Comparison of Different Lemmatization Approaches through the Means of Information Retrieval Performance

2014

Speech and Computer

Self Cite

Abstract. This paper presents our first experiments aimed at the automatic selection of the relevant documents for the blind relevance feedback method in speech information retrieval. Usually the relevant documents are selected only by simply determining the first N documents to be relevant. We consider this approach to be insufficient and we would try in this paper to outline the possibilities of the dynamical selection of the relevant documents for each query depending on the content of the retrieved documents instead of just blindly defining the number of the relevant documents to be used for the blind relevance feedback in advance. We have performed initial experiments with the application of the score normalization techniques used in the speaker identification task, which was successfully used in the multi-label classification task for finding the "correct" topics of a newspaper article in the output of a generative classifier. The experiments have shown promising results, therefore they will be used to define the possibilities of the subsequent research in this area.

Section: Query Likelihood Modelmentioning

confidence: 99%

First Experiments with Relevant Documents Selection for Blind Relevance Feedback in Spoken Document Retrieval

2014

Speech and Computer

Self Cite

“…As a result of these experiments the automatic text lemmatization is also applied in our work. The lemmatization module uses a lemmatizer described in the work [18]. The lemmatizer is automatically created from the data containing the pairs full word form -base word form.…”

Section: System For Acquisition and Storing Datamentioning

confidence: 99%

“…The lemmatizer is automatically created from the data containing the pairs full word form -base word form. A lemmatizer created in this way has been shown to be fully sufficient in the task of information retrieval [18].…”

Section: System For Acquisition and Storing Datamentioning

confidence: 99%

Dynamic Threshold Selection Method for Multi-label Newspaper Topic Identification

Text, Speech, and Dialogue

2013

Self Cite

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.

“…These methods were selected due to the good results in our information retrieval experiments [2], since we had no experience with the topic identification task so far.…”

Section: Identification Algorithmsmentioning

confidence: 99%

“…The appropriate keywords from the first-tier of the tree would then be politics & diplomacy, economy and health. 2 The first three lines of the Table 2 thus describe the language models that were trained using the articles published between January 1st, 2009 and July 17th, 2010 and are labeled with any keyword that comes from the subtree with the headword politics & diplomacy, politics & diplomacy and economy, and politics & diplomacy, economy and health. The results for these topicspecific LMs are compared with the models that are trained from all the articles that were published in the defined period just prior the broadcast day (lines 4 to 6).…”

Section: Language Modeling and Asr Experimentsmentioning

confidence: 99%

Automatic Topic Identification for Large Scale Language Modeling Data Filtering

Text, Speech and Dialogue

Ircing

Pražák

et al. 2011

Self Cite

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.