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 141–150 of 160 papers

TitleStatusHype
A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment AnalysisCode0
Aspect-Based Relational Sentiment Analysis Using a Stacked Neural Network ArchitectureCode0
ConQueR: Contextualized Query Reduction using Search LogsCode0
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment AnalysisCode0
Bridge-Based Active Domain Adaptation for Aspect Term ExtractionCode0
Extracting Mathematical Concepts with Large Language ModelsCode0
Towards Learning Terminological Concept Systems from Multilingual Natural Language TextCode0
Feature-Less End-to-End Nested Term ExtractionCode0
Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words ExtractionCode0
preon: Fast and accurate entity normalization for drug names and cancer types in precision oncologyCode0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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