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 111–120 of 160 papers

TitleStatusHype
Unsupervised Data Augmentation for Aspect Based Sentiment Analysis—0
Unsupervised Neural Aspect Search with Related Terms Extraction—0
Unsupervised Term Extraction for Highly Technical Domains—0
Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction—0
Varying Vector Representations and Integrating Meaning Shifts into a PageRank Model for Automatic Term Extraction—0
A Case Study in Bootstrapping Ontology Graphs from Textbooks—0
What if we had no Wikipedia? Domain-independent Term Extraction from a Large News Corpus—0
Adapting and evaluating a generic term extraction tool—0
AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis—0
A Dataset for Term Extraction in Hindi—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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