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Module for Management of Settings of NLPIS System
Linhart, Lukáš ; Otrusina, Lubomír (referee) ; Dytrych, Jaroslav (advisor)
This master's thesis is focused to create a module for management of settings of NLPIS system. The module will serve to make using of the NPLIS system easier, focusing especially on the session management improvements. The topic contains introduction of the issue, description of the NLPIS system and its technologies, as well as various solution drafts and final implementations including testing are covered in the thesis. The main focus is on improving user experience by simplifying the interface and hence making their work more effective.
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Semantic Similarity of Texts
Hajdin, Martin ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This paper deals with the determination of the semantic similarity of texts focusing on categorization of web documents in this case bookmarks. The part of the process is a theoretical overview of methods for system implementation. It describes the design and implementation of the various methods used in the system, too. This paper also deals with the evaluation of various methods where the chosen method are tested according to specified criteria.
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Information Extraction from Wikipedia
Musil, Martin ; Otrusina, Lubomír (referee) ; Schmidt, Marek (advisor)
This bachelor thesis deals with the problem of automatic information extraction from text. Goal is to create an application, which captures knowledge out of the articles from online information server Wikipedia, using extraction patterns. At the beginning, we interpret the basic terms of the subject and the main part of the publication is focused to the experiments and above all to the implementation, divided into two parts, processing of the text and following information extraction. The conclusion of the thesis analyses the results coming from experiments and efficiency of created rules.
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Machine-Learning in Natural Language Processing
Otrusina, Lubomír ; Šilhavá, Jana (referee) ; Smrž, Pavel (advisor)
This beachelor's thesis deals with word sense disambiguation problem using the machine learning techniques. There are shortly presented problems of word sense disambiguation and its timeline. There are described methods and approaches, especially the naive Bayes classifier that is implemented in the system. There's illustrated a simple example of using this classifier. In a practical section is described project of system based on naive Bayes classifier including description of various algorithms used in the system. Finally there are described evaluation and analysis of the system. This created system took part in an international competition on semantic evaluation workshop SemEval-2007.
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Machine-Learning Methods in Natural Language Processing
Vantuch, Marek ; Mrnuštík, Michal (referee) ; Otrusina, Lubomír (advisor)
Firstly, basic rules of tagging of the Czech language are described as well as problems connected to this field. Thereafter the focus of the thesis is put on the success rate of testing on the Czech corpus and at the same time trying to find the most suitable parameter values for using the features. After reaching a reasonable compromise between duration and accuracy, the value is then attempted to be improved using analysis of separate features and their eventual omission.
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Automatic Identification of Paraphrases
Otrusina, Lubomír ; Schwarz, Petr (referee) ; Smrž, Pavel (advisor)
Automatic paraphrase discovery is an important task in natural language processing. Many systems use paraphrases for improve performance e.g. systems for question answering, information retrieval or document summarization. In this thesis, we explain basic concepts e.g. paraphrase or paraphrase pattern. Next we propose some methods for paraphrase discovery from various resources. Subsequently we propose an unsupervised method for discovering paraphrase from large plain text based on context and keywords between NE pairs. In the end we explain evaluation metods in paraphrase discovery area and then we evaluate our system and compare it with similar systems.
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Question Answering over Structured Data
Birger, Mark ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
Tato práce se zabývá problematikou odpovídání na otázky nad strukturovanými daty. Ve většině případů jsou strukturovaná data reprezentována pomocí propojených grafů, avšak ukrytí koncové struktury dát je podstatné pro využití podobných systémů jako součástí rozhraní s přirozeným jazykem. Odpovídající systém byl navržen a vyvíjen v rámci této práce. V porovnání s tradičními odpovídajícími systémy, které jsou založené na lingvistické analýze nebo statistických metodách, náš systém zkoumá poskytnutý graf a ve výsledků generuje sémantické vazby na základě vstupních párů otázka-odpověd'. Vyvíjený systém je nezávislý na struktuře dát, ale pro účely vyhodnocení jsme využili soubor dát z Wikidata a DBpedia. Kvalita výsledného systému a zkoumaného přístupu byla vyhodnocena s využitím připraveného datasetu a standartních metrik.
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Automatic Keyword Extraction in Czech
Gallovič, Ľubomír ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This thesis describes design, implementation and testing of application for automatic keyterm extraction from technical texts in czech language. Multiple algorithms for candidate selection, as well as various statistical and linguistic methods for score calculation were implemented. All of these algorithms were analyzed and compared, and best performing ones were chosen to be included in the final version of the program.
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Conversion of Science Articles to Plain Text
Matička, Jiří ; Dytrych, Jaroslav (referee) ; Otrusina, Lubomír (advisor)
Purpose of this bachelor's work is a research in the area of converting scientific articles in electronic form to plain text. Main topic is the group of problematic articles with certain possible components causing non-acceptable output. Many conversion tools were investigated and the one with the required and most accurate conversion was chosen. Second part of this thesis examines the problematic of automated conversion, including creation of conversion request, forward of all articles to conversion, the conversion itself, detection of finished conversions and delivery of all converted articles. To achieve this objective, a communication principle based on client/server in conjuction with Python scripts and available needed libraries were created. From the client's point of view, it is required only to create a list of articles for conversion and then call the appropriate function (create a request). Rest of the process is taken care of automatically and the resulting text files are available for the client in a folder set beforehand.
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