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Named Entity Recognition and Linking
Taufer, Pavel ; Straka, Milan (advisor) ; Kliegr, Tomáš (referee)
The goal of this master thesis is to design and implement a named entity recognition and linking algorithm. A part of this goal is to propose and create a knowledge base that will be used in the algorithm. Because of the limited amount of data for languages other than English, we want to be able to train our method on one language, and then transfer the learned parameters to other languages (that do not have enough training data). The thesis consists of description of available knowledge bases, existing methods and design and implementation of our own knowledge base and entity linking method. Our method achieves state of the art result on a few variants of the AIDA CoNLL-YAGO dataset. The method also obtains comparable results on a sample of Czech annotated data from the PDT dataset using the parameters trained on the English CoNLL dataset. Powered by TCPDF (www.tcpdf.org)
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Processing of Turkic Languages
Ciddi, Sibel ; Zeman, Daniel (advisor) ; Hlaváčová, Jaroslava (referee)
Title: Processing of Turkic Languages Author: Sibel Ciddi Department: Institute of Formal and Applied Linguistics, Faculty of Mathematics and Physics, Charles University in Prague Supervisor: RNDr. Daniel Zeman, Ph.D. Abstract: This thesis presents several methods for the morpholog- ical processing of Turkic languages, such as Turkish, which pose a specific set of challenges for natural language processing. In order to alleviate the problems with lack of large language resources, it makes the data sets used for morphological processing and expansion of lex- icons publicly available for further use by researchers. Data sparsity, caused by highly productive and agglutinative morphology in Turkish, imposes difficulties in processing of Turkish text, especially for meth- ods using purely statistical natural language processing. Therefore, we evaluated a publicly available rule-based morphological analyzer, TRmorph, based on finite state methods and technologies. In order to enhance the efficiency of this analyzer, we worked on expansion of lexicons, by employing heuristics-based methods for the extraction of named entities and multi-word expressions. Furthermore, as a prepro- cessing step, we introduced a dictionary-based recognition method for tokenization of multi-word expressions. This method complements...
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Named Entity Recognition
Rylko, Vojtěch ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
In this master thesis are described the history and theoretical background of named-entity recognition and implementation of the system in C++ for named entity recognition and disambiguation. The system uses local disambiguation method and statistics generated from the Wikilinks web dataset. With implemented system and with alternative implementations are performed various experiments and tests. These experiments show that the system is sufficiently successful and fast. System participates in the Entity Recognition and Disambiguation Challenge 2014.
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Extracting Structured Data from Czech Web Using Extraction Ontologies
Pouzar, Aleš ; Svátek, Vojtěch (advisor) ; Labský, Martin (referee)
The presented thesis deals with the task of automatic information extraction from HTML documents for two selected domains. Laptop offers are extracted from e-shops and free-published job offerings are extracted from company sites. The extraction process outputs structured data of high granularity grouped into data records, in which corresponding semantic label is assigned to each data item. The task was performed using the extraction system Ex, which combines two approaches: manually written rules and supervised machine learning algorithms. Due to the expert knowledge in the form of extraction rules the lack of training data could be overcome. The rules are independent of the specific formatting structure so that one extraction model could be used for heterogeneous set of documents. The achieved success of the extraction process in the case of laptop offers showed that extraction ontology describing one or a few product types could be combined with wrapper induction methods to automatically extract all product type offers on a web scale with minimum human effort.
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