Classification of contradictory opinions in text using deep learning methods
tarafından
 
Oğul, İskender Ülgen, author.

Başlık
Classification of contradictory opinions in text using deep learning methods

Yazar
Oğul, İskender Ülgen, author.

Yazar Ek Girişi
Oğul, İskender Ülgen, author.

Fiziksel Tanımlama
x, 61 leaves: charts;+ 1 computer laser optical disc.

Özet
Natural language inference (NLI) problem aims to ensure consistency as well as accuracy of propositions while making sense of natural language. Natural language inference aims to classify the relationship between two given sentences as contradiction, entailment or neutrality. To accomplish the classification task, sentences or words must be translated into mathematical representations called vectors or embedding. Vectorization of a sentence is as important as the complexity of the classification model. In this study, both pre-trained (Glove, Fasttext, Word2Vec) and contextual word embedding methods (BERT) were used for comparison and acquire the best result. One of the natural language processing tasks NLI, is highly complex and requires solutions. Conventional machine learning methods are insufficient to carry out natural language processing solutions. Therefore, more advanced solutions are required. This study used deep learning methods to perform the classification task. Unlike conventional machine learning approaches, deep learning approaches reduce errors while increasing accuracy by repeating the data many times. Opinion sentences have complex grammatical structures that are difficult to classify. This study used Decomposable Attention and Enhanced LSTM for natural language inference to perform NLI classification task. Using the advanced LSTM deep learning method and Bert contextual vectors for natural language extraction on the SNLI dataset, an accuracy result 88.0% very close state of the art result 92.1% was obtained. In order to show the usability of the developed solution in different NLI tasks, an accuracy of 80.02% was obtained in the studies performed on the MNLI data set.

Konu Başlığı
Machine learning.
 
Learning classifier systems.
 
Artificial intelligence.

Yazar Ek Girişi
Tekir, Selma,

Tüzel Kişi Ek Girişi
İzmir Institute of Technology. Computer Engineering.

Tek Biçim Eser Adı
Thesis (Master)--İzmir Institute of Technology: Computer Engineering.
 
İzmir Institute of Technology: Computer Engineering--Thesis (Master).

Elektronik Erişim
Access to Electronic Versiyon.


LibraryMateryal TürüDemirbaş NumarasıYer Numarası
IYTE LibraryTezT002248Q325.5 .O35 2020