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 11–20 of 160 papers

TitleStatusHype
Unsupervised Technical Domain Terms Extraction using Term ExtractorCode1
GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment AnalysisCode1
Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment AnalysisCode1
A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment AnalysisCode1
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment AnalysisCode1
A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and DomainsCode1
SemRe-Rank: Improving Automatic Term Extraction By Incorporating Semantic Relatedness With Personalised PageRankCode1
AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis—0
Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval—0
Terminators: Terms of Service Parsing and Auditing Agents—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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