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 81–90 of 160 papers

TitleStatusHype
The Recent Advances in Automatic Term Extraction: A survey—0
Towards an integrated pipeline for aspect-based sentiment analysis in various domains—0
Towards a One-stop Solution to Both Aspect Extraction and Sentiment Analysis Tasks with Neural Multi-task Learning—0
Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets—0
Unsupervised Aspect Term Extraction with B-LSTM \& CRF using Automatically Labelled Datasets—0
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
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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