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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–60 of 160 papers

TitleStatusHype
A Gold Standard for Multilingual Automatic Term Extraction from Comparable Corpora: Term Structure and Translation Equivalents—0
Cross-lingual and Cross-domain Transfer Learning for Automatic Term Extraction from Low Resource Data—0
Creation of a bottom-up corpus-based ontology for Italian Linguistics—0
Aspect Term Extraction for Sentiment Analysis: New Datasets, New Evaluation Measures and an Improved Unsupervised Method—0
Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture—0
Dataset Creation and Evaluation of Aspect Based Sentiment Analysis in Telugu, a Low Resource Language—0
Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation—0
Computational Aspects of Frame-based Meaning Representation in Terminology—0
Developing an Arabic Infectious Disease Ontology to Include Non-Standard Terminology—0
Aspect based Sentiment Analysis in Hindi: Resource Creation and Evaluation—0
Show:102550
← PrevPage 6 of 16Next →
All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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