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
Analysing the Impact of Supervised Machine Learning on Automatic Term Extraction: HAMLET vs TermoStat—0
Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture—0
Aspect based Sentiment Analysis in Hindi: Resource Creation and Evaluation—0
A Case Study in Bootstrapping Ontology Graphs from Textbooks—0
Automatic Term Extraction from Newspaper Corpora: Making the Most of Specificity and Common Features—0
Aspect Term Extraction for Sentiment Analysis: New Datasets, New Evaluation Measures and an Improved Unsupervised Method—0
Aspect Term Extraction using Graph-based Semi-Supervised Learning—0
A Gold Standard for Multilingual Automatic Term Extraction from Comparable Corpora: Term Structure and Translation Equivalents—0
A Study of Association Measures and their Combination for Arabic MWT Extraction—0
A Deep Convolutional Neural Network-based Model for Aspect and Polarity Classification in Hausa Movie Reviews—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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