Application of lemmatization and summarization methods in topic identification module for large scale language modeling data filtering

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. © 2012 Springer-Verlag.