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 151–160 of 160 papers

TitleStatusHype
Aspect Term Extraction for Sentiment Analysis: New Datasets, New Evaluation Measures and an Improved Unsupervised Method—0
A Machine Learning Approach to Automatic Term Extraction using a Rich Feature Set—0
Iterative Chinese Bi-gram Term Extraction Using Machine-learning Classification Approach—0
Automatic Term Recognition Needs Multiple Evidence—0
Creation of a bottom-up corpus-based ontology for Italian Linguistics—0
The Quaero Evaluation Initiative on Term Extraction—0
Adapting and evaluating a generic term extraction tool—0
Expertise Mining for Enterprise Content Management—0
Medical Term Extraction in an Arabic Medical Corpus—0
GROBID: Combining Automatic Bibliographic Data Recognition and Term Extraction for Scholarship PublicationsCode0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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