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 61–70 of 160 papers

TitleStatusHype
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading ComprehensionCode0
Progressive Self-Training with Discriminator for Aspect Term Extraction—0
SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis—0
Towards Learning Terminological Concept Systems from Multilingual Natural Language TextCode0
Learning Joint Embedding with Modality Alignments for Cross-Modal Retrieval of Recipes and Food Images—0
Bridge-Based Active Domain Adaptation for Aspect Term ExtractionCode0
A Case Study in Bootstrapping Ontology Graphs from Textbooks—0
Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words ExtractionCode0
Question-Driven Span Labeling Model for Aspect–Opinion Pair Extraction—0
Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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