ECML PKDD 2012: Last Call for Demos and NECTAR-track submissions

ECML PKDD 2012: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
September 24-28, 2012 – Bristol, UK

Home

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Call for Demonstrations
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ECML PKDD 2012 solicits submissions for demos. Submissions must describe working systems and be based on state-of-the-art machine learning and data mining technology. These systems may be innovative prototype implementations or mature systems that use machine learning techniques and knowledge discovery processes in a real setting. We particularly welcome demos that use open-source software. For more information, please check http://www.ecmlpkdd2012.net/calls/call-for-demos/

Submission deadline: Friday, May 18, 2012

All aspects of the submission and notification process will be handled online via the CMT conference management toolkit at https://cmt.research.microsoft.com/ECMLPKDD2012/

For inquiries please contact us at ECMLPKDD2012demos@cs.bris.ac.uk

Bettina Berendt & Myra Spiliopoulou
ECML PKDD 2012 Demo Track Chairs

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Call for NECTAR-track submissions
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For the first time, ECML PKDD 2012 will have a NECTAR-track, featuring significant machine learning and data mining results published or disseminated no earlier than 2010 at a different conference or in a journal. One goal of this track is to offer conference attendees the opportunity to learn about machine learning and/or data mining related results published in other communities. Papers describing innovative applications of state-of-the-art machine learning and/or data mining algorithms are also welcome, but should be different from demonstration papers; the latter are to be submitted to the demos track. We also invite submissions presenting compactly well-founded results which appeared in a series of publications that advanced a single novel influential idea or vision. For more information, please check: http://www.ecmlpkdd2012.net/calls/call-for-nectar-talks/

Submission deadline: Friday, May 18, 2012

All aspects of the submission and notification process will be handled online via the CMT conference management toolkit at https://cmt.research.microsoft.com/ECMLPKDD2012/

For inquiries please contact us at ECMLPKDD2012nectar@cs.bris.ac.uk

Thomas Gaertner & Gemma Garriga
ECML PKDD 2012 NECTAR Track Chairs

ECML PKDD 2012: Last Call for Demos and NECTAR-track submissions

ECML PKDD 2012: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
September 24-28, 2012 – Bristol, UK

Home

———————————————–
Call for Demonstrations
———————————————–

ECML PKDD 2012 solicits submissions for demos. Submissions must describe working systems and be based on state-of-the-art machine learning and data mining technology. These systems may be innovative prototype implementations or mature systems that use machine learning techniques and knowledge discovery processes in a real setting. We particularly welcome demos that use open-source software. For more information, please check http://www.ecmlpkdd2012.net/calls/call-for-demos/

Submission deadline: Friday, May 18, 2012

All aspects of the submission and notification process will be handled online via the CMT conference management toolkit at https://cmt.research.microsoft.com/ECMLPKDD2012/

For inquiries please contact us at ECMLPKDD2012demos@cs.bris.ac.uk

Bettina Berendt & Myra Spiliopoulou
ECML PKDD 2012 Demo Track Chairs

———————————————–
Call for NECTAR-track submissions
———————————————–

For the first time, ECML PKDD 2012 will have a NECTAR-track, featuring significant machine learning and data mining results published or disseminated no earlier than 2010 at a different conference or in a journal. One goal of this track is to offer conference attendees the opportunity to learn about machine learning and/or data mining related results published in other communities. Papers describing innovative applications of state-of-the-art machine learning and/or data mining algorithms are also welcome, but should be different from demonstration papers; the latter are to be submitted to the demos track. We also invite submissions presenting compactly well-founded results which appeared in a series of publications that advanced a single novel influential idea or vision. For more information, please check: http://www.ecmlpkdd2012.net/calls/call-for-nectar-talks/

Submission deadline: Friday, May 18, 2012

All aspects of the submission and notification process will be handled online via the CMT conference management toolkit at https://cmt.research.microsoft.com/ECMLPKDD2012/

For inquiries please contact us at ECMLPKDD2012nectar@cs.bris.ac.uk

Thomas Gaertner & Gemma Garriga
ECML PKDD 2012 NECTAR Track Chairs

Job in Machine Translation: 2-year research position at Sheffield University

The Department of Computer Science has a position for a Research
Associate in Machine Translation. The position is funded by the EU FP7
as part of the QTLaunchPad project, which will investigate how to
measure and overcome quality barriers in Machine Translation (MT).

