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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 21–30 of 160 papers

Title	Date	Tasks	Status	Hype
A Self-enhancement Multitask Framework for Unsupervised Aspect Category Detection	Nov 16, 2023	Aspect Category DetectionRepresentation Learning	—Unverified	0
Deep Learning Brasil at ABSAPT 2022: Portuguese Transformer Ensemble Approaches	Nov 8, 2023	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	0
Indo LEGO-ABSA: A Multitask Generative Aspect Based Sentiment Analysis for Indonesian Language	Nov 3, 2023	Aspect-Based Sentiment AnalysisAspect Sentiment Triplet Extraction	CodeCode Available	0
Large language models for aspect-based sentiment analysis	Oct 27, 2023	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1
Extracting Mathematical Concepts with Large Language Models	Aug 29, 2023	Term Extraction	CodeCode Available	0
ATESA-BÆRT: A Heterogeneous Ensemble Learning Model for Aspect-Based Sentiment Analysis	Jul 29, 2023	Aspect-Based Sentiment AnalysisEnsemble Learning	—Unverified	0
ConQueR: Contextualized Query Reduction using Search Logs	May 22, 2023	Language ModelingLanguage Modelling	CodeCode Available	0
MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction	May 22, 2023	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1
Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis	Mar 1, 2023	Sentiment AnalysisTerm Extraction	—Unverified	0
InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis	Feb 16, 2023	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	CodeCode Available	1

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All datasets AWARE SemEval 2014 Task 4 Laptop

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Baseline	F1-Score	0.82	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Seq2Seq4ATE	F1-Score	0.8	—	Unverified