Identifying textual personal information with artificial neural networks

Download

index.pdf

Date

2019

Author

Demir, Memduh Çağrı

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

277
views

71
downloads

Solutions to many natural language processing problems need language-specific labeled data to be learned. However, both the endeavor of compiling a new dataset in a new language and the practice of translating an existing dataset to another language require human expert effort which can not be automated. To learn a solution in a new target language in an automated manner without any extra data, we focus on the known problem of dialogue act classification and propose two solutions that combine existing dialogue act classification methods with machine translation techniques. We implement the proposed solutions Localized Dialogue Act Classifier (LDAC) and Universal Dialogue Act Classifier (UDAC) using two different dialogue act classification methods, and a state-of-the-art machine translation method. We test both solutions on two datasets that are frequently used in testing a dialogue act classification method, namely Switchboard Dialogue Act (SwDA) and Meeting Recorder Dialogue Act (MRDA) datasets, and use German, Spanish and Turkish as the target languages. The results show that the models trained on translated datasets perform worse than their monolingual counterparts, trained on a dataset in its original language. Nonetheless, the results also indicate that acceptably accurate dialogue act classification is achieved on new target languages by LDAC, without having a dataset in that language. These results show that the automated dataset translation idea we propose deserves further exploration.

Subject Keywords

Neural networks (Computer science)., Keywords: De-identification of plain texts, text classification, local contexts of words, long short term memory networks.

URI

http://etd.lib.metu.edu.tr/upload/12623666/index.pdf
https://hdl.handle.net/11511/44123

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Zero shot dialogue act classification Uğur, ̇İlim; Üçoluk, Göktürk; Department of Computer Engineering (2019) Solutions to many natural language processing problems need language-specific labeled data to be learned. However, both the endeavor of compiling a new dataset in a new language and the practice of translating an existing dataset to another language require human expert effort which can not be automated. To learn a solution in a new target language in an automated manner without any extra data, we focus on the known problem of dialogue act classification and propose two solutions that combine existing dialo...
Identifying textual personal information using bidirectional LSTM networks Ertekin Bolelli, Şeyda (2018-07-09) Data-driven approaches based on the data collected from individuals are improving everyday life as a result of the developments in big data studies. Prior to developing such an approach, removal of personal information from the data is important since personal information contained in data would jeopardize people's privacy and may harm related individuals. Especially in the field of health sciences, identifying personal information in the collected data is a difficult task as most of the data collected in h...
Representing temporal knowledge in connectionist expert systems Alpaslan, Ferda Nur (1996-09-27) This paper introduces a new temporal neural networks model which can be used in connectionist expert systems. Also, a Variation of backpropagation algorithm, called the temporal feedforward backpropagation algorithm is introduced as a method for training the neural network. The algorithm was tested using training examples extracted from a medical expert system. A series of experiments were carried out using the temporal model and the temporal backpropagation algorithm. The experiments indicated that the alg...
Identifying preferred solutions in multiobjective combinatorial optimization problems Lokman, Banu (2019-01-01) We develop an evolutionary algorithm for multiobjective combinatorial optimization problems. The algorithm aims at converging the preferred solutions of a decision-maker. We test the performance of the algorithm on the multiobjective knapsack and multiobjective spanning tree problems. We generate the true nondominated solutions using an exact algorithm and compare the results with those of the evolutionary algorithm. We observe that the evolutionary algorithm works well in approximating the solutions in the...
Deep learning approach for laboratory mice grimace scaling Eral, Mustafa; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2016) Deep learning is extremely attractive research topic in pattern recognition and machine learning areas. Applications in speech recognition, natural language processing, and machine vision fields gained huge acceleration in performance by employing deep learning. In this thesis, deep learning is used for medical purposes in order to scale pain degree of drug stimulated mice by examining facial grimace. For this purpose each frame in the videos in the training set were scaled manually by experts according to ...

Citation Formats

M. Ç. Demir, “Identifying textual personal information with artificial neural networks,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Computer Engineering., Middle East Technical University, 2019.