[Corpora-List] CFP: NIPS Workshop on Modern Machine Learning and Natural Language Processing

[Chris Dyer]

Call for papers and participation:

NIPS Workshop on Modern Machine Learning and Natural Language Processing
December 12th (Friday), 2014, Montreal, Quebec, Canada

http://www.cs.cmu.edu/~apparikh/nips2014ml-nlp/index.html

Important Dates:
----------------
Paper Submission Deadline: Oct 9, 2014
Notification of Acceptance: Oct 23, 2014
Workshop: Friday Dec 12th, 2014

Invited Talks:
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 * Phil Blunsom, University of Oxford
 * Hal Daume III, University of Maryland
 * Jacob Eisenstein, Georgia Tech
 * Percy Liang, Stanford
 * Sujith Ravi, Google

Overview:
---------
The structure, complexity, and sheer diversity and variety of human
language makes Natural Language Processing (NLP) distinct from other
areas of AI. Certain core NLP problems have traditionally been an
inspiration for machine learning (ML) solutions e.g., sequence
tagging, syntactic parsing, and language modeling, primarily because
these tasks can be easily abstracted into machine learning
formulations (e.g., structured prediction, dimensionality reduction,
or simpler regression and classification techniques). In turn, these
formulations have facilitated the transfer of ideas such as (but not
limited to) discriminative methods, Bayesian nonparametrics, neural
networks, and low-rank / spectral techniques into NLP. Problems in NLP
are particularly appealing to those doing core ML research due to the
high-dimensional nature of the spaces involved (both the data and the
label spaces) and the need to handle noise robustly, while principled,
well-understood ML techniques are attractive to those in NLP since
they potentially offer a solution to ill-behaved heuristics and
training-test domain mismatch due to the lack of generalization
ability these heuristics possess. But there are many other areas
within NLP where the ML community is less involved, such as semantics,
discourse and pragmatics analysis, summarization, and parts of machine
translation, and that continue to rely on linguistically- motivated
but imprecise heuristics which may benefit from new machine learning
approaches. Similarly, there are paradigms in ML, statistics, and
optimization ranging from sub-modularity to bandit theory to Hilbert
space embeddings that have not been well explored in the context of
NLP. The goal of this workshop is to bring together both applied and
theoretical researchers in natural language processing and machine
learning to facilitate the discussion of new frameworks that can help
advance modern NLP. We invite papers on any relevant topic,
particularly:

 * representation learning for NLP
 * novel theoretical ideas with assumptions suitable to NLP
 * scalable inference/optimization techniques
 * weakly-supervised approaches to handle lack of annotated data in
complex structured prediction tasks
 * problems in multilinguality, NLP for social media, discourse
analysis, semantics, and other areas that would benefit from ML
approaches and analysis

Submission Instructions:
------------------------
Submissions should be written as anonymous extended abstracts, no
longer than 4 pages (excluding references) in the NIPS latex style.
Relevant work previously presented in other conferences is encouraged,
though submitters should note this in their submission. All
submissions should be emailed to nips2014mlnlp at gmail.com

Paper Submission Deadline: Oct 9, 2014
Notification of Acceptance: Oct 23, 2014

Please note that at least one author of each accepted paper must be
available to present the paper at the workshop. Further details
regarding the submission process are available at the workshop
homepage.

Organizing Committee:
--------------------
Ankur P. Parikh (Carnegie Mellon University)
Avneesh Saluja (Carnegie Mellon University)
Chris Dyer (Carnegie Mellon University)
Eric P. Xing (Carnegie Mellon University)

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