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 151–160 of 160 papers

TitleStatusHype
Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings—0
Computational Aspects of Frame-based Meaning Representation in Terminology—0
Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation—0
Creation of a bottom-up corpus-based ontology for Italian Linguistics—0
Cross-lingual and Cross-domain Transfer Learning for Automatic Term Extraction from Low Resource Data—0
Dataset Construction via Attention for Aspect Term Extraction with Distant Supervision—0
Dataset Creation and Evaluation of Aspect Based Sentiment Analysis in Telugu, a Low Resource Language—0
Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction—0
Developing an Arabic Infectious Disease Ontology to Include Non-Standard Terminology—0
Dialogue Term Extraction using Transfer Learning and Topological Data Analysis—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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