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 31–40 of 160 papers

TitleStatusHype
A Deep Convolutional Neural Network-based Model for Aspect and Polarity Classification in Hausa Movie Reviews—0
Building and Applying Profiles Through Term Extraction—0
Aspect and Opinion Term Extraction Using Graph Attention Network—0
Aspect and Opinion Term Extraction for Hotel Reviews using Transfer Learning and Auxiliary Labels—0
A Machine Learning Approach to Automatic Term Extraction using a Rich Feature Set—0
Categorisation of Bulgarian Legislative Documents—0
BAN-ABSA: An Aspect-Based Sentiment Analysis dataset for Bengali and it's baseline evaluation—0
A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis—0
Bootstrapping Term Extractors for Multiple Languages—0
AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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