QTLaunchPad is a support action which aims at producing a better
environment for large-scale collaborative research on high-quality MT,
and preparing and launching a large international action dedicated to
a scientifically and economically significant breakthrough in quality
translation technology. Key to this project are the methodological
and empirical foundations of research toward breaking the
quality-barriers. As a partner in this project, the University of
Sheffield will be primarily responsible for the Quality Estimation
methods and software and its use for guiding MT, but will also
contribute to the analysis of quality barriers and design of quality
metrics.

At the University of Sheffield the project will be led by Dr Lucia
Specia (http://www.dcs.shef.ac.uk/~lucia/). The research will build
upon the group’s leading research in statistical modelling of the
quality estimation problem. It will employ a Research Associate for up
to 24 months, available to start July 2012. The role will require the
post holder to undertake research and software development on quality
estimation, and to collaborate with other researchers working on the
QTLaunchPad Project (Sheffield, DCU-Dublin, DFKI-Saarbrücken and
ILSP-Athens).

This is an opportunity to work in a well-connected international team
with world-leading reputations in the Natural Language Processing
(NLP) research group at The University of Sheffield. The NLP group is
well known internationally for its research, and is one of the largest
research groups in computational linguistics and text engineering in
the UK. There are excellent opportunities for publications, project
and conference trips. For suitably qualified candidates, possibilities
exist to contribute to teaching, for example as part of an MSc in
Computer Science with Speech and Language Processing.

Applicants should have:

– PhD (or equivalent work experience) in a related area
– Experience in natural language processing
– Acquaintance with Machine Translation
– Programming experience, e.g., in C++ , Java, Perl/Python

Experience in machine learning, experience in evaluation of machine
translation and multilingual background (English and especially one of
the following languages of the Project: Portuguese, Greek, or German)
are a plus.

Salary for this grade: £28,401 – £35,938 per annum with potential to
progress to £37,012 per annum.

For informal inquiries contact Dr. Lucia Specia: L.Specia@sheffield.ac.uk

For applications: http://www.shef.ac.uk/jobs, Search and apply for
jobs using reference number UOS004520

Closing date: 25 May 2012.

Call for Paper: Special Issue on NLP in the Web Era

Call for paper

Special issue on
Natural Language Processing in the Web Era

Intelligenza Artificiale, Volume 6.2, scheduled for December 2012

Guest editors: Roberto Basili and Bernardo Magnini

THEMES

Natural Language is still the main carrier for the definition, synthesis and exchange of knowledge in the real world,
and this is entirely reflected in the Web contents. Although the growing levels of integration, multichannel and
modalities of the information made available in the current Web, thus including the Social Web bodies of resources,
the central role of language in e-mails, blogs, twits as well as multimedia pages cannot be denied.
Notice that even for pictures, videos or audio information (including news or artistic materials) natural language is
crucial in the annotation, explanation and delivery processes.

Unfortunately, the increase in volumes also corresponds to a growing complexity in terms of needs,
phenomena and applications. While social media often introduce specific sublanguages that are still largely
unexplored by current language processing tools, the pervasive noise and incompleteness that characterize
real documents in blogs, forums or SMS channels also amplify requirements such as coverage and robustness
of the NLP technology. On the other side, while knowledge representation technologies require massive amount
of Web data to be traced, linked and semantically harmonized, this enterprise is tightly bound by the quality
of the linguistic interpretation capabilities that the underlying integration systems can exhibit. Finally, the forms
of information retrieval, exchange and sharing used commonly by large communities of Social Web users
are such that the semantic management of smaller text units is crucially needed. Specific tasks, such as
personalized document management or context-aware search in mobile applications are strongly tight to the
interpretation of fine-grain phenomena, such as questions, short queries or twits. In these large scale distributed
scenarios multilinguality is also an issue for language processing technologies.

