Natural Language Processing with Tsetlin Machines
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: closed (26 September 2021) | Viewed by 1758
Special Issue Editors
Interests: learning automata; bandit algorithms; Tsetlin machines; Bayesian reasoning; reinforcement learning; computational linguistics
Special Issues, Collections and Topics in MDPI journals
Interests: Tsetlin machines; learning automata; reinforcement learning; stochastic processes
Special Issues, Collections and Topics in MDPI journals
Interests: information integration; data fusion and sense-making; complex adaptive systems; scalable data streams and Tsetlin machines
Special Issues, Collections and Topics in MDPI journals
Interests: computational linguistics; deep learning; Tsetlin machines; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The emerging paradigm of Tsetlin machines makes a fundamental shift from arithmetic-based to logic-based machine learning. At the core, finite-state machines learn patterns using logical clauses, and these constitute a global description of the task learnt. The paradigm has enabled competitive accuracy, scalability, memory footprint, inference speed, and energy consumption across diverse tasks, including classification, convolution, regression, natural language processing (NLP), and speech understanding.
Tsetlin machines have been particularly successful in NLP. The pioneering NLP approaches use Boolean bag-of-words to represent natural language and logical clauses to capture textual patterns. Recent work addresses text classification, word-sense disambiguation, semantic relation analysis, novelty detection, and aspect-based sentiment analysis.
In this Special Issue, we invite papers that advance the state-of-the-art in NLP with Tsetlin machines. The issue will cover both methodology advances and novel applications.
Prof. Dr. Ole-Christoffer Granmo
Dr. Lei Jiao
Prof. Dr. Vladimir Zadorozhny
Prof. Dr. Morten Goodwin
Guest Editors
Manuscript Submission Information
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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. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
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Keywords
- Tsetlin machine NLP methodology:
- First-order logics
- Embedding
- Boolean representations
- Clustering
- Interpretation
- Convolution
- Novelty detection
- Knowledge representation
- Hawkes processes
- Attention
- Tsetlin machine NLP applications:
- Sentiment analysis
- Question-answering
- Word-sense disambiguation
- Speech understanding
- Chatbots
- Explainable machine learning
- Continuous interpretation of document streams
