@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}
}

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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  - 

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