Paper Info
Title
Using LSTM Encoder-Decoder for Rhetorical Structure Prediction
Abstract
The importance of identifying rhetorical categories in texts has been widely acknowledged in the literature, since information regarding text organization or structure can be applied in a variety of scenarios, including genre-specific writing support and evaluation, both manually and automatically. In this paper we present a Long Short-Term Memory (LSTM) encoder-decoder classifier for scientific abstracts. As a large corpus of annotated abstracts was required to train our classifier, we built a corpus using abstracts extracted from PUBMED/MEDLINE. Using the proposed classifier we achieved approximately 3% improvement in per-abstract accuracy over the baselines and 1% improvement for both per-sentence accuracy and f1-score.
Year
DOI
Venue
2018
10.1109/BRACIS.2018.00055
2018 7th Brazilian Conference on Intelligent Systems (BRACIS)
Keywords
Field
DocType
rhetorical structure classification,recurrent neural networks,neural networks,deep learning
Encoder decoder,Task analysis,Computer science,Support vector machine,Rhetorical question,Recurrent neural network,Natural language processing,Artificial intelligence,Decoding methods,Classifier (linguistics),MEDLINE
Conference
ISBN
Citations 
PageRank 
978-1-5386-8024-7
0
0.34
References 
Authors
4
2
Name
Order
Citations
PageRank
Gustavo Bennemann de Moura100.34
Valéria Delisandra Feltrim2147.71