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
Cross-lingual and Cross-domain Transfer Learning for Automatic Term Extraction from Low Resource Data—0
D-Terminer: Online Demo for Monolingual and Bilingual Automatic Term Extraction—0
A Dataset for Term Extraction in Hindi—0
Terminology extraction using co-occurrence patterns as predictors of semantic relevance—0
Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction—0
A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment AnalysisCode0
AWARE: Aspect-Based Sentiment Analysis Dataset of Apps Reviews for Requirements Elicitation—0
Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis ModelCode0
Improving Aspect Extraction based on Rules through Deep Syntax-Semantics CommunicationCode0
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