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 121–130 of 160 papers

TitleStatusHype
Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture—0
Aspect-Based Relational Sentiment Analysis Using a Stacked Neural Network ArchitectureCode0
Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets—0
Deep Multi-Task Learning for Aspect Term Extraction with Memory Interaction—0
Towards an integrated pipeline for aspect-based sentiment analysis in various domains—0
Unsupervised Aspect Term Extraction with B-LSTM \& CRF using Automatically Labelled Datasets—0
Modeling the dynamics of domain specific terminology in diachronic corpora—0
Document Retrieval for Large Scale Content Analysis using Contextualized Dictionaries—0
Annotation of negation in the IULA Spanish Clinical Record Corpus—0
Local-Global Vectors to Improve Unigram Terminology Extraction—0
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All datasetsAWARESemEval 2014 Task 4 Laptop

Benchmark Results

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