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 101–110 of 160 papers

TitleStatusHype
Aspect and Opinion Term Extraction for Hotel Reviews using Transfer Learning and Auxiliary Labels—0
Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings—0
Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction: HAMLET vs TermoStat—0
Term-Based Extraction of Medical Information: Pre-Operative Patient Education Use Case—0
Feature-Less End-to-End Nested Term ExtractionCode0
Evaluating Automatic Term Extraction Methods on Individual Documents—0
Neural Aspect and Opinion Term Extraction with Mined Rules as Weak SupervisionCode0
Exploring Sequence-to-Sequence Learning in Aspect Term Extraction—0
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-ExtractionCode0
KAS-term: Extracting Slovene Terms from Doctoral Theses via Supervised Machine Learning—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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