KOSHIK- A Large-scale Distributed Computing Framework for NLP
Peter Exner, Pierre Nugues · 2014
In this paper, we describe KOSHIK, an end-to-end framework to process the unstructured natural language content of multilingual documents. We used the Hadoop distributed computing infrastructure to build this framework as it enables KOSHIK to easily scale by adding inexpensive commodity hardware. We designed an annotation model that allows the processing algorithms to incrementally add layers of annotation without modifyingtheoriginaldocument. We used the Avro binary format to serialize th edocuments. Avro is designed for Hadoop and allows other data warehousing tools to directly query the documents. This paper reports the implementation choices and details of the framework,the annotation model,the options for querying processed data, and the parsing results on the English and Swedish editions of Wikipedia.