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 91–100 of 160 papers

TitleStatusHype
Rule-based Automatic Multi-word Term Extraction and Lemmatization—0
SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis—0
Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis—0
SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction—0
Technical Term Extraction Using Measures of Neology—0
Term-Based Extraction of Medical Information: Pre-Operative Patient Education Use Case—0
TermEval 2020: RACAI's automatic term extraction system—0
TermEval 2020: Shared Task on Automatic Term Extraction Using the Annotated Corpora for Term Extraction Research (ACTER) Dataset—0
TermEval 2020: TALN-LS2N System for Automatic Term Extraction—0
TermEval 2020: Using TSR Filtering Method to Improve Automatic Term Extraction—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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