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 101–110 of 160 papers

TitleStatusHype
Terminators: Terms of Service Parsing and Auditing Agents—0
Terminology extraction using co-occurrence patterns as predictors of semantic relevance—0
TermPortal: A Workbench for Automatic Term Extraction from Icelandic Texts—0
The ACL RD-TEC 2.0: A Language Resource for Evaluating Term Extraction and Entity Recognition Methods—0
The Quaero Evaluation Initiative on Term Extraction—0
The Recent Advances in Automatic Term Extraction: A survey—0
Towards an integrated pipeline for aspect-based sentiment analysis in various domains—0
Towards a One-stop Solution to Both Aspect Extraction and Sentiment Analysis Tasks with Neural Multi-task Learning—0
Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets—0
Unsupervised Aspect Term Extraction with B-LSTM \& CRF using Automatically Labelled Datasets—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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