Yazar "Sel, Ilhami" seçeneğine göre listele

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    E-Mail Classification Using Natural Language Processing
    (Ieee, 2019) Sel, Ilhami; Hanbay, Davut
    Thanks to the rapid increase in technology and electronic communications, e-mail has become a serious communication tool. In many applications such as business correspondence, reminders, academic notices, web page memberships, e-mail is used as primary way of communication. If we ignore spam e-mails, there remain hundreds of e-mails received every day. In order to determine the importance of received e-mails, the subject or content of each e-mail must be checked. In this study we proposed an unsupervised system to classify received e-mails. Received e-mails' coordinates are determined by a method of natural language processing called as Word2Vec algorithm. According to the similarities, processed data are grouped by k-means algorithm with an unsupervised training model. In this study, 10517 e-mails were used in training. The success of the system is tested on a test group of 200 e-mails. In the test phase M3 model (window size 3, min. Word frequency 10, Gram skip) consolidated the highest success (91%). Obtained results are evaluated in section VI.
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    Feature Selection by Using Heuristic Methods for Text Classification
    (Ieee, 2019) Sel, Ilhami; Yeroglu, Celalettin; Hanbay, Davut
    Feature selection can be defined as the selection of the best subset to represent the data set in machine learning applications, in other words extraction of the unnecessary data that has no effect on the result. In classification problems efficiency and accuracy of the system can be increased when the dimension is reduced by feature selection. In this study, text classifying application is performed by using the data set of 20 News Group released in Reuters News Agent. The pre-processed news data were converted to vectors by using Doc2Vec method and the data set was created and classified by Naive Bayes method. Subsequently, a subset of the data set was formed by using heuristic methods that were inspired by nature (Whale and Gray Wolf Optimization Algorithms) and Chi-square method for feature selection. Then the reclassification was applied and the results were compared. While the success of the system with 600 features before the feature selection is 0.9214, the performance ratio of the 100 featured models created later is figured higher (0.94095 - 0.93833- 0.93619).
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    Feature Selection for Text Classification Using Mutual Information
    (Ieee, 2019) Sel, Ilhami; Karci, Ali; Hanbay, Davut
    The feature selection can be defined as the selection of the best subset to represent the data set, that is, the removal of unnecessary data that does not affect the result. The efficiency and accuracy of the system can be increased by decreasing the size and the feature selection in classification applications. In this study, text classification was applied by using 20 news group data published by Reuters news agency. The pre-processed news data were converted into vectors using the Doc2Vec method and a data set was created. This data set is classified by the Maximum Entropy Classification method. Afterwards, a subset of data sets was created by using the Mutual Information Method for the feature selection. Reclassification was performed with the resulting data set and the results were compared according to the performance rates. While the success of the system with 600 features was (0.9285) before the feature selection, (0.9285), then, the performance rates of the 200, 100, 50, 20 models were obtained as (0.9454, 0.9426, 0.9407, 0.9123), respectively. When the results were examined, the success of the 50-featured model was higher than the 600-featured model initially created.
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    Fully Attentional Network for Low-Resource Academic Machine Translation and Post Editing
    (Mdpi, 2022) Sel, Ilhami; Hanbay, Davut
    English is accepted as an academic language in the world. This necessitates the use of English in their academic studies for speakers of other languages. Even when these researchers are competent in the use of the English language, some mistakes may occur while writing an academic article. To solve this problem, academicians tend to use automatic translation programs or get assistance from people with an advanced level of English. This study offers an expert system to enable assistance to the researchers throughout their academic article writing process. In this study, Turkish which is considered among low-resource languages is used as the source language. The proposed model combines the transformer encoder-decoder architecture model with the pre-trained Sci-BERT language model via the shallow fusion method. The model uses a Fully Attentional Network Layer instead of a Feed-Forward Network Layer in the known shallow fusion method. In this way, a higher success rate could be achieved by increasing the attention at the word level. Different metrics were used to evaluate the created model. The model created as a result of the experiments reached 45.1 BLEU and 73.2 METEOR scores. In addition, the proposed model achieved 20.12 and 20.56 scores, respectively, with the zero-shot translation method in the World Machine Translation (2017-2018) test datasets. The proposed method could inspire other low-resource languages to include the language model in the translation system. In this study, a corpus composed entirely of academic sentences is also introduced to be used in the translation system. The corpus consists of 1.2 million parallel sentences. The proposed model and corpus are made available to researchers on our GitHub page.