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► Journal BrowserSpecial Issue "Advances in Machine Learning Methods for Natural Language Processing and Computational Linguistics"
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: 27 June 2022 | Viewed by 2748
Special Issue Editors
Interests: theory of formal languages; machine translation; artificial intelligence; information extraction
Interests: machine learning; data mining; metalearning; knowledge discovery in databases; text mining; automatic summarization
Special Issue Information
Dear Colleagues,
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 recurse 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.
Prof. Dr. Manuel Vilares-Ferro
Prof. Dr. Pavel Brazdil
Prof. Dr. Gaël Dias
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
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
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Tentative Title: Improving large scale k-nearest neighbor text categorization with label autoencoders
Possible Author: Manuel VILARES
Affiliation: Department of Computer Science, University of Vigo, Ourense 32004, Spain
Abstract: TBA
Tentative Title: Automatic Generation of Domain-Specific Sentiment Lexicon
Possible Author: Pavel BRAZDIL
Affiliation: Laboratory of Artificial Intelligence & Decision Support,INESC TEC, 4200 465 Porto, Portugal
Abstract: TBA
Tentative Title: Prior latent distribution comparison for the RNN variational autoencoder in low-resource modeling
Possible Author: Alexander Gelbukh
Affiliation: Natural Language and Text Processing Laboratory, Center for Computing Research, National Polytechnic Institute, 07738 Mexico City, Mexico
Abstract: TBA
Tentative Title: On the automatic decision about answering questions
Possible Author: Anselmo PEÑAS
Affiliation: Department of Computer Science, UNED, 28040 Madrid, Spain
Abstract: TBA
Tentative Title: Learning Contextualized Models from Dependency Trees
Possible Author: Pablo GAMALLO
Affiliation: Centro Singular de Investigación en Tecnoloxías Intelixentes (CITIUS), University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
Abstract: TBA
Tentative Title: Variational fusion for multimodal sentiment analysis
Possible Author: Alexander Gelbukh
Affiliation: Natural Language and Text Processing Laboratory, Center for Computing Research, National Polytechnic Institute, 07738 Mexico City, Mexico
Abstract: TBA