The above challenges and the topics related to research advances in this area are all central to this special issue.
Original research papers as well as surveys of on-going work in specific sectors are welcomed.
The aim is to provide to researchers both in the academic and industrial sectors a comprehensive picture of a
largely multidisciplinary field. We are soliciting contributions related to the following, not exhaustive, set of themes:

– Semantic Web. Language processing for open linked data. Linguistic approaches to Ontology Learning, Reconciliation
and Population. Language Processing for Ontology Engineering. Semantic Web applications with explicit language
processing components.
– Search. Question Answering. Lexical Semantic approaches to Intelligent Web Search. Semantic search.
Enterprise Search. The use of Lexical Resources for Knowledge Search in the Web.
– Language and Learning. Computational Natural Language Learning from Web data. Machine Learning methods,
models and algorithms for treating linguistic information implicit in Web data. Advanced Supervised learning for NLP.
Semi-supervised and Unsupervised approaches for NLP over the Web.
– Web, Linguistic Resources and Knowledge Acquisition. Wikipedia-based knowledge acquisition. Machine
Reading from Web sources. Web-scale Information Extraction.
– Multimodality. Human-Computer Interfaces. Speech and Language based interface. Multimodal Interaction.
Digital assistants. Language-based Mobile and Web applications.
– Social media. New languages of the Social Web. Linguistic Treatment of Social Web data. Special-purpose
lexical and grammatical models for Web 2.0 languages.
Folksonomies and Language Processing. Lexical knowledge and Cloud Tags.
– Sentiment Analysis, Emotion Modeling and Recommending. Language Processing approaches to
Opinion Analysis. Emotional computing. Digital Advertisement and Persuasion.

PRELIMINARY REVIEWING COMMITTEE

Marie Helene Candito, Universite Paris 7
Philipp Cimiano, University of Bielefeld
Rodolfo Delmonte, University of Venice
Gregory Grefenstette, 3DS Exalead, France
Iryna Gurevych, University of Darmstadt
Leonardo Lesmo, University of Turin
Rutu Mulkar-Mehta, University of California, San Diego
Guenter Neumann, DFKI, Germany
Sergei Nirenburg, University of Maryland
Massimo Poesio, University of Trento, Italy
German Rigau, University of the Basque Country
Frederique Segond, Viseo, France
Giovanni Semeraro, University of Bari
Anne Vilnat, Limsi, France
Yorick Wilks, Florida Institute of Human and Machine Cognition

IMPORTANT DATES

– Deadline for submission: June 15th, 2012
– List of papers selected: July 30th , 2012
– Deadline for camera ready papers: September 15th 2012
– Publication: December 2012

THE JOURNAL

Intelligenza Artificiale (http://www.iospress.nl/journal/intelligenza-artificiale/), edited by IOS Press, is the official
journal of the Italian Association for Artificial Intelligence (AI*IA). Intelligenza Artificiale publishes rigorously reviewed articles
(in English) in all areas of Artificial Intelligence, with a special attention to original contributions. It also publish assessments
of the state of the art in various areas of AI, and innovative system descriptions with appropriate evaluation.

PRACTICAL ISSUES

Contributions (around 15 pages, PDF format) should be sent by email to the two guest editors,
Bernardo Magnini (magnini@fbk.eu) and Roberto Basili (basili@info.uniroma2.it).

Detailed submission instructions are available on the Web site of the journal:
http://www.iospress.nl/journal/intelligenza-artificiale/.
The journal only publishes original contributions in English.

