Tom Geller
- Pongnumkul, S., Chaovalit, P., and Surasvadi, N. (2015). Applications of Smartphone-Based Sensors in Agriculture: A Systematic Review of Research. Journal of Sensors, 2015.Google Scholar
Cross Ref
- Hengl, T., Heuvelink, G. B., Kempen, B., Leenaars, J. G., Walsh, M. G., Shepherd, K. D., and Tondoh, J. E. (2015). Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PloS one, 10(6), e0125814.Google ScholarCross Ref
- Derda, R., Gitaka, J., Klapperich, C. M., Mace, C. R., Kumar, A. A., Lieberman, M., and Yager, P. (2015). Enabling the Development and Deployment of Next Generation Point-of-Care Diagnostics, PLOS Negelected Tropical Diseases, http://bit.ly/1PnG40F. z.Google Scholar
Index Terms
- Rich data, poor fields
Recommendations
Rich results from poor resources: NTCIR-4 monolingual and cross-lingual retrieval of korean texts using chinese and english
We report on Korean monolingual, Chinese-Korean English-as-pivot bilingual, and Chinese-English bilingual CLIR experiments using MT software augmented with Web-based entity-oriented translation as resources in the NTCIR-4 environment. Simple stemming is ...
Constructing a poor man’s wordnet in a resource-rich world
AbstractIn this paper we present a language-independent, fully modular and automatic approach to bootstrap a wordnet for a new language by recycling different types of already existing language resources, such as machine-readable dictionaries, parallel ...
Named Entity Disambiguation for Resource-Poor Languages
ESAIR '15: Proceedings of the Eighth Workshop on Exploiting Semantic Annotations in Information RetrievalNamed entity disambiguation (NED) is the task of linking ambiguous names in natural language text to canonical entities like people, organizations or places, registered in a knowledge base. The problem is well-studied for English text, but few systems ...
Comments