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 11–20 of 160 papers

TitleStatusHype
A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment AnalysisCode1
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment AnalysisCode1
Large language models for aspect-based sentiment analysisCode1
Unsupervised Technical Domain Terms Extraction using Term ExtractorCode1
Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment AnalysisCode1
CheckEmbed: Effective Verification of LLM Solutions to Open-Ended TasksCode1
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment AnalysisCode1
Annotation of negation in the IULA Spanish Clinical Record Corpus—0
A Gold Standard for Multilingual Automatic Term Extraction from Comparable Corpora: Term Structure and Translation Equivalents—0
AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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