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M4S 2016 : Mining Big Text Data for Semantics (M4S) and it applications in Finance and Healthcare


When Oct 17, 2016 - Oct 21, 2016
Where Kobe Japan
Submission Deadline Jul 10, 2016
Notification Due Jul 31, 2016
Final Version Due Aug 21, 2016
Categories    NLP

Call For Papers

*Mining Big Text Data for Semantics (M4S)*
and it applications in Finance and Healthcare

ISWC workshop, October 2016, Kobe Japan,

The Mining Big Text Data for Semantics (M4S) workshop aims to explore the
potential combinations of statistical and formal semantic based approaches
that will help to combine the analytic depth and precision of the latter
with the scalability, recall and speed of the former.

M4S focuses on two application domains, namely healthcare and finance. For
both, we see coexistence of large amount of textual documents, which are
still the predominant means of communication, and extensive models in formal
knowledge representation languages. Taking healthcare as an example, textual
documents are still the means of communication when scholars, industrial
practitioners, and authorities publish their research findings, clinical trial
reports, recommendations, GxP protocols and guidelines. However, gigantic
ontologies are also widely available as the outcomes of community-wide
collaborations. In the finance domain, new pieces of data are being produced
at second or even millisecond magnitude. Unambiguously defining the data nuances
and bringing them under regulatory powers of authorities becomes essential.

The workshop intends to foster discussions and seek answers to the following
research and development questions:

Theoretical questions
1. How can distributional semantics and formal semantic work seamlessly
2. What is the optimal way of combining e.g. large-scale curated knowledge
models with associations mined from large text corpora?
3. Which characteristics of formal knowledge models are needed such that
they can be used in combination with distributional semantics?

Application questions
1. How do certain NLP tasks benefit from a combination of distributional
and formal semantics?
2. Specifically, how can such combination be used fruitfully in the
healthcare and finance domains?


Topics of interest include but are not limited to:

Learning/mining formal semantics from large text corpora
1. Relation mining, extraction and validation
2. Event extraction
3. Entity disambiguation and resolution
4. Latent topic modelling
5. Incorporate imperfections from text mining in semantic web

Working with two sorts of semantics
1. Ontology enhanced distributional language models
2. Reasoning with both distributional and formal semantics
3. Full-text search: increasing precision and recall of searches using semantics
4. Semantics-based information extraction,
5. Question answering
6. Translation aids and Multilingual systems

Utilisation in finance and healthcare
1. Requirements and use cases
2. Technical and business challenges

Deployed systems
1. Mining from open data such as PubMed, Edgar, OpenFDA, etc.
2. Experiences and lessons-learnt,
3. Evaluation results

**Submission and Proceedings**

M4S invites three types of submissions:
1. Technical papers: maximum 14 pages
2. Short position papers: maximum 6 pages
3. System demo: a 2-page summary of system features

Submitted papers will be peer-reviewed by at least two workshop Programme
Committee members. Accepted papers will be presented at the workshop. All
papers should be written in English following the Springer conference proceedings
format. Technical papers should not exceed 14 pages including bibliography and
figures. Short position papers should be no more than 6 pages clearly state
“position paper” in the title. All system demo submissions should be accompanied
by a two-page description of key features and core technologies of the system.
Preferably, a link to the real demo should be made available at the time of submission.

**Important Dates**

Paper submission due Sunday, 10 July 2016
Author notification Sunday, 31 July 2016
Camera ready copy due Sunday, 21 August 2016

**Program Committee**

Panos Alexopoulus TextKernel, Netherlands
Ghislain Atemezling Mondeca, France
Christian Biemann TU Darmstadt, Germany
Victor de la Torre Fujitsu Labs, Spain
Ronald Denaux Expert System, Spain
Jana Diesner UIUC, USA
Sergio Fernanadez Redlink, Austria
Alessio Ferrari ISTI CNR, Italy
Nuria Garcia-Santa Expert System, Spain
Andreas Holzinger TU Graz, Austria
Daqing He Pittsburgh University, USA
Gerhard Heyer University of Leipzig, Germany
Bo Hu Fujitsu, United Kingdom
Terunobu Kume Fujitsu Labs, Japan
Yu-ru Lin Pittsburgh University, USA
Nuno Lopez IBM, Ireland
Pablo Mendes IBM, USA
Fumihito Nishino Fujitsu, Japan
Vandenbussche Pierre-Yves Fujitsu, Ireland
Elena Montiel Ponsoda UPM, Spain
Simone Paolo Ponzetto University of Mannheim, Germany
Angus Roberts University of Sheffield, UK
Barbara Thönssen FHNW, Switzerland
Boris Villazon Terrazas Fujitsu, Spain
Hans Friedrich Witschel FHNW, Switzerland

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