|
|
Wikipedia Page Classification
Suchý, Ondřej ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
The goal of this paper is to design and implement a system for selection of Wikipedia articles relevant to a given topic in order to reduce the amount of memory taken by its offline version. The solution of this problem was achieved with use of methods from information retrieval and theirs implementation using Elasticsearch search engine. The system tries to determine the area of user's interest by given keywords and make a selection of articles from that area. This is achieved by measuring of similarity of articles and adding all articles from frequent categories in the selection. The sizes of the output files for queries over Simple English Wikipedia are usually below 30 MB.
|
|
| |
|
|
Dialogue System for Human-Robot Communication
Birger, Mark ; Materna, Zdeněk (referee) ; Smrž, Pavel (advisor)
In this thesis a problematic of spoken dialog systems was discovered. The dialog system framework was developed for a fast implementation of spoken dialog interfaces for existing robotics software. This framework allows describing a dialog flow in special markup format, which allows scope variables manipulating and controlling a flow of general-purpose programming language software by user input phrase. Markup language is designed for asynchronous function execution and subsequent manipulations with them. It allows robot to solve tasks simultaneously. Developed framework uses Link Grammar Parser for natural language processing. With this framework was implemented a dialog system instance for PR2 robot control.
|
|
|
Advanced Songbook for Mobile Devices
Komárek, Ondřej ; Škoda, Petr (referee) ; Smrž, Pavel (advisor)
This Thesis describes all way from design and implementation to release and advertising of songbook application for Android operation system. The most important goal of the app is the ability to modify lyrics in such way, that they can be viewed flawlessly even on small screens of mobile devices. Application also features many other functions mostly useful for guitar players. User can download lyrics of song with chords directly from app and set up automatic scrolling with different speed for each part of the song. Thesis also describes other important aspects of application development, like user testing and final app release.
|
|
| |
|
|
Methods of Information Extraction
Adamček, Adam ; Smrž, Pavel (referee) ; Kouřil, Jan (advisor)
The goal of information extraction is to retrieve relational data from texts written in natural human language. Applications of such obtained information is wide - from text summarization, through ontology creation up to answering questions by QA systems. This work describes design and implementation of a system working in computer cluster which transforms a dump of Wikipedia articles to a set of extracted information that is stored in distributed RDF database with a possibility to query it using created user interface.
|
|
|
Optimization of Aircraft Tracker Parameters
Samek, Michal ; Vlk, Jan (referee) ; Smrž, Pavel (advisor)
Diplomová práce se zabývá optimalizací systému pro sledování letadel, využívaného pro řízení letového provozu. Je popsána metodika vyhodnocování přesnosti sledovacího systému a přehled relevantních algoritmů pro sledování objektů. Dále jsou navrženy tři přístupy k řešení problému. První se pokouší identifikovat parametry filtrovacích algoritmů pomocí algoritmu Expectation-Maximisation, implementací metody maximální věrohodnosti. Druhý přístup je založen na prostých odhadech parametrů normálního rozložení z naměřených a referenčních dat. Nakonec je zkoumána možnost řešení pomocí optimalizačního algoritmu Evoluční strategie. Závěrečné vyhodnocení ukazuje, že třetí přístup je pro daný problém nejvhodnější.
|
|
|
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.
|
|
|
Klasifikace entit pomocí Wikipedie a WordNetu
Kliegr, Tomáš ; Rauch, Jan (advisor) ; Berka, Petr (referee) ; Smrž, Pavel (referee) ; Žabokrtský, Zdeněk (referee)
This dissertation addresses the problem of classification of entities in text represented by noun phrases. The goal of this thesis is to develop a method for automated classification of entities appearing in datasets consisting of short textual fragments. The emphasis is on unsupervised and semi-supervised methods that will allow for fine-grained character of the assigned classes and require no labeled instances for training. The set of target classes is either user-defined or determined automatically. Our initial attempt to address the entity classification problem is called Semantic Concept Mapping (SCM) algorithm. SCM maps the noun phrases representing the entities as well as the target classes to WordNet. Graph-based WordNet similarity measures are used to assign the closest class to the noun phrase. If a noun phrase does not match any WordNet concept, a Targeted Hypernym Discovery (THD) algorithm is executed. The THD algorithm extracts a hypernym from a Wikipedia article defining the noun phrase using lexico-syntactic patterns. This hypernym is then used to map the noun phrase to a WordNet synset, but it can also be perceived as the classification result by itself, resulting in an unsupervised classification system. SCM and THD algorithms were designed for English. While adaptation of these algorithms for other languages is conceivable, we decided to develop the Bag of Articles (BOA) algorithm, which is language agnostic as it is based on the statistical Rocchio classifier. Since this algorithm utilizes Wikipedia as a source of data for classification, it does not require any labeled training instances. WordNet is used in a novel way to compute term weights. It is also used as a positive term list and for lemmatization. A disambiguation algorithm utilizing global context is also proposed. We consider the BOA algorithm to be the main contribution of this dissertation. Experimental evaluation of the proposed algorithms is performed on the WordSim353 dataset, which is used for evaluation in the Word Similarity Computation (WSC) task, and on the Czech Traveler dataset, the latter being specifically designed for the purpose of our research. BOA performance on WordSim353 achieves Spearman correlation of 0.72 with human judgment, which is close to the 0.75 correlation for the ESA algorithm, to the author's knowledge the best performing algorithm for this gold-standard dataset, which does not require training data. The advantage of BOA over ESA is that it has smaller requirements on preprocessing of the Wikipedia data. While SCM underperforms on the WordSim353 dataset, it overtakes BOA on the Czech Traveler dataset, which was designed specifically for our entity classification problem. This discrepancy requires further investigation. In a standalone evaluation of THD on Czech Traveler dataset the algorithm returned a correct hypernym for 62% of entities.
|
|
|
Semantics in Multimedia: Event detection and cross-media feature extraction
Nemrava, Jan ; Svátek, Vojtěch (advisor) ; Berka, Petr (referee) ; Smrž, Pavel (referee)
This dissertation thesis describes the area of multimedia semantics which is a research area that brings together research streams that until recently run separately. The aim of the work is to provide an insight to all areas from this wide discipline and give an outlook on current problems especially to the semantic gab phenomena. Number of findings and outcomes in this work comes from international project K-Space, in which the author took part for three years. The extensive theoretical introduction into problematic is followed by a list state-of-the-art application from this area and overview of KIZI activities and involvements in the European project. The contribution of the work is a research on textual resources complementary to video and experiments with automatic detection of sporting events based on pre-classified examples and trained model. The practical contribution is also a demo web application that shows all the resources together and allows non-linear browsing of events.
|
guest ::
RSS feed.