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 21–30 of 160 papers

TitleStatusHype
Methods for Recognizing Nested TermsCode0
Extracting domain-specific terms using contextual word embeddings—0
A Preliminary Survey of Semantic Descriptive Model for Images—0
Efficient Terminology Integration for LLM-based Translation in Specialized Domains—0
Instruct-DeBERTa: A Hybrid Approach for Aspect-based Sentiment Analysis on Textual Reviews—0
Large Language Model-based Role-Playing for Personalized Medical Jargon Extraction—0
Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction—0
CoastTerm: a Corpus for Multidisciplinary Term Extraction in Coastal Scientific Literature—0
A Deep Convolutional Neural Network-based Model for Aspect and Polarity Classification in Hausa Movie Reviews—0
PTA: Enhancing Multimodal Sentiment Analysis through Pipelined Prediction and Translation-based Alignment—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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