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 91–100 of 160 papers

TitleStatusHype
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment AnalysisCode0
A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment AnalysisCode1
Distributional semantic modeling: a revised technique to train term/word vector space models applying the ontology-related approach—0
Aspect Term Extraction using Graph-based Semi-Supervised Learning—0
A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term ExtractionCode2
My Approach = Your Apparatus? Entropy-Based Topic Modeling on Multiple Domain-Specific Text CollectionsCode0
PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction—0
Aspect and Opinion Term Extraction for Hotel Reviews using Transfer Learning and Auxiliary Labels—0
Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings—0
Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction: HAMLET vs TermoStat—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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