for Search Verticals—0
DeSpin: a prototype system for detecting spin in biomedical publications—0
Detecting Backdoor Attacks via Similarity in Semantic Communication Systems—0
Detecting Collocations Similarity via Logical-Linguistic Model—0
Detecting Paraphrases of Standard Clause Titles in Insurance Contracts—0
Detecting Redundant Health Survey Questions Using Language-agnostic BERT Sentence Embedding (LaBSE)—0
Detecting Singleton Review Spammers Using Semantic Similarity—0
DialoGPS: Dialogue Path Sampling in Continuous Semantic Space for Data Augmentation in Multi-Turn Conversations—0
Differentially Private Adversarial Robustness Through Randomized Perturbations—0
Direct Network Transfer: Transfer Learning of Sentence Embeddings for Semantic Similarity—0
Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings—0
Discovering Knowledge Deficiencies of Language Models on Massive Knowledge Base—0
Discovery of Shared Semantic Spaces for Multi-Scene Video Query and Summarization—0
Discriminative Supervised Subspace Learning for Cross-modal Retrieval—0
Dissociating Semantic and Phonemic Search Strategies in the Phonemic Verbal Fluency Task in early Dementia—0
Distractor Generation for Chinese Fill-in-the-blank Items—0
Distributional Measures of Semantic Distance: A Survey—0
DIT: Summarisation and Semantic Expansion in Evaluating Semantic Similarity—0
DOCK: Detecting Objects by transferring Common-sense Knowledge—0
Do Cross Modal Systems Leverage Semantic Relationships?—0
Doctoral Advisor or Medical Condition: Towards Entity-specific Rankings of Knowledge Base Properties [Extended Version]—0
Document Valuation in LLM Summaries: A Cluster Shapley Approach—0
Do LLMs Exhibit Human-Like Reasoning? Evaluating Theory of Mind in LLMs for Open-Ended Responses—0
Domain-Relevant Embeddings for Medical Question Similarity—0
Comparative analysis of word embeddings in assessing semantic similarity of complex sentences—0
Don't Blame Distributional Semantics if it can't do Entailment—0
Do Smaller Language Models Answer Contextualised Questions Through Memorisation Or Generalisation?—0
DTAFA: Decoupled Training Architecture for Efficient FAQ Retrieval—0
DTSim at SemEval-2016 Task 1: Semantic Similarity Model Including Multi-Level Alignment and Vector-Based Compositional Semantics—0
DT\_Team at SemEval-2017 Task 1: Semantic Similarity Using Alignments, Sentence-Level Embeddings and Gaussian Mixture Model Output—0
Dual-Stream Knowledge-Preserving Hashing for Unsupervised Video Retrieval—0
Duluth : Measuring Cross-Level Semantic Similarity with First and Second Order Dictionary Overlaps—0
Duplicate Detection in a Knowledge Base with PIKA—0
Dynamic Few-Shot Learning for Knowledge Graph Question Answering—0
Ebiquity: Paraphrase and Semantic Similarity in Twitter using Skipgrams—0
ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity—0
ECNU: Leveraging on Ensemble of Heterogeneous Features and Information Enrichment for Cross Level Semantic Similarity Estimation—0
EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association—0
EDS-MEMBED: Multi-sense embeddings based on enhanced distributional semantic structures via a graph walk over word senses—0
Early Discovery of Emerging Entities in Persian Twitter with Semantic Similarity—0
Effective Parallel Corpus Mining using Bilingual Sentence Embeddings—0
Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs—0
Efficient Audio Captioning Transformer with Patchout and Text Guidance—0
Efficient comparison of sentence embeddings—0
Efficient data selection employing Semantic Similarity-based Graph Structures for model training—0
Efficient Domain Adaptation of Sentence Embeddings Using Adapters—0
Efficient Embedding of Semantic Similarity in Control Policies via Entangled Bisimulation—0
Efficient learning of neighbor representations for boundary trees and forests—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