dc.contributor.author |
Lourens, Roger L
|
|
dc.contributor.author |
Patra, A
|
|
dc.contributor.author |
Hassim, Luqmaan
|
|
dc.contributor.author |
Sima, Faheem
|
|
dc.contributor.author |
Moodley, Avashlin
|
|
dc.contributor.author |
Sharma, P
|
|
dc.date.accessioned |
2019-04-01T09:22:27Z |
|
dc.date.available |
2019-04-01T09:22:27Z |
|
dc.date.issued |
2018-12 |
|
dc.identifier.citation |
Lourens, R.L. et al. 2018. Water quality information dissemination at real-time in South Africa using language modelling. Machine Learning for the Developing World (ML4D) Workshop, part of the 23rd Conference on Neural Information Processing Systems (NIPS 2018), 8 December 2018, Palais des Congrès de Montréal, Montréal, Canada |
en_US |
dc.identifier.uri |
https://arxiv.org/abs/1812.09745
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/10893
|
|
dc.description |
Paper presented at the Machine Learning for the Developing World (ML4D) Workshop, part of the 23rd Conference on Neural Information Processing Systems (NIPS 2018), 8 December 2018, Palais des Congrès de Montréal, Montréal, Canada |
en_US |
dc.description.abstract |
We present a conversational model to apprise users with limited access to computational resources about water quality and real-time accessibility for a given location. We used natural language understanding through neural embedding driven approaches. This was integrated with a chatbot interface to accept user queries and decide on action output based on entity recognition from such input query and online information from standard databases and governmental and non-governmental resources. We present results of attempts made for some South African use cases, and demonstrate utility for information search and dissemination at a local level. |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.ispartofseries |
Worklist;22347 |
|
dc.subject |
Water quality |
en_US |
dc.subject |
Machine learning |
en_US |
dc.subject |
Language modelling |
en_US |
dc.title |
Water quality information dissemination at real-time in South Africa using language modelling |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Lourens, R. L., Patra, A., Hassim, L., Sima, F., Moodley, A., & Sharma, P. (2018). Water quality information dissemination at real-time in South Africa using language modelling. http://hdl.handle.net/10204/10893 |
en_ZA |
dc.identifier.chicagocitation |
Lourens, Roger L, A Patra, Luqmaan Hassim, Faheem Sima, Avashlin Moodley, and P Sharma. "Water quality information dissemination at real-time in South Africa using language modelling." (2018): http://hdl.handle.net/10204/10893 |
en_ZA |
dc.identifier.vancouvercitation |
Lourens RL, Patra A, Hassim L, Sima F, Moodley A, Sharma P, Water quality information dissemination at real-time in South Africa using language modelling; 2018. http://hdl.handle.net/10204/10893 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Lourens, Roger L
AU - Patra, A
AU - Hassim, Luqmaan
AU - Sima, Faheem
AU - Moodley, Avashlin
AU - Sharma, P
AB - We present a conversational model to apprise users with limited access to computational resources about water quality and real-time accessibility for a given location. We used natural language understanding through neural embedding driven approaches. This was integrated with a chatbot interface to accept user queries and decide on action output based on entity recognition from such input query and online information from standard databases and governmental and non-governmental resources. We present results of attempts made for some South African use cases, and demonstrate utility for information search and dissemination at a local level.
DA - 2018-12
DB - ResearchSpace
DP - CSIR
KW - Water quality
KW - Machine learning
KW - Language modelling
LK - https://researchspace.csir.co.za
PY - 2018
T1 - Water quality information dissemination at real-time in South Africa using language modelling
TI - Water quality information dissemination at real-time in South Africa using language modelling
UR - http://hdl.handle.net/10204/10893
ER -
|
en_ZA |