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Semantic disambiguation using Distributional Semantics
Prodanovic, Srdjan ; Hana, Jiří (advisor) ; Vidová Hladká, Barbora (referee)
Ve statistických modelů sémantiky jsou významy slov pouze na základě jejich distribuční vlastnosti.Základní zdroj je zde jeden slovník, který lze použít pro různé úkoly, kde se význam slov reprezentovány jako vektory v vektorového prostoru, a slovní podoby jako vzdálenosti mezi jejich vektorových osobnosti. Pomocí silných podobnosti, může vhodnost podmínek uvedených zejména v souvislosti se vypočítá a používá pro celou řadu úkolů, jeden z nich je slovo smysl Disambiguation. V této práci bylo vyšetřeno několik různých přístupů k modelům z vektorového prostoru a prováděny tak, aby k překročení vyhodnocení vlastního výkonu na Word Sense disambiguation úkolem Prague Dependency Treebank.
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Semantic Similarity of Articles
Veselovský, Martin ; Otrusina, Lubomír (referee) ; Kouřil, Jan (advisor)
This bachelor's thesis deals with modelling of structure of semantic relationships among articles in English language. There are introduced existing methods of articles representation and computation of similarity. The base method is vector space model, which represents document as vector of words. There are given weights of importance to these words using TF-IDF method. Next, there are described advanced methods of modelling, Latent semantic analysis (LSA) and Latent Dirichlet allocation (LDA). This thesis also deals with articles, which are semantically annotated, while weights of annotation words are computed by Stochastic Gradient Descent method. Evaluation of results takes place on the prepared test corpus of documents to which there is reference similarity evaluation.
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Information Retrieval in Text Data
Tkadlčík, Luboš ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This thesis researches the issue of text data mining and information retrieval. It describes the most common representations of text documents and retrieval strategies. The aim of this thesis is design and implementation of application, which realises information retrieval via vector space model. The application implements three different ways of similarity calculation: cosine measure, the Jaccard coefficient and the Dice coefficient. Achieved results are assessed. Possible continuance of the project is outlined.
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Digital Library Information Retrieval
Hochmal, Petr ; Rychlý, Marek (referee) ; Chmelař, Petr (advisor)
This thesis deals with methods of information retrieval. Firstly, it describes models of information retrieval and methods of retrieval evaluation. Then it brings closer the principles of the input text processing for IR with use of stopword list and stemmer. Furthermore, it shows the way of the query expansion with synonyms using the thesaurus, methods of handling phrases appearance in queries and introduces the idea of ranking documents by the degree of phrase occurrence similarity in documents. In the second part of this thesis is described the design of whole IR system with using vector model, query expansion with synonyms and phrases handling. This system has been implemented in C# as the application for retrieving and administration of the documents in digital libraries. The effectiveness of this system has been evaluated at the end of this thesis by several tests.
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Word Sense Clustering
Jadrníček, Zbyněk ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This thesis is focused on the problem of semantic similarity of words in English language. At first reader is informed about theory of word sense clustering, then there are described chosen methods and tools related to the topic. In the practical part we design and implement system for determining semantic similarity using Word2Vec tool, particularly we focus on biomedical texts of MEDLINE database. At the end of the thesis we discuss reached results and give some ideas to improve the system.
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