Important Dates:

*. Papers due to: June 13, 2014**
**. Author notifications: July 11, 2014**
**. Accepted camera-ready copy due to AAAI: September 10, 2014**
**. Symposium: November 13—15, 2014*

Today’s enterprises need to make decisions based on analyzing massive
and heterogeneous data sources. More and more aspects of decision making
are driven by data, and as a result, more and more business users need
access to data. Offering easy access to the right data to diverse
business users is of growing importance. There are several challenges
that must be overcome to meet this goal. One is the sheer volume:
enterprise data are predicted to grow by 800 percent in the next five
years. The biggest part (80 percent) are stored in unstructured
documents, most of which are lacking informative meta data or semantic
tags (beyond date, size, and author) that might help in accessing them.
A third challenge comes from the need to offer access to these data for
different types of users, most of whom are not familiar with the
underlying syntax or semantics of the data.

Natural Language Interfaces and Question Answering Systems, such as
Watson, Smartweb, Siri, Start, or Evi, have been successfully
implemented in various domains; for example in encyclopedic knowledge
bases (e.g., IBM`s Jeopardy Challenge), in the field of energy (e.g.,
DGRC), or in the domain of mathematics (e.g., Wolfram Alpha). Following
up on prior work in natural language interfaces to databases (NLIDB) and
question answering (QA) systems, this workshop brings together experts
from both academia and industry to present their most recent work
related to problems that leverage natural language in the context of big
data. They can share information on their latest investigations and
exchange ideas and thoughts in order to push the research frontier
towards new technologies that tackle the aspect of natural language
access to large-scale and heterogeneous data.

Call for Papers:

We welcome the submission of research papers on all aspects of natural
language access and question answering to large-scale structured and
unstructured data. The following topics are of particular interest:

. Natural language interaction technologies (e.g., in the context of
knowledge navigation; personal assistant)
. Speech interfaces and interactive question answering
. Automatic question answering based on structured data sources
. Natural language access to the Semantic Web
. Question answering and natural language interfaces to Linked Data
. Formalization of structured information / queries (RDF, OWL, SPARQL)
. Machine learning techniques (e.g., large-scale hierarchical
classification) for translating the users’ information needs into formal
. Information extraction at web scale that supports natural language access
. Web mining and social network analysis
. Social media analysis and opinion mining
. Text summarization (e.g., question-focused summarization)
. Natural language processing for document analysis including
information extraction, semantic role labeling and co-reference resolution
. Architectures for natural language access to big data
. UIMA modules
. Applications and projects

Об авторе Лидия Пивоварова

СПбГУ - старший преподаватель, University of Helsinki - PhD student
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