Ontology and dictionary of science and education terms

Reikšminiai žodžiai: Ontologija; Tekstynas; Šveitimo ir mokslo terminai; Švietimo ir mokslo terminai; Žodynas; Corpus; Dictionary; Education and science terms; Ontology; Science and education term

Research of youth accentuation tendencies: principles and methodology

2011–2013 m. penkiuose Lietuvos universitetuose (Vilniaus, Lietuvos edukologijos, Vytauto Didžiojo, Šiaulių, Klaipėdos) buvo vykdomas projektas „Jaunimo kirčiavimo polinkiai: bendrinės kalbos normos ir šnekamosios kalbos kirčiavimo tendencijos“, kurį finansavo Valstybinė lietuvių kalbos komisija. Buvo analizuojamas 1476 žodžių ir jų formų kirčiavimas. Apklausose dalyvavo 593 informantai. Informantų apklausai vykdyti, apklausos duomenims kaupti ir sisteminti, projekto rezultatams viešinti sukurta interneto svetainė. Jos adresas: http://daukantas.vdu.lt/. Atlikto tyrimo pagrindu buvo parengti siūlymai Valstybinės lietuvių kalbos komisijos Tarties ir kirčiavimo pakomisei svarstyti, publikuota 15 straipsnių, perskaityta 11 pranešimų konferencijose ir seminaruoseThe research “Youth Accentuation Tendencies: Standard Language Norms and Tendencies of Spoken Language” was conducted in 2011–2013 at five Lithuanian universities: Vilnius University, Lithuanian University of Educational Sciences, Vytautas Magnus University, Šiauliai University, and Klaipėda University. This research was funded by the State Commission of the Lithuanian Language. The accentuation of 1476 words was analysed. The research involved 593 respondents. A website was created for questioners, collection and processing of data and publishing the project results (accessible at http://daukantas.vdu.lt/). The results of the research have been published in 11 scientific articles and presented at 11 conferences and workshops. The results have been summarized and recommendations for discussions were submitted to the Pronunciation and Accentuation Sub-commission under the Commission of the State Lithuanian LanguageKompiuterinės lingvistikos centrasLituanistikos katedraVytauto Didžiojo universiteta

Onthology and dictionary of science and education terms

Kompiuterinės lingvistikos centrasLituanistikos katedraVytauto Didžiojo universiteta

Semi-supervised learning of action ontology from domain-specific corpora

Online ISBN 978-3-642-41947-8Sistemų analizės katedraTaikomosios informatikos katedraVytauto Didžiojo universiteta

Semi-supervised learning of action ontology from domain-specific corpora

Online ISBN 978-3-642-41947-8Sistemų analizės katedraTaikomosios informatikos katedraVytauto Didžiojo universiteta

Ontology learning in practice : using semantics for knowledge grounding

This chapter presents research results, showing the use of ontology learning for knowledge grounding in e-learning environments. The established knowledge representation model is organized around actions, as the main elements linking the acquired knowledge with knowledge-based real-world activities. A framework for action ontology building from domain-specific corpus texts is suggested, utilizing different Natural Language Processing (NLP) techniques, such as collocation extraction, frequency lists, word space model, etc. The suggested framework employs additional knowledge sources of WordNet and VerbNet with structured linguistic and semantic information. Results from experiments with crawled chemical laboratory corpus texts are presentedSistemų analizės katedraTaikomosios informatikos katedraVytauto Didžiojo universiteta

Text mining for robotic action ontology engineering

The development of the action ontology (ACAT) from domain specific texts allows to discover previously unknown dependencies between robotic actions and their environment objects. This study explains the conceptual model of the ontology actions and environment objects and relations between them. Two main ACAT ontology classes determine the hierarchical structure of action and object hyponymy/hypernymy, troponyny: „action“ and „object“. Each action and object synset contains a subset of synonymous entities. All synsets from the ontology are described by the semantic roles, used in action execution by robots: main action, main object, primary object and secondary object. Study also explores various text mining methods for action ontology learning: collocation extraction, frequency lists, bag-of-words, word space model and Heart’s hyponomy recognition patterns. Verbnet thematic roles and frames are used to identify text syntactic and semantic structure – in this way recognized new text patterns allow to define dependencies between ontology synsets. Robotic action classes are identified by text classification with SVM machine learning method, where action categories are treated as classes, and appropriate verb context – as classification instances. The action ontology completeness and utility is evaluated empirically, by running as additional source of knowledge base in instruction processing system. This study introduces the preliminary testing results of the ACAT ontology usage in instruction processing to sequence of robotic execution tasks (chosen rotor assembly and biotechnology laboratory scenarios). While the explicit knowledge is parsed directly from the instructions, reasoning on queried ACAT ontology data allows to cover instruction tacit knowledge. It helps to execute human readable instructions with polysemous information, omissions, too general or non-robotic actions and not relevant textsSistemų analizės katedraTaikomosios informatikos katedraVytauto Didžiojo universiteta

Action classification in action ontology building using robot-specific texts

Instructions written in human-language cause no perception problems for humans, but become a challenge when translating them into robot executable format. This complex translation process covers different phases, including instruction completion by adding obligatory information that is not explicitly given in human-oriented instructions. Robot action ontology is a common source of such additional information, and it is normally structured around a limited number of verbs, denoting robot specific action categories, each of them characterized by a certain action environment. Semi-manual action ontology building procedures are normally based on domain-specific human-language text mining, and one of the problems to be solved is the assignment of action categories for the obtained verbs. Verbs in English language are very polysemous, therefore action category, referring to different robot capabilities, can be determined only after comprehensive analysis of the verb’s context. The task we solve is formulated as the text classification task, where action categories are treated as classes, and appropriate verb context – as classification instances. Since all classes are clearly defined, supervised machine learning paradigm is the best selection to tackle this problem. We experimentally investigated different context window widths; directions (context on the right, left, both sides of analyzed verb); and feature types (symbolic, lexical, morphological, aggregated). All statements were proved after exploration of two different datasets. The fact that all obtained results are above random and majority baselines allow us to claim that the proposed method can be used for predicting action categories. The best obtained results were achieved with Support Vector Machine method using window width of only 25 symbols on the right and bag-of-words as features.[...]Sistemų analizės katedraTaikomosios informatikos katedraVytauto Didžiojo universiteta