33.1325, Calls: Computational Linguistics / Frontiers in Artificial Intelligence (Jrnl)

[The LINGUIST List]

LINGUIST List: Vol-33-1325. Wed Apr 13 2022. ISSN: 1069 - 4875.

Subject: 33.1325, Calls:  Computational Linguistics / Frontiers in Artificial Intelligence (Jrnl)

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Date: Wed, 13 Apr 2022 06:56:24
From: Valery Solovyev [maki.solovyev at mail.ru]
Subject: Computational Linguistics / Frontiers in Artificial Intelligence (Jrnl)

 
Full Title: Frontiers in Artificial Intelligence 


Linguistic Field(s): Computational Linguistics 

Call Deadline: 07-Feb-2022 

Text Complexity and Simplification
(https://www.frontiersin.org/research-topics/34050/text-complexity-and-simplif
ication).

Text complexity assessment is one of the urgent problems of our time. Many
modern texts, including classroom books and legislative acts, prove to be too
difficult and as such cannot cater to readers’ needs. This also applies to
legal, financial, banking documents. Although the first methods of measuring
text complexity were suggested over 70 years ago, the problem is far from
being solved. The diversity of languages, text types and genres, as well as
their audience, are major challenges for researchers. Despite the constant
growth in the number of scientific publications, their complex language or the
lack of scientific acculturation of users creates a tendency to avoid these
sources by favoring commercial or political incentives rather than accuracy
and informational value. This difficulty in reading scientific documents also
exists when scientists are interested in scientific documents from disciplines
other than those in which they are experts. Text simplification aims to reduce
these barriers. Text simplification is used in the field of translation
(pre-editing), localization and technical writing. Simplified texts are also
more accessible to non-native speakers, young readers, people with reading
disabilities, or with lower levels of education. Excessively complicated texts
contribute to the strain on automatic simplification of texts. The purpose of
simplifying texts is twofold: to provide their availability to a wider or
specific target audience including readers with learning disabilities and to
further benefit automatic processing of texts. Simplification can be achieved
with different techniques, i.e. lexical substitutions, syntactic paraphrasing,
etc. Deep learning neural networks ensure hope for a breakthrough in assessing
complexity and simplifying texts. The first findings of deep learning
implementation for this have already been obtained, which can be learned from
and pave the way for further research. Attention should be paid to new ideas
on assessing conceptual complexity and simplifying it.

This Research Topic focuses on modern machine learning approaches to these
problems. We hope that this will contribute to the developing best practices.
The final goal is to create an interdisciplinary community of researchers in
information retrieval, data mining, automatic language processing,
linguistics, didactics.

We are looking for contributions in the form of Review, Original Research,
Brief Research Report, Perspective, Technology and Code etc. in the following
areas, including, but not limited to:

- application of state-of-the-art models of neural architectures to text
simplification and complexity
- understanding which features neural networks extract from texts for text
simplification and complexity
- compiling corpora annotated with complexity labels for training and testing
- model evaluation and validation
- description of linguistic features relevant to the assessment of the
difficulty of various classes of texts
- Complex Word Identification
- evaluating the dependence on subject areas, types and genres of texts
- text readability for foreign language learners
- complexity of web content
- text adaptation
- scientific multi-document summarization
- visualization as text simplification
- identification of difficulties preventing the simplification and
summarization of texts
- metrics of text difficulty
- applications in education, law, etc.




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