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 121–130 of 160 papers

TitleStatusHype
MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews—0
Multilingualization of Medical Terminology: Semantic and Structural Embedding Approaches—0
Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection—0
Octet: Online Catalog Taxonomy Enrichment with Self-Supervision—0
Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction—0
Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction—0
PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction—0
Predicting Degrees of Technicality in Automatic Terminology Extraction—0
Progressive Self-Training with Discriminator for Aspect Term Extraction—0
PTA: Enhancing Multimodal Sentiment Analysis through Pipelined Prediction and Translation-based Alignment—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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