Big Data and Natural Language Processing workshop hosted at IEEE Big Data 2016 http://wp.lancs.ac.uk/bignlp2016/

The modality of textual data has been somewhat under-represented in
big data and data science research thus far. This is despite the fact
that large amounts of data are stored in unstructured textual format.
We intend that this workshop will address this shortcoming and bring
together academic and industrial researchers to exchange cutting edge
research in the emerging area of extremely large-scale natural language processing (NLP). This topic has emerged in several areas in parallel
in recent years: information retrieval and search engines, text mining,
machine learning, web-derived corpus/computational linguistics, digital libraries, high performance and parallel computing. Common to all
these areas is some or all of the main parts of the NLP pipeline:
collection, cleaning, annotation, indexing, storage, retrieval and
analysis of voluminous quantities of naturally occurring language
data from the web or large-scale national and international
digitisation initiatives. By hosting this event at IEEE Big Data 2016,
we hope to encourage the communities to come together to consider
synergies between NLP and data science.

In this context, numerous issues should be considered including those
linked to the five Vs of big data: (a) Volume: is having more data for
training and testing NLP techniques always better? (b) Variety: are all
types of data available on a sufficiently large scale? (c) Velocity:
how are parallel methods best applied to carry out NLP on a large scale?
(d) Variability: how does inconsistent data impact on the accuracy of
NLP techniques? (e) Veracity: how does the accuracy of data affect
inferences that can be drawn from it?

Research topics:
Topics covered by the workshop include, but are not restricted to, the following:
Application focused papers e.g. security informatics
Crowdsourcing approaches to large-scale language analysis
Use of big data to train/test methods for low resource languages where
existing NLP approaches do not exist
Efficient NLP for analysing large data sets
Challenges of scaling the NLP pipeline
Big Data Management for NLP
Storage and access for large linguistic data sets
Language processing via GPGPUs
Parallel and distributed computing techniques for language analysis e.g.
HPC, MapReduce, Hadoop, Spark and cloud based machine learning
Visualisation methods for the analysis of large corpora

Dates:
Oct 3, 2016: Due date for full workshop papers submission
Oct 25, 2016: Notification of paper acceptance to authors
Nov 8, 2016: Camera-ready of accepted papers
Dec 5-8, 2016: Workshops

Program Chairs:
Dr Paul Rayson (Lancaster University, UK)
Dr Mark Stevenson (Sheffield University, UK)
Dr John Mariani (Lancaster University, UK)
Dr Laura Irina Rusu (IBM Research Australia)
Gandhi Sivakumar (Watson CoC, IBM Australia)

Program committee members:
Dr Nikos Aletras (Amazon UK)
Dr Enrique Alfonseca (Google Zurich)
Professor Laurence Anthony (Waseda University, Japan)
Dr Piotr Banski (IDS-Mannheim, Germany)
Dr Alistair Baron (Lancaster University, UK)
Dr Eddie Bell (Lyst, UK)
Matt Coole (Lancaster University, UK)
Professor John Keane (University of Manchester, UK)
Dr Dawn Knight (Cardiff University, UK)
Dr Marc Kupietz (IDS-Mannheim, Germany)
Dr Jochen Leidner (Thomson Reuters, UK)
Dr Diana Maynard (Sheffield University, UK)
Dr Rao Muhammad Adeel Nawab (COMSATS, Pakistan)
Dr Sebastian Riedel (UCL, UK)
Dr Mahsa Salehi (IBM Research, Australia)
Dr Irena Spasic (Cardiff University, UK)
Dr Stephen Wattam (Lancaster University, UK)

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