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 131–140 of 160 papers

TitleStatusHype
Question-Driven Span Labeling Model for Aspect–Opinion Pair Extraction—0
Recognition of non-domain phrases in automatically extracted lists of terms—0
Requirement Tracing using Term Extraction—0
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-ExtractionCode0
Constituency Lattice Encoding for Aspect Term ExtractionCode0
Aspect Term Extraction with History Attention and Selective TransformationCode0
My Approach = Your Apparatus? Entropy-Based Topic Modeling on Multiple Domain-Specific Text CollectionsCode0
Neural Aspect and Opinion Term Extraction with Mined Rules as Weak SupervisionCode0
JATE 2.0: Java Automatic Term Extraction with Apache SolrCode0
Aspect-Based Argument MiningCode0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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