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 51–60 of 160 papers

TitleStatusHype
AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation—0
Creation of a bottom-up corpus-based ontology for Italian Linguistics—0
Automatic Term Recognition Needs Multiple Evidence—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
A Gold Standard for Multilingual Automatic Term Extraction from Comparable Corpora: Term Structure and Translation Equivalents—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
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