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 101–110 of 160 papers

TitleStatusHype
Exploring Sequence-to-Sequence Learning in Aspect Term Extraction—0
Extracting domain-specific terms using contextual word embeddings—0
Featureless Domain-Specific Term Extraction with Minimal Labelled Data—0
Generating Complement Data for Aspect Term Extraction with GPT-2—0
Geo-located Aspect Based Sentiment Analysis (ABSA) for Crowdsourced Evaluation of Urban Environments—0
Global Inference for Aspect and Opinion Terms Co-Extraction Based on Multi-Task Neural Networks—0
Graph Based Automatic Domain Term Extraction—0
Hot Topics and Schisms in NLP: Community and Trend Analysis with Saffron on ACL and LREC Proceedings—0
Improving Patent Translation using Bilingual Term Extraction and Re-tokenization for Chinese--Japanese—0
Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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