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Word Sense Clustering
Haljuk, Petr ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This Bachelor's thesis deals with the semantic similarity of words . It describes the design and the implementation of a system, which searches for the most similar words and measures the semantic similarity of words . The system uses the Word2Vec model from GenSim library . It learns the relations among words from CommonCrawl corpus .
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Word Sense Clustering
Bárta, Jakub ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This bachelor's thesis deals with the design and implementation of a modular system focused on semantic similarity. System is able to stem the corpus and to analyze corpus in different ways - through coocurrence matrix or LSA.
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Computer as an Intelligent Partner in the Word-Association Game Codenames
Jareš, Petr ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
This thesis solves a determination of semantic similarity between words. For this task is used a combination of predictive model fastText and count based method Pointwise Mutual Information. Thesis describes a system which utilizes semantic models for ability to substitue a player in a word association game Codenames. The system has implemented game strategy enabling use of context information from the game progression to benefit his own team. The system is able to substitue a player in both team roles.
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Word2vec Models with Added Context Information
Šůstek, Martin ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
This thesis is concerned with the explanation of the word2vec models. Even though word2vec was introduced recently (2013), many researchers have already tried to extend, understand or at least use the model because it provides surprisingly rich semantic information. This information is encoded in N-dim vector representation and can be recall by performing some operations over the algebra. As an addition, I suggest a model modifications in order to obtain different word representation. To achieve that, I use public picture datasets. This thesis also includes parts dedicated to word2vec extension based on convolution neural network.
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Word Sense Clustering
Hošták, Viliam Samuel ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This thesis deals with semantic similarity of words. It describes and compares existing models that are currently used for this purpose. It discusses the design and implementation of the system for corpus preprocessing, semantic modelling and retrieval of semantically related words. The system that has been created supports the use of distributional semantic models Word2vec, FastText and Glove.
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Semantic Similarity of Texts
Bradáč, Václav ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This paper deals with the determination of semantic similarity texts, focusing on scalability. Part of treatment is a theoretical overview of the tools to implement the system on test data. Tested corpus contains expert articles in the English language. The aim is to analyze these articles, modified to facilitate the analysis of their semantic analogues. One of the most utilized tools is a representation of data in a vector space model.
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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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Semantic Relatedness of Scientific Articles
Dresto, Erik ; Schmidt, Marek (referee) ; Otrusina, Lubomír (advisor)
The main goal of the thesis is to explore basic methods which can be used to find semantically related scientific articles. All the methods are explained in detail, compared and in the end evaluated by the standard metrics. Based on the evaluation, a new method for computing semantic similarity of scientific articles is proposed. The proposed method is based on the current state-of-the-art methods and adds the another important factor for computing similarity - citations. Using citations is important, since they represent a static bond between the articles. Finally, the proposed method is evaluated on the real data and compared with other described methods.
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