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 31–40 of 160 papers

TitleStatusHype
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
Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge GraphsCode4
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
PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment AnalysisCode3
Inheriting the Wisdom of Predecessors: A Multiplex Cascade Framework for Unified Aspect-based Sentiment AnalysisCode1
Generating Complement Data for Aspect Term Extraction with GPT-2—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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