Author: Pavel Brazdil

[rede.APPIA] MDPI Special Issue “Advances in Machine Learning Methods for Natural Language Processing and Computational Linguistics”

Pavel Brazdil
Call for Papers MDPI Special Issue
“Advances in Machine Learning Methods for Natural Language Processing and Computational Linguistics”

Deadline: 30 June 2022

Site:
https://www.mdpi.com/journal/mathematics/special_issue/Machine_Learning_Methods_Natural_Language_Processing_Computational_Linguistics

Keywords
– ML-based tools for CL and NLP
– Domain-specific and low-resource languages
– Generation of training resources from raw data
– Halting conditions and over–under-fitting detection
– Integration of symbolic and model-based processing
– Reasoning about large and multiple documents
– Sampling strategies

Machine learning (ML) algorithms can be used to analyze vast volumes of information, identify patterns and generate models capable of recognizing them in new data instances. This allows us to address complex tasks with the only constraint being the necessity of a suitable training database.

Furthermore, today's digital society provides access to a vast range of raw data, but also generates the need for managing them effectively. This makes up natural language processing (NLP), a collective term referring to the automatic computational treatment of human languages for which purely symbolic techniques show clear limitations, a popular field for exploiting ML capacities. The same is true for computational linguistics (CL), which is more concerned with the study of linguistics.

However, this collaborative framework must be based on a formally well-informed strategy to ensure its reliability. In this context, this Special Issue focuses on both the application of ML techniques to solve NLP and CL tasks and on the generation of linguistic resources to enable this, for example, the construction of syntactic structures without recourse to tree banks for training, which would greatly simplify the implementation of statistical-based parsers, especially when dealing with out-of-domain scenarios or low-resource languages. By way of a more applicative issue, we could address the generation of models allowing efficient contextual representations, a nontrivial task when dealing with large-scale or multiple documents, but essential for language understanding.

[rede.APPIA] CFP for AutoML workshop at ECML/PKDD-2017

Pavel Brazdil
CALL FOR PAPERS
The ECML-PKDD 2017 Workshop on Automatic Machine Learning (AutoML) 
Collocated with ECML-PKDD in Skopje, Macedonia, September 22, 2017
—————————————————————-
Important Dates:
 Submission deadline: 10 July, 2017, 11:59pm UTC-12 (July 10 anywhere in the world)
 Notification: 30 July, 2017
—————————————————————-
AutoML: Automatic selection, configuration and composition of machine learning algorithms
This workshop will provide a platform for discussing recent developments in the areas of meta-learning, algorithm selection and configuration, which arise in many diverse domains and are increasingly relevant today. Researchers and practitioners from all areas of science and technology face a large choice of parameterized machine learning algorithms, with little guidance as to which techniques to use in a given application context. Moreover, data mining challenges frequently remind us that algorithm selection and configuration are crucial in order to achieve cutting-edge performance, and drive industrial applications. Meta-learning leverages knowledge of past algorithm applications to select the best techniques for future applications, and offers effective techniques that are superior to humans both in terms of the end result and especially in the time required to achieve it. In this workshop, we will discuss different ways of exploiting meta-learning techniques to identify the potentially best algorithm(s) for a new task, based on meta-level information, including prior experiments on both past datasets and the current one. Many contemporary problems also require the use of complex workflows that consist of several processes or operations. Constructing such complex workflows requires extensive expertise, and could be greatly facilitated by leveraging planning, meta-learning and intelligent system design. This task is inherently interdisciplinary, as it builds on expertise in various areas of AI.
Main research areas of relevance to this workshop include, but are not limited to:
– Algorithm / model selection and configuration
– Meta-learning and exploitation of meta-knowledge
– Hyperparameter optimization
– Automatic generation and evaluation of learning processes / workflows
– Representation learning and automatic feature extraction / construction
– Automatic feature coding / transformation
– Automatic detection and handling skewed data or missing values
– Automatic acquisition of new data (active learning, experimental design)
– Usage of planners in the construction of workflows
– Reinforcement learning for parameter control & algorithm design
– Representation of learning goals and states in learning
– Control and coordination of learning processes
– Meta-reasoning
– Layered learning
– Multi-task and transfer learning
– Learning to learn
– Intelligent experiment design
Co-chairs: Frank Hutter, Holger Hoos, Pavel Brazdil and Joaquin Vanschoren
We welcome standard submissions of up to 6 pages (not including references) in ECML-PKDD format, as well as longer papers of up to 15 pages (not including references).
For further details, please see the submission page; the submission deadline is July 10th, 2017.
All accepted papers will be presented as posters and very short poster spotlights; the best paper(s) will be selected for an oral presentation.
At least one author of each accepted paper should be registered for the main conference.