Skorkovská, Lucie

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49Studentská V K 2010ìdecká onferenceFIRST EXPERIMENTS WITH AUTOMATIC TOPICIDENTIFICATION OF CZECH NEWSPAPER ARTICLESLucie SKORKOVSKA´ 11 INTRODUCTIONTopic identification module is an important part of the system for generation of languagemodels with the use of internet data (in development at the Department of Cybernetics).A language model is an essential component of every automatic speech recognition systemand it requires large amounts of data to be well trained. Our effort is to use the largepotential of the internet for gathering the topic oriented training data.We intend to use articles from internet newspapers to train the topic oriented languagemodels. As a typical article from most newspapers has no assigned keywords and noprecisely defined topic, we have to automatically identify the topic of each article.Our goal is to automatically identify the topic of an article from among the set oftopics. For our experiments we used data from news server Cˇeske´ Noviny . The articleshere have manually specified keywords. We have used 10412 articles in our test, 9000 astraining data, the rest as test data. The training articles contained 3434 keywords - topicsfor our purpose.2 REALIZATIONFor the first experiments the Naive Bayes classifier was chosen (Manning et al. (2008)),where the probability P (T |A) of an article A belonging to a class (topic in our case) T iscomputed asP (T |A) ∝ P (T )∏t∈AP (t|T ) (1)where P (T ) is the prior probability of a topic T and P (t|T ) is a conditional probability ofa term t given the topic T . This probability can be estimated by the maximum likelihoodestimate (MLE) simply as the relative frequency of the term t in the training articlesbelonging to the topic T :Pˆ (t|T ) =tft,TNT(2)where tft,T is the frequency of the term t in T and NT is the total number of tokens inarticles of the topic T .The goal of this language modeling based approach is to find the most likely or themaximum a posteriori topic Tmap of an article A:Tmap = arg maxT Pˆ (T |A) = arg maxT Pˆ (T )∏t∈APˆ (t|T ). (3)1Ing. Lucie Skorkovska´, student of the doctoral study programme Applied Sciences and Informatics,specialization Cybernetics, e-mail: lskorkov@kky.zcu.cz50L. Skorkovska´The linear interpolation smoothing method (Jelinek and Mercer (1980)) was also im-plemented, where the computation of the probability P (T |A) is:P (T |A) ∝ P (T )∏t∈A(λP (t|T ) + (1− λ)P (t|MC)) (4)where P (t|MC) is a conditional probability of a term t in the whole article collection andλ is an interpolation parameter.3 CONCLUSIONThese first experiments with topic identification have disclosed several findings usable forthe future research. First, the experiments have shown that automatic use of all keywordsas topics is not suitable - many of them are too detailed to have enough articles assignedto proper model the topic. So for the future work the development of some keywordsclusters is needed.Second, the results have shown that the use of the prior probability of a topic has notaffected the identification process. Identified topics and also their order is the same aswhen P (T ) is considered equal for all topics, so it can be ignored in future experiments.Finally, as smoothing is considered to be an essential part of the language modelingapproach to the information retrieval, it was expected to improve topic identification aswell. On the contrary, the first analysis of the results of our experiments has shown thatsmoothing may not be necessary in this task. Deeper examination of this hypothesis is asuitable matter for further research.Acknowledgement: The work has been supported by the grant of The University ofWest Bohemia, project No. SGS-2010-054 - ”Inteligentn´ı metody strojove´ho vn´ıma´n´ı aporozumeˇn´ı”REFERENCESCˇeske´ Noviny. www.ceskenoviny.cz.Manning, Christopher D., Raghavan, Prabhakar and Schu¨tze, Hinrich, 2008. Chapter 13- Text classification & Naive Bayes. Introduction to Information Retrieval. CambridgeUniversity Press, New York.Jelinek, Frederick and Mercer, Robert L., 1980. Interpolated estimation of Markov sourceparameters from sparse data. Proceedings of the Workshop on Pattern Recognition inPractice, Amsterdam, The Netherlands: North-Holland.

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First experiments with automatic topic identification of czech newspaper articles

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First experiments with automatic topic identification of czech newspaper articles

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