However, the typical experimental setup for evaluating Named Entity Recognition (NER) systems is not directly applicable to systems that process text in real time as the text is being typed.
Supervised machine learning assumes the availability of fully-labeled data, but in many cases, such as low-resource languages, the only data available is partially annotated.
We evaluate our system on a broad range of datasets, including standard fine-grained and coarse-grained entity typing datasets, and also a dataset in the biological domain.
Named entity recognition (NER) is the backbone of many NLP solutions.
1 code implementation • • Daniel Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicholas Rizzolo, Lev Ratinov, Guanheng Luo, Quang Do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling, Dan Roth
Recent work in NLP has attempted to deal with low-resource languages but still assumed a resource level that is not present for most languages, e. g., the availability of Wikipedia in the target language.
The cross-lingual NER model is a language-independent model which can extract named entity mentions in the text of any language in Wikipedia.