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
Distributional semantic modeling: a revised technique to train term/word vector space models applying the ontology-related approach—0
DLIREC: Aspect Term Extraction and Term Polarity Classification System—0
Aspect based Sentiment Analysis in Hindi: Resource Creation and Evaluation—0
Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction: HAMLET vs TermoStat—0
AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis—0
Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings—0
Comparative Study of Domain Driven Terms Extraction Using Large Language Models—0
CoastTerm: a Corpus for Multidisciplinary Term Extraction in Coastal Scientific Literature—0
Categorisation of Bulgarian Legislative Documents—0
Building and Applying Profiles Through 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