Gender prediction from Tweets with convolutional neural networks: Notebook for PAN at CLEF 2018

19th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2018; Avignon; France; 10 September 2018 through 14 September 2018This paper presents a system1 developed for the author profiling task of PAN at CLEF 2018. The system utilizes style-based features to predict the gender information from the given tweets of each user. These features are automatically extracted by Convolutional Neural Networks (CNN). The system mainly depends on the idea that the informativeness of each tweet is not the same in terms of the gender of a user. Thus, the attention mechanism is included to the CNN outputs in order to discriminate the tweets carrying more information. Our architecture was able to obtain competitive results on three languages provided by the PAN 2018 author profiling challenge with an average accuracy of 75.1% on local runs and 70.23% on the submission run

Nanomaterials Based Drinking Water Purification: Comparative Study with a Conventional Water Purification Process

One of the ways of fully securing the presence of fresh water is water treatment process. Nanomaterials and nanotechnology offers an innovative solution for water treatment. In this study, physical, chemical and microbiological improvement rates of raw water were analyzed after filtration with graphene oxide. Graphene oxide's water treatment performance; silver nanoparticles, silver nanoparticles & graphene oxide composites that are commonly used in water treatment were compared with a traditional treatment method. When compared to the traditional method, there were improvements of 50 %, 40.7 %, 86.8 % and 45.5 % for color, TIC, TOC and hardness properties, respectively in water treatment by GO-based filtration with solid liquid ratio of 0.7 % (v/v). In water treatment with GO-Ag based filtration, 39.8 %, 69.8 %, 10.3 % and 28.6 % of improvements were obtained for TIC, TOC, hardness and LSI value compared to the conventional method. Both GO at 0.7 % (v/v) solid-liquid ratio and GO-Ag nanocomposites were successful in the number of total viable microorganisms and inhibiting microorganisms such as Escherichia coli fecal (gaita-infected), Salmonella typhi, Enterococcus faecalis, Pseudomona aeruginosa and Staphylococcus aureus. Among the studied parameters GO-Ag nanocomposites found to be the most suitable for drinking water treatment

Doğal dil işlemede semantik gösterimlerin sistematik değerlendirilmesi

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2018Includes bibliographical references (leaves: 67-71)Text in English; Abstract: Turkish and EnglishIn the studies of semantics, the main aim is to address meaning. In a computational manner, this goal is accomplished through the encoding of language constructs. These encodings are in the form of information-theoretic measures and vector representations. We have focused on the representation of words. In word representations, the earlier approaches depend on counting the statistics between word and its accompanied words, whereas the current methods are based on learning approaches. At this point, we have investigated the relation between these two approaches. We have realized that both approaches use context as the normalization factor. We support our idea by evaluating word representations on some Natural Language Processing (NLP) tasks. Furthermore, we have studied the polysemous words which carry more than one meaning. The word representation of the polysemous word provides a representation that covers more than one meaning. To overcome this issue, we provide a method to create a representation for each sense of polysemous word.Semantik çalışmalarında, temel amaç anlamı ele almaktır. Hesaplamalı yöntemlerde, bu hedef dil yapılarının kodlanması ile gerçekleştirilir. Bu kodlamalar istatistiksel ve vektör gösterimleri şeklindedir. Çalışmada kelime gösterimleri üzerine odaklanılmıştır. Kelime gösterimlerinde, önceki çalışmalar kelime ve onun eşlik ettiği kelimeler arasındaki istatistiklerin sayılmasına dayanırken, mevcut yöntemler öğrenme tabanlıdır. Tezde bu iki yaklaşım arasındaki ilişki araştırılmıştır. Her iki yaklaşımın da bağlamı normalleştirme faktörü olarak kullandığı görülmüştür. Bu fikir, kelime gösterimlerinin bazı Doğal Dil İşleme problemlerinde değerlendirilmesi ile desteklenmiştir. Ayrıca, birden fazla anlam taşıyan çok-anlamlı kelimeler üzerine çalışılmıştır. Çok-anlamlı bir kelimenin kelime gösterimi, birden fazla anlamını kapsamaktadır. Bu sorunu aşmak için, çok-anlamlı kelimenin her bir anlamı için ayrı bir gösterim sağlayan bir yöntem geliştirilmiştir

Doğal dil işlemede semantik gösterimlerin sistematik değerlendirilmesi

Thesis (Master)--Izmir Institute of Technology, Computer Engineering, Izmir, 2018Includes bibliographical references (leaves: 67-71)Text in English; Abstract: Turkish and EnglishIn the studies of semantics, the main aim is to address meaning. In a computational manner, this goal is accomplished through the encoding of language constructs. These encodings are in the form of information-theoretic measures and vector representations. We have focused on the representation of words. In word representations, the earlier approaches depend on counting the statistics between word and its accompanied words, whereas the current methods are based on learning approaches. At this point, we have investigated the relation between these two approaches. We have realized that both approaches use context as the normalization factor. We support our idea by evaluating word representations on some Natural Language Processing (NLP) tasks. Furthermore, we have studied the polysemous words which carry more than one meaning. The word representation of the polysemous word provides a representation that covers more than one meaning. To overcome this issue, we provide a method to create a representation for each sense of polysemous word.Semantik çalışmalarında, temel amaç anlamı ele almaktır. Hesaplamalı yöntemlerde, bu hedef dil yapılarının kodlanması ile gerçekleştirilir. Bu kodlamalar istatistiksel ve vektör gösterimleri şeklindedir. Çalışmada kelime gösterimleri üzerine odaklanılmıştır. Kelime gösterimlerinde, önceki çalışmalar kelime ve onun eşlik ettiği kelimeler arasındaki istatistiklerin sayılmasına dayanırken, mevcut yöntemler öğrenme tabanlıdır. Tezde bu iki yaklaşım arasındaki ilişki araştırılmıştır. Her iki yaklaşımın da bağlamı normalleştirme faktörü olarak kullandığı görülmüştür. Bu fikir, kelime gösterimlerinin bazı Doğal Dil İşleme problemlerinde değerlendirilmesi ile desteklenmiştir. Ayrıca, birden fazla anlam taşıyan çok-anlamlı kelimeler üzerine çalışılmıştır. Çok-anlamlı bir kelimenin kelime gösterimi, birden fazla anlamını kapsamaktadır. Bu sorunu aşmak için, çok-anlamlı kelimenin her bir anlamı için ayrı bir gösterim sağlayan bir yöntem geliştirilmiştir

Gender prediction from Tweets with convolutional neural networks: Notebook for PAN at CLEF 2018

19th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2018; Avignon; France; 10 September 2018 through 14 September 2018This paper presents a system1 developed for the author profiling task of PAN at CLEF 2018. The system utilizes style-based features to predict the gender information from the given tweets of each user. These features are automatically extracted by Convolutional Neural Networks (CNN). The system mainly depends on the idea that the informativeness of each tweet is not the same in terms of the gender of a user. Thus, the attention mechanism is included to the CNN outputs in order to discriminate the tweets carrying more information. Our architecture was able to obtain competitive results on three languages provided by the PAN 2018 author profiling challenge with an average accuracy of 75.1% on local runs and 70.23% on the submission run