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

Showing 111120 of 160 papers

TitleStatusHype
Language-Agnostic Model for Aspect-Based Sentiment Analysis0
Towards a One-stop Solution to Both Aspect Extraction and Sentiment Analysis Tasks with Neural Multi-task Learning0
Global Inference for Aspect and Opinion Terms Co-Extraction Based on Multi-Task Neural Networks0
Aspect Sentiment Model for Micro ReviewsCode0
Improving Aspect Term Extraction with Bidirectional Dependency Tree RepresentationCode0
Aspect Term Extraction with History Attention and Selective TransformationCode0
A Gold Standard for Multilingual Automatic Term Extraction from Comparable Corpora: Term Structure and Translation Equivalents0
SemRe-Rank: Improving Automatic Term Extraction By Incorporating Semantic Relatedness With Personalised PageRankCode1
MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews0
Dataset Construction via Attention for Aspect Term Extraction with Distant Supervision0
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Benchmark Results

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