Ceyhun Karbeyaz Çağrı Toraman Anıl Türel Ahmet Yeniçağ

1. Text Categorization For Turkish News • Ceyhun Karbeyaz • Çağrı Toraman • Anıl Türel • Ahmet Yeniçağ 12.05.2010

2. Text categorization • Classify text to predefined categories • Supervised learning • Labeled corpus • Used in • Indexing (e.g. Libraries)‏ • News articles • Spam filtering 11.5.2010 1 / 22

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4. Bilkent News Portal • Gather news from different news providers • News are more accessible • New event detection and tracking • Novelty detection • Dublicate elimination • Personalization • News Categorization 3 / 22

5. CATEGORIES Tüm Haberler Çevre Son Dakika News Politika Anasayfa Spor En Çok Okunanlar Aktuel ....... 4 / 22

6. Motivation • News are categorized • From Rss • 24 good categories • A few bad categories • Anasayfa • EnCokOkunanlar • Gundem • SonDakika • Tum_Haberler • Yazarlar Aktuel Avrupa_Futbol BilimTeknoloji Bilisim Cevre DisHaberler Dunya Ege Egitim Ekonomi Formula1 Hava_Yol Ispanya Italya KulturSanat Politika Saglik Siyaset Spor Televizyon Turkiye Yasam Yazarlar YurtHaberler 5 / 22

7. Data Set • Categories are skewed (not homogene)‏ 6 / 22

8. Approach • Classifiers • K Nearest Neighbour (kNN)‏ • Support Vector Machines (SVM)‏ • Use training set • News with good categories • Use test set • News with good categories (for evaluation)‏ • Evaluation • Test with already categorized news Found to be best [1] 7 / 22

9. Methodology • Cleaning Noises • Preprocessing • Indexing • Document Classification 8 / 22

10. Cleaning Noises • News documents coming from different RSS feeds generally contain noises such as advertisements, hypertexts, etc. • Increase the similarity between documents which contain the same or similar noises • Decrease in the performance of the systems as Bilkent News Portal, which uses similarity between documents. 9 / 22

11. Cleaning Noises (cntd.) • Cleaning process of noises such as hypertexts is easily done by removing the sentences contain these noises. • Their pattern do not change for each news document coming from different RSS feeds. • E.g. hypertexts, which contain links to other documents, are defined as “<a href="http://…" ></a>”. 10 / 22

12. Cleaning Noises (cntd.) • Each RSS feed attaches specific advertisements to its news documents. • No general pattern for all news documents. • After a while, even the same RSS feed changes the advertisement being attached to its news documents. 11 / 22

13. Cleaning Noises (cntd.) • Compare two consecutive news documents from the same RSS feed sentence-by-sentence. (Each sentence is compared with every sentence of the consecutive news document) • Calculate the similarity between each sentence by using Cosine Similarity. 12 / 22

14. Preprocessing • Stemming – Zemberek API is used. • Stop word list comparison – frequently occuring words are not taken into consideration. 13 / 22

15. Document Indexing • Creating vector space model from index terms + feature selection may be a costly process. • Consistency with Bilkent News Portal • Lemur[2]for document indexing operation. • Lemur can only index predefined formats • TREC text 14 / 22

16. Document Classification • Two different approaches to assess which one performs better: • K-Nearest Neighbor • Support Vector Machines 15 / 22

17. K Nearest Neighbor • Given training data D (categorized news in our case), goal is to assign test point X (news with unknown category in our case) to label of associated closest neighbors in D. • As distance function to specify k nearest news, we again used Lemur. • Lemur can also retrieve documents according to some score calculation. • Lemur is quite fast at retrieving k similar documents. 16 / 22

18. Support Vector Machines • Support Vector Machines (SVMs), • Applied to various problems, • Data in k-dim space, • Find a hyperplane (i.e subset with k-1 dim), • Several possible hyperplanes.. 17 / 22

19. Support Vector Machines (cntd.)‏ Support Vectors Margin of u2 Figure 2. Possible hyperplanes in a sample space. 18 / 22

20. Support Vector Machines (cntd.)‏ • Aim: Find a hyperplane correctly classifying with maximal margin, • Support vectors are only effective, • Represent a hyperplane: 19 / 22

21. Support Vector Machines (cntd.)‏ Figure 3. A sample linear SVM. 20 / 22

22. Support Vector Machines (cntd.)‏ Figure 5. Mapping with kernel function. Figure 4. A sample non-linear SVM. 21 / 22

23. Conclusion • The necessity of Turkish news categorization is covered. • Bilkent News Portal: RSS feeds may lack category information or having unrealistic categories such as Last Minute, Main Page, Agenda etc. • A categorization methodology for Turkish news is proposed. • Finding the correct category is done both by KNN (base classifier) and SVM to evaluate which one performs better. • KNN for 100 experiments, 60% success. 22 / 22

24. References [1] Yang, Y. and Liu, X., A re-examination of text categorization methods. Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval, (SIGIR), 1999 [2] http://www.lemurproject.org/

25. Questions? • Thank you for listening…