LINGUIST List 28.1163

Tue Mar 07 2017

Calls: Comp Ling, Gen Ling, Ling Theories, Psycholing, Typology/Australia

Editor for this issue: Sarah Robinson <srobinsonlinguistlist.org>


Date: 06-Mar-2017
From: M. Dolores Jiménez-López <mariadolores.jimenezurv.cat>
Subject: Workshop on Computational Methods for Measuring Language Complexity
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Full Title: Workshop on Computational Methods for Measuring Language Complexity
Short Title: COMLACO 2017

Date: 19-Aug-2017 - 25-Aug-2017
Location: Melbourne, Australia
Contact Person: M. Dolores Jiménez-López
Meeting Email: < click here to access email >
Web Site: https://comlaco.wordpress.com/

Linguistic Field(s): Computational Linguistics; General Linguistics; Linguistic Theories; Psycholinguistics; Typology

Call Deadline: 15-May-2017

Meeting Description:

Workshop held in conjunction with IJCAL-2017: International Joint Conference on Artificial Intelligence http://ijcai-17.org/

This workshop focuses on natural language complexity. Complexity has become an important concept in several scientific disciplines. There has been a lot of research on complexity and complex systems in the natural sciences, economics, social sciences and, now, also increasingly in linguistics. Are all languages equally complex? Does it make sense to compare the complexity of languages? Can languages differ in complexity? Complexity is a controversial concept in linguistics. Until recently, natural language complexity has not been widely researched and still not clear how complexity has to be defined and measured. Twentieth century most theoretical linguists have defended the equi-complexity dogma, which states that the total complexity of a natural language is fixed because sub-complexities in linguistic sub-systems trade off. This idea of equi-complexity, seen for decades as an unquestioned truism of linguistics, has begun to be explicitly questioned in recent years. There have been attempts to apply the concept of complexity used in other disciplines in order to find useful tools to calculate linguistic complexity. Information theory, computational models or the theory of complex systems are examples of areas that provide measures to quantitatively evaluate linguistic complexity.

Many models have been proposed to confirm or refute the hypothesis of linguistic equi-complexity. The tools, criteria and measures to quantify the level of complexity of languages vary and depend on the specific research interests and on the definition of complexity adopted. In fact, there is no agreement in the literature about how to define complexity. Instead, in the literature, we can find a variety of approaches that has led to linguistic complexity taxonomy: absolute complexity vs. relative complexity; global complexity vs. local complexity; system complexity vs. structural complexity, etc. Currently, there is no clear solution to quantify the complexity of languages and each of the proposed models has advantages and disadvantages.

Linguistic complexity is also a key point in automatic natural language processing. Therefore, we propose a cross-discipline workshop that foster exchange of ideas between people in the area of artificial intelligence and natural language processing and people dealing with natural language complexity from a cognitive or a theoretical point of view. The main objective of this workshop is to bring together researchers from different areas that have in common their interest on linguistic complexity boosting the interchange of knowledge and methods between specialists that have approached complexity from different viewpoints. We want to promote interdisciplinarity among researchers that are dealing with any type of language complexity.

Call for Papers:

We are interested in contributions introducing new methods, models, definitions and measures to assess natural languages complexity. We are especially interested in computational and formal approaches to linguistic complexity.

Topics include (but are not limited to):

- Complexity in human natural language processing
- Complexity in automatic natural language processing
- Formal tools for measuring linguistic complexity
- Natural language processing tools for measuring linguistic complexity
- Machine learning tools for measuring linguistic complexity
- Information theory measures of linguistic complexity
- Complex systems theory measures for linguistic complexity
- Typological approaches to linguistic complexity
- Psycholinguistic approaches to linguistic complexity
- Cognitive approaches to linguistic complexity

Submissions:

We invite submissions including original research contributions, applications, surveys, state-of-the-art papers, position papers and work in progress papers.

Submitted papers must be formatted according to IJCAI guidelines here:

http://ijcai-17.org/MainTrackCFP.html

Papers must be no longer than 7 pages.

All proposed papers must be submitted in electronic form (PDF format) through EasyChair:

https://easychair.org/conferences/?conf=comlaco2017


Page Updated: 07-Mar-2017