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 11–20 of 160 papers

TitleStatusHype
CoastTerm: a Corpus for Multidisciplinary Term Extraction in Coastal Scientific Literature—0
CheckEmbed: Effective Verification of LLM Solutions to Open-Ended TasksCode1
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
Aspect and Opinion Term Extraction Using Graph Attention Network—0
Comparative Study of Domain Driven Terms Extraction Using Large Language Models—0
A Hybrid Approach To Aspect Based Sentiment Analysis Using Transfer Learning—0
preon: Fast and accurate entity normalization for drug names and cancer types in precision oncologyCode0
Exploiting Adaptive Contextual Masking for Aspect-Based Sentiment Analysis—0
Geo-located Aspect Based Sentiment Analysis (ABSA) for Crowdsourced Evaluation of Urban Environments—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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