Abstract We take a step towards addressing the under- representation of African continent in NLP research by bringing together different stakeholders to create first large, publicly available, high-quality dataset for named entity recognition (NER) ten languages. detail characteristics these languages help researchers and practitioners better understand challenges they pose NER tasks. analyze o...

The significant amount of medicinal chemistry information contained in patents make them an attractive target for text mining. The CHEMDNER task at BioCreative V focused on information extraction from patents. This manuscript describes our submissions to the CEMP (chemical named entity recognition) and GPRO (gene and related object identification) subtasks. Our CEMP submission is an ensemble of...

Recognizing and extracting exact name entities, like Persons, Locations and Organizations are very useful to mining information from text. Learning to extract names in natural language text is called Named Entity Recognition (NER) task. Proper named entity recognition and extraction is important to solve most problems in hot research area such as Question Answering and Summarization Systems, In...