25.3575, Calls: Computational Linguistics/Canada

Wed Sep 10 21:38:25 UTC 2014

LINGUIST List: Vol-25-3575. Wed Sep 10 2014. ISSN: 1069 - 4875.

Subject: 25.3575, Calls: Computational Linguistics/Canada

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Date: Wed, 10 Sep 2014 17:38:14
From: Chris Dyer [cdyer at cs.cmu.edu]
Subject: NIPS Workshop on Modern Machine Learning and Natural Language Processing

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Full Title: NIPS Workshop on Modern Machine Learning and Natural Language Processing 

Date: 12-Dec-2014 - 12-Dec-2014
Location: Montreal, Quebec, Canada 
Contact Person: Chris Dyer
Meeting Email: cdyer at cs.cmu.edu
Web Site: http://www.cs.cmu.edu/~apparikh/nips2014ml-nlp/index.html 

Linguistic Field(s): Computational Linguistics 

Call Deadline: 09-Oct-2014 

Meeting Description:

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.

Invited Talks:

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

Call for Papers:

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 

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.

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