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

Showing 131140 of 160 papers

TitleStatusHype
Aspect and Opinion Term Extraction for Hotel Reviews using Transfer Learning and Auxiliary Labels0
Aspect and Opinion Term Extraction Using Graph Attention Network0
Aspect based Sentiment Analysis in Hindi: Resource Creation and Evaluation0
Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture0
Aspect Term Extraction for Sentiment Analysis: New Datasets, New Evaluation Measures and an Improved Unsupervised Method0
Aspect Term Extraction using Graph-based Semi-Supervised Learning0
A Study of Association Measures and their Combination for Arabic MWT Extraction0
A Term Extraction Approach to Survey Analysis in Health Care0
ATESA-BÆRT: A Heterogeneous Ensemble Learning Model for Aspect-Based Sentiment Analysis0
Automatic Extraction of Agriculture Terms from Domain Text: A Survey of Tools and Techniques0
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Benchmark Results

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