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 41–50 of 160 papers

TitleStatusHype
Building and Applying Profiles Through Term Extraction—0
Categorisation of Bulgarian Legislative Documents—0
A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis—0
AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation—0
Comparative Study of Domain Driven Terms Extraction Using Large Language Models—0
Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings—0
Automatic Term Recognition Needs Multiple Evidence—0
Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation—0
Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture—0
A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment Analysis—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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