Web site: http://ai-nlp.info.uniroma2.it/basili/NLPiWE/SpecialIssueOnNLP_in_the_Web_era_CFP.html

Last Call For Papers – Special Issue on Learning Semantics in Machine Learning *** Deadline May 1st ***

**Overview**
A key ambition of AI is to render computers able to evolve and interact
with the real world. This can be made possible only if the machine is able
to produce an interpretation of its available modalities (image, audio,
text, etc.) which can abe used to support reasoning and taking appropriate
actions. Computational linguists use the term “semantics” to refer to the
possible interpretations of natural language expressions and there is
recent work in “learning semantics” – finding (in an automated way) these
interpretations. However, “semantics” are not restricted to the natural
language (and speech) modality, and are also pertinent to visual
modalities. Hence, knowing visual concepts and common relationships
between them would certainly provide a leap forward in scene analysis and
in image parsing akin to the improvement that language phrase
interpretations would bring to data mining, information extraction or
automatic translation, to name a few.

Progress in learning semantics has been slow mainly because this involves
sophisticated models which are hard to train, especially since they seem
to require large quantities of precisely annotated training data. However,
recent advances in learning with weak, limited and indirect supervision
led to the emergence of a new body of research in semantics based on
multi-task/transfer learning, on learning with semi/ambiguous/indirect
supervision or even with no supervision at all. Hence, this special issue
invites paper submissions on recent work for learning semantics of natural
language, vision, speech, etc.

Papers should address at least some of the following questions:
– How should meaning representations be structured to be easily
interpretable by a computer and still express rich and complex knowledge?
– What is a realistic supervision setting for learning semantics?
– How can we learn sophisticated representations with limited supervision?
– How can we jointly infer semantics from several modalities?

**Dates**
Submission deadline: **May 1, 2012**
First review results: July 30, 2012
Final drafts: September 30, 2012

**Submissions**
Papers must be submitted online, selecting the article type that indicates
this special issue. Peer reviews will follow the standard Machine Learning
journal review process. It is the policy of the Machine Learning journal
that no submission, or substantially overlapping submission, be published
or be under review at another journal or conference at any time during the
review process. Papers extending previously published conference papers
are acceptable, as long as the journal submission provides a significant
contribution beyond the conference paper, and the overlap is described
clearly at the beginning of the journal submission. Complete manuscripts
of full length are expected, following the MLJ guidelines in
http://www.springer.com/computer/ai/journal/10994 .

**Guest Editors**
Antoine Bordes (antoine.bordes@utc.fr)
Léon Bottou (leon@bottou.org)
Ronan Collobert (ronan@collobert.com)
Dan Roth (danr@illinois.edu)
Jason Weston (jweston@google.com)
Luke Zettlemoyer (lsz@cs.washington.edu)

MLG-2012: Call for papers

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Tenth workshop on Mining and Learning with Graphs (MLG-2012).
http://dtai.cs.kuleuven.be/events/mlg2012/

Edinburg, July 1st, 2012
Co-located with ICML-2012
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Introduction:
There is a great deal of interest in analyzing data that is best represented as a graph. Examples include the WWW, social networks, biological networks, communication networks, food webs, and many others. The importance of being able to effectively mine and learn from such data is growing, as more and more structured and semi-structured data is becoming available. Traditionally, a number of subareas have worked with mining and learning from graph structured data, including communities in graph mining, learning from structured data, statistical relational learning, inductive logic programming, and, moving beyond subdisciplines in computer science, social network analysis, and, more broadly network science. The objective of this workshop is to bring together researchers from a variety of these areas, and discuss commonality and differences in challenges faced, survey some of the different approaches, and provide a forum for to present and learn about some of the most cutting edge research in this area. As an outcome, we expect participants to walk away with a better sense of the variety of different tools available for graph mining and learning, and an appreciation for some of the interesting emerging applications for mining and learning from graphs.

