In this section
@inproceedings{Cocarascu:2017:v1/D17-1144,
author = {Cocarascu, O and Toni, F},
doi = {v1/D17-1144},
pages = {1374--1379},
publisher = {Association for Computational Linguistics},
title = {Identifying attack and support argumentative relations using deep learning},
url = {http://dx.doi.org/10.18653/v1/D17-1144},
year = {2017}
}
TY - CPAPER
AB - We propose a deep learning architecture tocapture argumentative relations ofattackandsupportfrom one piece of text to an-other, of the kind that naturally occur ina debate. The architecture uses two (uni-directional or bidirectional) Long Short-Term Memory networks and (trained ornon-trained) word embeddings, and al-lows to considerably improve upon exist-ing techniques that use syntactic featuresand supervised classifiers for the sameform of (relation-based) argument mining.
AU - Cocarascu,O
AU - Toni,F
DO - v1/D17-1144
EP - 1379
PB - Association for Computational Linguistics
PY - 2017///
SP - 1374
TI - Identifying attack and support argumentative relations using deep learning
UR - http://dx.doi.org/10.18653/v1/D17-1144
UR - https://aclanthology.info/papers/D17-1144/d17-1144
UR - http://hdl.handle.net/10044/1/56644
ER -
Artificial Intelligence Network
South Kensington Campus
Imperial College London
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