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 1120 of 160 papers

TitleStatusHype
MvP: Multi-view Prompting Improves Aspect Sentiment Tuple PredictionCode1
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment AnalysisCode1
SemRe-Rank: Improving Automatic Term Extraction By Incorporating Semantic Relatedness With Personalised PageRankCode1
Unsupervised Technical Domain Terms Extraction using Term ExtractorCode1
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment AnalysisCode1
Inheriting the Wisdom of Predecessors: A Multiplex Cascade Framework for Unified Aspect-based Sentiment AnalysisCode1
A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and DomainsCode1
Deep Learning Brasil at ABSAPT 2022: Portuguese Transformer Ensemble ApproachesCode0
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment AnalysisCode0
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-ExtractionCode0
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Benchmark Results

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