Deep Neural Network Architecture for Part-of-Speech Tagging for Turkish Language

2018 ◽  
Author(s):  
Cenk Anil Bahcevan ◽  
Emirhan Kutlu ◽  
Tugba Yildiz
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Neural Network Architecture ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Turkish Language ◽  
Speech Tagging

scholarly journals Discontinuous Constituent Parsing with Pointer Networks

2020 ◽  
Vol 34 (05) ◽  
pp. 7724-7731
Author(s):  
Daniel Fernández-González ◽  
Carlos Gómez-Rodríguez
Keyword(s):  
Computational Linguistics ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Wide Margin ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Dependency Trees ◽  
Syntactic Relations ◽  
Dependency Structures ◽  
Speech Tagging

One of the most complex syntactic representations used in computational linguistics and NLP are discontinuous constituent trees, crucial for representing all grammatical phenomena of languages such as German. Recent advances in dependency parsing have shown that Pointer Networks excel in efficiently parsing syntactic relations between words in a sentence. This kind of sequence-to-sequence models achieve outstanding accuracies in building non-projective dependency trees, but its potential has not been proved yet on a more difficult task. We propose a novel neural network architecture that, by means of Pointer Networks, is able to generate the most accurate discontinuous constituent representations to date, even without the need of Part-of-Speech tagging information. To do so, we internally model discontinuous constituent structures as augmented non-projective dependency structures. The proposed approach achieves state-of-the-art results on the two widely-used NEGRA and TIGER benchmarks, outperforming previous work by a wide margin.

scholarly journals Integrated Sequence Tagging for Medieval Latin Using Deep Representation Learning

2017 ◽  
Vol Special Issue on... (Towards a Digital Ecosystem:...) ◽  
Author(s):  
Mike Kestemont ◽  
Jeroen De Gussem
Keyword(s):  
Network Architecture ◽  
Integrated Approach ◽  
Representation Learning ◽  
Context Aware ◽  
Neural Network Architecture ◽  
Medieval Latin ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Pos Tagger ◽  
Speech Tagging

In this paper we consider two sequence tagging tasks for medieval Latin: part-of-speech tagging and lemmatization. These are both basic, yet foundational preprocessing steps in applications such as text re-use detection. Nevertheless, they are generally complicated by the considerable orthographic variation which is typical of medieval Latin. In Digital Classics, these tasks are traditionally solved in a (i) cascaded and (ii) lexicon-dependent fashion. For example, a lexicon is used to generate all the potential lemma-tag pairs for a token, and next, a context-aware PoS-tagger is used to select the most appropriate tag-lemma pair. Apart from the problems with out-of-lexicon items, error percolation is a major downside of such approaches. In this paper we explore the possibility to elegantly solve these tasks using a single, integrated approach. For this, we make use of a layered neural network architecture from the field of deep representation learning.

scholarly journals USE OF GENETIC ALGORITHM IN DEEP NEURAL NETWORKS CONFIGURATION FOR THE PURPOSES OF COMPUTER ATTACKS CLASSIFICATION

2020 ◽  
pp. 104-117
Author(s):  
O.S. Amosov ◽  
◽  
S.G. Amosova ◽  
D.S. Magola ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Classification Problem ◽  
Neural Network Architecture ◽  
Computer Attacks ◽  
Neural Network Technology

The task of multiclass network classification of computer attacks is given. The applicability of deep neural network technology in problem solving has been considered. Deep neural network architecture was chosen based on the strategy of combining a set of convolution and recurrence LSTM layers. Op-timization of neural network parameters based on genetic algorithm is proposed. The presented results of modeling show the possibility of solving the network classification problem in real time.

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