Abstract. This paper presents a quantitative performance analysis of two different approaches to the lemmatization of the Czech text data. The first one is based on manually prepared dictionary of lemmas and set of derivation rules while the second one is based on automatic inference of the dictionary and the rules from training data. The comparison is done by evaluating the mean Generalized Average Precision (mGAP) measure of the lemmatized documents and search queries in the set of information retrieval (IR) experiments. Such method is suitable for efficient and rather reliable comparison of the lemmatization performance since a correct lemmatization has proven to be crucial for IR effectiveness in highly inflected languages. Moreover, the proposed indirect comparison of the lemmatizers circumvents the need for manually lemmatized test data which are hard to obtain and also face the problem of incompatible sets of lemmas across different systems.

Abstract. The paper presents a module for topic identification that is embedded into a complex system for acquisition and storing large volumes of text data from the Web. The module processes each of the acquired data items and assigns keywords to them from a defined topic hierarchy that was developed for this purposes and is also described in the paper. The quality of the topic identification is evaluated in two ways -using classic precision-recall measures and also indirectly, by measuring the ASR performance of the topic-specific language models that are built using the automatically filtered data.

Abstract. Nowadays, the multi-label classification is increasingly required in modern categorization systems. It is especially essential in the task of newspaper article topics identification. This paper presents a method based on general topic model normalisation for finding a threshold defining the boundary between the "correct" and the "incorrect" topics of a newspaper article. The proposed method is used to improve the topic identification algorithm which is a part of a complex system for acquisition and storing large volumes of text data. The topic identification module uses the Naive Bayes classifier for the multiclass and multi-label classification problem and assigns to each article the topics from a defined quite extensive topic hierarchy -it contains about 450 topics and topic categories. The results of the experiments with the improved topic identification algorithm are presented in this paper.

Abstract. The paper presents experiments with the topic identification module which is a part of a complex system for acquisition and storing large volumes of text data. The topic identification module processes each acquired data item and assigns it topics from a defined topic hierarchy. The topic hierarchy is quite extensive -it contains about 450 topics and topic categories. It can easily happen that for some narrowly focused topic there is not enough data for the topic identification training. Lemmatization is shown to improve the results when dealing with sparse data in the area of information retrieval, therefore the effects of lemmatization on topic identification results is studied in the paper. On the other hand, since the system is used for processing large amounts of data, a summarization method was implemented and the effect of using only the summary of an article on the topic identification accuracy is studied.

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