SOTAVerified

Term Extraction

Term Extraction, or Automated Term Extraction (ATE), is about extraction domain-specific terms from natural language text. For example, the sentence “We meta-analyzed mortality using random-effect models” contains the domain-specific single-word terms "meta-analyzed", "mortality" and the multi-word term "random-effect models".

Papers

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Showing 71–80 of 160 papers

TitleStatusHype
Large Language Model-based Role-Playing for Personalized Medical Jargon Extraction—0
Learning Joint Embedding with Modality Alignments for Cross-Modal Retrieval of Recipes and Food Images—0
Local-Global Vectors to Improve Unigram Terminology Extraction—0
Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction—0
Medical Term Extraction in an Arabic Medical Corpus—0
Modeling the dynamics of domain specific terminology in diachronic corpora—0
MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews—0
Multilingualization of Medical Terminology: Semantic and Structural Embedding Approaches—0
Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection—0
Octet: Online Catalog Taxonomy Enrichment with Self-Supervision—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BaselineF1-Score0.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Seq2Seq4ATEF1-Score0.8—Unverified