dc.contributor.author |
Sharma, Yashvardhan |
|
dc.date.accessioned |
2023-01-02T10:48:12Z |
|
dc.date.available |
2023-01-02T10:48:12Z |
|
dc.date.issued |
2017 |
|
dc.identifier.uri |
https://ceur-ws.org/Vol-2036/T4-7.pdf |
|
dc.identifier.uri |
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8224 |
|
dc.description.abstract |
The task of Native Language Identification involves identifying the
prior or first learnt language of a user based on his writing technique
and/or analysis of speech and phonetics in second language.
There is a surplus of such data present on social media sites and organised
dataset from bodies like Educational Testing Service(ETS),
which can be exploited to develop language learning systems and
forensic linguistics. In this paper we propose a deep neural network
for this task using hierarchical paragraph encoder with attention
mechanism to identify relevant features over tendencies and errors
a user makes with second language for the INLI task in FIRE 2017.
The task involves six Indian languages as prior/native set and english
as the second language which has been collected from user's
social media account. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
CEUR |
en_US |
dc.subject |
Computer Science |
en_US |
dc.subject |
Native Language Identification |
en_US |
dc.subject |
Natural Language Processing |
en_US |
dc.subject |
Deep Learning |
en_US |
dc.subject |
Neural networks |
en_US |
dc.title |
Bits_Pilani@INLI-FIRE-2017:Indian Native Language Identification using Deep Learning |
en_US |
dc.type |
Article |
en_US |