The goal of this workshop will be to structure and explore the state-of-the-art algorithms and methods, to examine graph-based models in the context of real-world applications, and to identify future challenges and issues. In particular we are interested in the following topics:
* Relationships between mining and learning with graphs and statistical relational learning
* Relationships between mining and learning with graphs and inductive logic programming
* Relationships between mining and learning with graphs and algorithmic graph theory and related fields
* Kernel methods for structured data
* Probabilistic models for structured data
* Graph mining
* (Multi-)relational data mining
* Methods for structured outputs
* Network analysis
* Large-scale learning and applications
* Sampling issues in graph algorithms
* Evaluation of graph algorithms
* Graph mining benchmarks and datasets
* Applications of graph mining in real world domain

Submission Guildance:
We invite both full papers presenting new contributions and short papers describing work in progress or open problems. Full papers should be at most 8 pages (ICML format), short papers and open problem statements at most 3 pages.

Papers can be submitted online via
https://www.easychair.org/account/signin.cgi?timeout=1;conf=mlg2012

Authors whose papers were accepted to the workshop will have the opportunity to give a short presentation at the workshop as well as present their work in a poster session.

Important dates:
Paper submission deadline May 7th
Notification of acceptance May 21th
Final paper submission June 18th
Workshop July 1st

IMS 2012 – registration open

http://www.IMS2012.org.uk

The 2012 International Mathematica Symposium will be hosted by the Departments of Mathematics and Computer Science at University College London in June 2012. The IMS organizers are grateful to NVIDIA for their sponsorship of IMS 2012.

About the International Mathematica Symposium
The International Mathematica Symposium is an interdisciplinary conference for and by users of Mathematica in the physical and life sciences, mathematics, engineering, graphics and design, arts and music, education, industry, finance, and commerce.

If you use Mathematica in research or teaching, or if you have developed or are developing products based on Mathematica, then IMS is an opportunity to share your results with like-minded colleagues. IMS has built up a deserved reputation as an exceptionally convivial and friendly gathering.

For full details please see: http://www.IMS2012.org.uk

MLSB 2012: Call for Contributions

Basel, Switzerland, September 8 and 9, 2012
http://mlsb.cc

Submission Deadline: June 7, 2012
Author Notification: June 28, 2012

MLSB12, the Sixth International Workshop on Machine Learning in Systems Biology will be held in Basel, Switzerland on September 8 and 9, 2012. The Workshop is organized as “Satellite Meeting” of the 11th European Conference on Computational Biology (ECCB).

The aim of this workshop is to contribute to the cross-fertilization between the research in machine learning methods and their applications to systems biology (i.e., complex biological and medical questions) by bringing together method developers and experimentalists. We encourage submissions bringing forward methods for discovering complex structures (e.g. interaction networks, molecule structures) and methods supporting genome-wide data analysis.

Submissions
The deadline for submissions of extended abstracts is June 7, 2012 (23:59 in the time zone of your choice).

We invite you to submit an extended abstract of up to 2 pages describing new or recently published (2012) results, formatted according to the NIPS style format (MLSB is not double-blind this year). Each extended abstract must be submitted online via the Easychair submission system: http://www.easychair.org/conferences/?conf=mlsb2012.

The extended abstracts will be reviewed by the scientific programme committee. They will be selected for oral or poster presentation according to their originality and relevance to the workshop topics. Electronic versions of the extended abstracts will be accessible to the participants prior to the conference, distributed in hardcopy form to participants at the conference, and will be made publicly available on the conference web site after the conference. However, the book of abstracts will not be published and the extended abstracts will not constitute a formal publication.

