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M4S 2016 : Mining Big Text Data for Semantics (M4S) and it applications in Finance and Healthcare | |||||||||||||||
Link: http://www.pitt.edu/~dah44/M4S-2016/M4S | |||||||||||||||
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Call For Papers | |||||||||||||||
*Mining Big Text Data for Semantics (M4S)* and it applications in Finance and Healthcare ISWC workshop, October 2016, Kobe Japan, http://www.pitt.edu/~dah44/M4S-2016/M4S 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 together? 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** 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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