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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 41–50 of 160 papers

TitleStatusHype
ATESA-BÆRT: A Heterogeneous Ensemble Learning Model for Aspect-Based Sentiment Analysis—0
ConQueR: Contextualized Query Reduction using Search LogsCode0
Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis—0
The Recent Advances in Automatic Term Extraction: A survey—0
Ensembling Transformers for Cross-domain Automatic Term Extraction—0
Unsupervised Term Extraction for Highly Technical Domains—0
Unsupervised Data Augmentation for Aspect Based Sentiment Analysis—0
Dialogue Term Extraction using Transfer Learning and Topological Data Analysis—0
Court Judgement Labeling Using Topic Modeling and Syntactic ParsingCode0
Generating Complement Data for Aspect Term Extraction with GPT-2—0
Show:102550
← PrevPage 5 of 16Next →
All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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