Topics
We encourage submissions bringing forward methods for discovering complex structures (e.g. interaction networks, molecule structures) and methods supporting genome-wide data analysis. A non-exhaustive list of topics suitable for this workshop are:

Methods:
Machine Learning Algorithms
Bayesian Methods
Data integration/fusion
Feature/subspace selection
Clustering Metabolic pathway modeling
Biclustering/association rules
Kernel Methods
Probabilistic inference
Structured output prediction
Systems identification
Graph inference, completion, smoothing
Semi-supervised learning

Applications:
Sequence Annotation
Gene Expression and post-transcriptional regulation
Inference of gene regulation networks
Gene prediction and whole genome association studies
Signaling networks
Systems biology approaches to biomarker identification
Rational drug design methods
Metabolic reconstruction
Protein function and structure prediction
Protein-protein interaction networks
Synthetic biology

MLSB 2012 Chairs
Karsten Borgwardt, Max Planck Institutes and University of Tübingen
Gunnar Rätsch, Memorial Sloan-Kettering Cancer Center, New York

CFP: Big Data Mining (BigMine-12) Workshop at KDD12

CALL FOR PAPERS

Big Data Mining (BigMine-12)
1st International Workshop on Big Data, Streams and Heterogeneous
Source Mining: Algorithms, Systems, Programming Models and
Applications (BigMine-12) – a KDD2012 Workshop

KDD2012 Conference Dates: August 12-16, 2012
BigMine-12 Workshop Date: Aug 12, 2012
Beijing, China

http://www.big-data-mining.org

Key dates:
Papers due: May 9, 2012
Acceptance notification: May 23, 2012
Workshop Final Paper Due: June 8, 2012
Workshop Proceedings Due: June 15, 2012

Paper submission and reviewing will be handled electronically. Authors
should consult the submission site
(http://big-data-mining.org/submission/) for full details regarding
paper preparation and submission guidelines.

Papers submitted to BigMine-12 should be original work and
substantively different from papers that have been previously
published or are under review in a journal or another
conference/workshop.

Following KDD main conference tradition, reviews are not double-blind,
and author names and affiliations should be listed.

We invite submission of papers describing innovative research on all
aspects of big data mining.

Examples of topic of interest include

– Scalable, Distributed and Parallel Algorithms
– New Programming Model for Large Data beyond Hadoop/MapReduce,
STORM, streaming languages
– Mining Algorithms of Data in non-traditional formats
(unstructured, semi-structured)
– Applications: social media, Internet of Things, Smart Grid,
Smart Transportation Systems
– Streaming Data Processing
– Heterogeneous Sources and Format Mining
– Systems Issues related to large datasets: clouds, streaming
system, architecture, and issues beyond cloud and streams.
– Interfaces to database systems and analytics.
– Evaluation Technologies
– Visualization for Big Data
– Applications: Large scale recommendation systems, social media
systems, social network systems, scientific data mining,
environmental, urban and other large data mining applications.

Papers emphasizing theoretical foundations, algorithms, systems,
applications, language issues, data storage and access, architecture
are particularly encouraged.

We welcome submissions by authors who are new to the data mining
research community.

Submitted papers will be assessed based on their novelty, technical
quality, potential impact, and clarity of writing. For papers that
rely heavily on empirical evaluations, the experimental methods and
results should be clear, well executed, and repeatable. Authors are
strongly encouraged to make data and code publicly available whenever
possible.

Top-quality papers accepted and presented at the workshop after
careful revisions by the authors, reviewed by original PC members and
chairs will be recommended to ACM TIST, ACM TKDD, IEEE Intelligent
Systems or IEEE Computer for fast publication, depending on relevance
of the topic

Theoretical attention

Dear Colleagues,

I would appreciate if you could devote some of your attention
to explaining the shape of the learning curves seen in statistical
machine translation.

Here below is a link to an extensive experimental study.
These learning curves are different than those I expect from
statistical learning theory
and it would be great to have some model explaining them.
I can think of no better crowd than Pascal2 to answer this question.

regards

Nello Cristianini

Learning to Translate: A Statistical and Computational Analysis
Marco Turchi, Tijl De Bie,Cyril Goutte, and Nello Cristianini

Advances in Artificial Intelligence
Volume 2012 (2012), Article ID 484580, 15 pages
doi:10.1155/2012/484580

The article can be found here:
downloads.hindawi.com/journals/aai/2012/484580.pdf