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KOSHIK: A large-scale distributed computing framework for NLP

Exner, Peter LU and Nugues, Pierre LU (2014) 3rd International Conference on Pattern Recognition Applications an Methods (ICPRAM 2014) In 3rd International Conference on Pattern Recognition Applications and Methods (ICPRAM 2014) p.464-470
Abstract
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... (More)
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.
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
3rd International Conference on Pattern Recognition Applications and Methods (ICPRAM 2014)
pages
464 - 470
publisher
SciTePress
conference name
3rd International Conference on Pattern Recognition Applications an Methods (ICPRAM 2014)
external identifiers
  • Scopus:84902310568
ISBN
978-989-758-018-5
language
English
LU publication?
yes
id
c9fc300c-af19-4b87-be5b-e4547075d01a (old id 4352684)
date added to LUP
2014-03-20 13:11:37
date last changed
2017-01-16 12:40:22
@inproceedings{c9fc300c-af19-4b87-be5b-e4547075d01a,
  abstract     = {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.<br/>},
  author       = {Exner, Peter and Nugues, Pierre},
  booktitle    = {3rd International Conference on Pattern Recognition Applications and Methods (ICPRAM 2014)},
  isbn         = {978-989-758-018-5 },
  language     = {eng},
  pages        = {464--470},
  publisher    = {SciTePress},
  title        = {KOSHIK: A large-scale distributed computing framework for NLP},
  year         = {2014},
}