Post on 02-Jan-2016
Ceyhun Karbeyaz
Çağrı Toraman
Anıl Türel
Ahmet Yeniçağ12.05.2010
Text Categorization For Turkish News
Text categorization
Classify text to predefined categories
Supervised learningLabeled corpus
Used inIndexing (e.g. Libraries)News articlesSpam filtering
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Bilkent News Portal
Gather news from different news providers
News are more accessibleNew event detection and trackingNovelty detectionDublicate eliminationPersonalizationNews Categorization
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Aktuel
En Çok Okunanlar
Anasayfa
Spor
Politika
Çevre
Tüm Haberler
Son Dakika
.......
News
CATEGORIES
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Motivation
News are categorizedFrom Rss
24 good categories
A few bad categoriesAnasayfaEnCokOkunanlarGundemSonDakikaTum_HaberlerYazarlar
AktuelAvrupa_FutbolBilimTeknolojiBilisimCevreDisHaberlerDunyaEgeEgitimEkonomiFormula1Hava_Yol
IspanyaItalyaKulturSanatPolitikaSaglikSiyasetSporTelevizyonTurkiyeYasamYazarlarYurtHaberler
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Approach
ClassifiersK Nearest Neighbour (kNN)Support Vector Machines (SVM)
Use training setNews with good categories
Use test setNews with good categories (for evaluation)
EvaluationTest with already categorized news 7 / 22
Found to be best [1]
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.
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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>”.
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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.
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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.
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Preprocessing
Stemming – Zemberek API is used.
Stop word list comparison – frequently occuring words are not taken into consideration.
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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
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Document Classification
Two different approaches to assess which one performs better:
K-Nearest NeighborSupport Vector Machines
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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
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..
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Support Vector Machines (cntd.)
Figure 2. Possible hyperplanes in a sample space.
Margin of u2
Support Vectors
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Support Vector Machines (cntd.)
Aim: Find a hyperplane correctly classifying with maximal margin,
Support vectors are only effective,
Represent a hyperplane:
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Support Vector Machines (cntd.)
Figure 4. A sample non-linear SVM.
Figure 5. Mapping with kernel function.
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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
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/