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
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
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment AnalysisCode1
A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and DomainsCode1
KAS-term: Extracting Slovene Terms from Doctoral Theses via Supervised Machine Learning—0
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-ExtractionCode0
SURel: A Gold Standard for Incorporating Meaning Shifts into 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