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 401–450 of 1564 papers

TitleStatusHype
DialoGPS: Dialogue Path Sampling in Continuous Semantic Space for Data Augmentation in Multi-Turn Conversations—0
Balanced Multi-Factor In-Context Learning for Multilingual Large Language Models—0
Differentially Private Adversarial Robustness Through Randomized Perturbations—0
Cross-modal Subspace Learning via Kernel Correlation Maximization and Discriminative Structure Preserving—0
Cross-modal Deep Metric Learning with Multi-task Regularization—0
Author-aware Aspect Topic Sentiment Model to Retrieve Supporting Opinions from Reviews—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
Cross-media Similarity Metric Learning with Unified Deep Networks—0
Cross-Media Scientific Research Achievements Retrieval Based on Deep Language Model—0
A Modified Word Saliency-Based Adversarial Attack on Text Classification Models—0
Dissociating Semantic and Phonemic Search Strategies in the Phonemic Verbal Fluency Task in early Dementia—0
A density compensation-based path computing model for measuring semantic similarity—0
Estimating Text Similarity based on Semantic Concept Embeddings—0
Distractor Generation for Chinese Fill-in-the-blank Items—0
Evaluating distributed word representations for capturing semantics of biomedical concepts—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
Beyond [CLS] through Ranking by Generation—0
Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples—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
Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation—0
Cross-Lingual Syntactically Informed Distributed Word Representations—0
A Mixed Learning Objective for Neural Machine Translation—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
Cross-lingual Sentence Embedding using Multi-Task Learning—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
Cross-Lingual Semantic Similarity of Words as the Similarity of Their Semantic Word Responses—0
Augmenting Modelers with Semantic Autocompletion of Processes—0
A Method for Estimating the Proximity of Vector Representation Groups in Multidimensional Space. On the Example of the Paraphrase Task—0
Ebiquity: Paraphrase and Semantic Similarity in Twitter using Skipgrams—0
Cross-Lingual Predicate Mapping Between Linked Data Ontologies—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
Cross-lingual paraphrase identification—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
Cross-lingual Alignment of Knowledge Graph Triples with Sentences—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