Metin Madenciliğine Giriş

Eğitim Şekli

Yüz Yüze

Ön Koşullar, Diğer Koşullar

Yok

Önerilen Destekleyici Dersler

Olasılık ve İstatistik, Veri Madenciliği, Makine Öğrenmesine Giriş

Dersin İçeriği

Metin Madenciliğine Giriş: Karmakarışık Metin Verileri Madenciliği İstatistiksel Doğal Dil İşlemeye Giriş (NLP) Matematiksel Temeller Dilsel Temeller ve Korpus Tabanlı Çalışma Eşdizimli Frekans ile Kollokasyon Seçimi, Hipotez Testleri, Karşılıklı Bilgi İstatistiksel Çıkarım: Seyrek Verilere Göre n-gram Modeller Verilerin veri madenciliği algoritmaları için hazırlanması. Kümeleme Sınıflandırma Web sayfası sınıflandırması

Haftalık Ders İzlencesi

1) Introduction to Text Mining
2) Introduction to Statistical Natural Language Processing (NLP)
3) Mathematical Foundations Elementary Probability Theory Essential Information Theory
4) Linguistic Essentials and Corpus-Based Work Low level Processing of the text corpora Tokenization, Sentence boundary detection, part-of-speech tagging, stemming (Porter’s stemmer algorithm), stop words,
5) Collocations Selection of Collocations by Frequency, Hypothesis Testing, Mutual Information
6) Statistical Inference: n-gram Models over Sparse Data Statistical estimators, combining estimators
7) Statistical Inference: n-gram Models over Sparse Data Statistical estimators, combining estimators
8) Spelling correction and synonyms: edit distance, soundex, language detection. IIR Ch. 3 Techniques for automatically correcting words in text (Kukich 1992) Finding approximate matches in large lexicons (Zobel and Dart 1995) Efficient Generation and Ranking of Spelling Error Corrections (Tillenius) How to write a spelling corrector (Peter Norvig)
9) Preparing our data for data mining algorithms. Index structures. Scoring, term weighting, and the vector space model. tf.idf weighting. The cosine measure
10) Clustering 1 Introduction to the problem. Partitioning methods: k-means clustering
11) Clustering 2 Hierarchical clustering.
12) Classification 1 Introduction to text classification. Naive Bayes models. Spam filtering.
13) Machine learning in automated text categorization (Sebastiani 2002) A re-examination of text categorization methods (Yang et al. 1999) A Comparison of event models for naive Bayes text classification (McCallum et al. 1998)
14) Classification 2 K Nearest Neighbors, Decision boundaries, Vector space classification, Decision Trees. Comparative results. NLP Ch. 16, IIR Ch. 14 Web page classification: Features and algorithms (Qi, Davison 2009) Semi-supervised text classification using EM (Nigam et al. , 2006) Transductive SVMs (Joachims, 1999) Link-based classification (Getoor 2005)
15) Review, examples from real world applications. Term project presentations Evaluation
16) Review, examples from real world applications. Term project presentations Evaluation

Önerilen/İstenen Ders Kaynakları

1- Foundations of Statistical Natural Language Processing, by C. Manning and H. Schütze (2003).
2- Introduction to Information Retrieval, Manning, Raghavan and Schütze, Cambridge University Press (2008)
3- Mining the Web: Discovering Knowledge from Hypertext Data, Chakrabarti (2003)
4- Information Retrieval: A book by C. J. van RIJSBERGEN

Planlanan Öğrenim Faaliyetleri Ve Eğitim Yöntemi

1) Grup Çalışması
2) Bireysel Çalışma
3) Proje Temelli Öğrenme

Değerlendirme Yöntemi ve Ölçütleri

Ara Sınav Notunun Başarıya Oranı	40%
Yarıyıl Sonu Sınavının Başarıya Oranı	60%
Toplam	100%

Dersin Eğitim Dili

Türkçe

Mesleki Uygulama

İstenmemekte