@article {418,
	title = {Orchestrating the natural language processing software in the cloud computing environment},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {396 - 399},
	abstract = {The most of natural language processing problems are data-intensive. An important step in the distributed orchestration of natural language processing software is a rational choice of the specific middleware. The middleware should solve the presented problem with minimal deployment, support and usage costs. It is necessary to run and use that software in the distributed cloud computing environment to achieve such advantages such as consolidation, isolation, and efficient use of the existent infrastructure. It is often impossible to modify the existent natural language processing software to integrate it into the cloud computing environment because of licensing or organizational issues. This paper studies various popular distributed data processing tools and evaluates the selected natural language processing tools on a relatively large document collection in distributed way using the Gearman framework. The document collection is a 10{\textquoteright}000 sentences from the Russian news subcorpus of the Leipzig corpora. The benchmarks are presented and discussed.},
	keywords = {Cloud computing, Data-intensive computing, Distributed computing, Natural Language Processing, Service orchestration},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84890443570\&partnerID=40\&md5=80168707d25eb1e8a3acabfb35fdf93f},
	author = {Ustalov, D. and Goldshtein, M.}
}
@article {354,
	title = {Creating multi-level class hierarchy for question classification with NP analysis and wordnet},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Export Date: 10 July 2014},
	month = {2012},
	pages = {379 - 388},
	abstract = {Question answering systems provide answer to the questions using various question processing techniques and extracts answers from a set of documents. Finding the exact answer is more interesting and useful than getting a list of documents to look through and find the answer manually. Question answering is not same as a traditional document retrieval search engine where a set of relevant documents are returned in response of the query, whereas, in the question answering systems the response of the query is a concise and exact answer to the question. Typically, Question Classification (QC) is the first step in a Question Answering (QA) system. This phase is responsible for finding out the type of the expected answer by pruning out the extra information that is not relevant to extract the answer. Almost all the previous QC algorithms evaluated their work on the basis of a common class hierarchy already defined. The coarse grained classes Location, Entity and Numeric in the existing hierarchy have a fine grained class Other. We present the framework to create new fine grained classes to replace the Other classes. We also discuss the motivation behind the replacement and how the new fine grained classes may support the answer extraction. Additionally, we also present an automatic hierarchy creation method to add new class nodes using WordNet and Noun phrase parsing.},
	keywords = {Meta Information Management, Natural Language Processing, Question Answering4, Question Class Hierarchy, Question Classification},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84874837728\&partnerID=40\&md5=3f2a45281c24dba71e9fbc36717f9a70},
	author = {Bakhtyar, M. and Kawtrakul, A. and Baber, J. and Doudpota, S.M.}
}
@article {159,
	title = {AnT\&CoW: Share, classify and elaborate documents by means of annotation},
	journal = {Journal of Digital Information Management},
	volume = {6},
	year = {2008},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2008},
	pages = {61 - 70},
	abstract = {As collective and mediated work spread out, digital data, and particularly documents, become predominant, In this paper, we describe how to support document management and especially document classification within a mechanical engineering design team working in an asynchronous and distributed way. The team members exchange comments to plan their project, to share documents, and to elaborate new documents. These comments are annotations linked together and linked to document(s). Annotations enable users to understand who is playing which role in the project and how collaborators understand a document and classify it. Observing this team led us to design an annotation tool (AnT\&CoW) that we have implemented to support its collective activity. We conclude this article by focusing on the classification building functionality, based on the use of Natural Language Processing tools.},
	keywords = {Annotation, Document, Multi-viewpoint classification, Natural Language Processing, Thesauri},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-56849132057\&partnerID=40\&md5=8195cee5570fdfc7541bbf7254f465cb},
	author = {Lortal, G. and Todirascu-Courtier, A. and Lewkowicz, M.}
}
