During the last decade increasing interest in parallel programming can be observed. It is caused by a tendency of developing microprocessors as a multicore units, that can perform instructions simultaneously. Popular and widely used example of such platform is a graphic processing unit (GPU). Its ability to perform calculations simultaneously is being investigated as a way for improving performance of the complex algorithms. Therefore, GPU’s are now having the architectures that allows to use its computional power by programmers and software developers in the same way as CPU. One of these architectures is CUDA platform, developed by nVidia. Aim of this thesis is to implement the parallel CYK algorithm, which is one of the most popular parsing algorithms, for CUDA platform, that will gain a significant speed-up in comparison with the sequential CYK algorithm. The thesis presents review of existing parallelisations of CYK algorithm, descriptions of implemented algorithms (basic version and few modifications), and experimental stage, that includes testing these versions for various inputs in order to justify which version of algorithm is giving the best performance. There are three versions of algorithm presented, from which one was selected as the best (giving about 10 times better performance for the longest instances of inputs). Also, a limited version of algorithm, that gives best performance (even 100 times better in comparison with non-limited sequential version), but requires some conditions to be fulfilled by grammar, is presented. The motivation for the thesis is to use the developed algorithm in GCS.

We deal with text processing, pattern matching and syntax analysis every day, and new areas emerging. We can consider programming languages, communication protocols or a simple text as well-know examples. Therefore, we need new effective methods which fit into these areas. In this work, we explore the topics of parallel grammars, E0L-systems and multigenerative grammar systems. The goal is to improve the Cocke-Younger-Kasami algorithm and present a stronger algorithm of analysis.

Les grammaires de dépendance permettent de construire une organisation hiérarchique syntaxique des mots d’une phrase. La construction manuelle des arbres de dépendances étant une tâche exigeant temps et expertise, de nombreux travaux cherchent à l’automatiser. Visant à établir un processus léger et facilement adaptable nous nous sommes intéressés à l’apprentissage non supervisé de dépendances, évitant ainsi d’avoir recours à une expertise coûteuse. L’état de l’art en apprentissage non supervisé de dépendances (DMV) se compose de méthodes très complexes et extrêmement sensibles au paramétrage initial. Nous présentons dans cette thèse un nouveau modèle pour résoudre ce problème d’analyse de dépendances, mais de façon plus simple, plus rapide et plus adaptable. Nous apprenons une famille de grammaires (PCFG) réduites à moins de 6 non terminaux et de 15 règles de combinaisons des non terminaux à partir des étiquettes grammaticales. Les PCFG de cette famille que nous nommons DGdg (pour DROITE GAUCHE droite gauche) se paramètrent très légèrement, ainsi elles s’adaptent sans effort aux 12 langues testées. L’apprentissage et l’analyse sont effectués au moins deux fois plus rapidement que DMV sur les mêmes données. Et la qualité des analyses DGdg est pour certaines langues proches des analyses par DMV. Nous proposons une première application de notre méthode d’analyse de dépendances à l’extraction d’informations. Nous apprenons par des CRF un étiquetage en fonctions « sujet », « objet » et « prédicat », en nous fondant sur des caractéristiques extraites des arbres construits. / Dependency grammars allow the construction of a hierarchical organization of the words of sentences. The one-by-one building of dependency trees can be very long and it requries expert knowledge. In this regard, we are interested in unsupervised dependency learning. Currently, DMV give the state-of-art results in unsupervised dependency parsing. However, DMV has been known to be highly sensitive to initial parameters. The training of DMV model is also heavy and long. We present in this thesis a new model to solve this problem in a simpler, faster and more adaptable way. We learn a family of PCFG using less than 6 nonterminal symbols and less than 15 combination rules from the part-of-speech tags. The tuning of these PCFG is ligth, and so easily adaptable to the 12 languages we tested. Our proposed method for unsupervised dependency parsing can show the near state-of-the-art results, being twice faster. Moreover, we describe our interests in dependency trees to other applications such as relation extraction. Therefore, we show how such information from dependency structures can be integrated into condition random fields and how to improve a relation extraction task.

This thesis deals with the topic of sequential and parallel grammars. Both of these groups cover a large number of grammar families, most of which, however, are not widely used because of the difficulties related to their processing. The thesis examines some of these grammar types, such as scattered-context grammars, multigenerative systems, and interactive L-systems, with focus on their normal forms. Subsequently, it introduces a set of algorithms utilising properties of the discussed grammar types as well as their normal forms. These algorithms are based on the Cocke-Younger-Kasami algorithm for context-free grammars, and are capable of parsing any grammar in the corresponding normal form. Finally, a program implementing the proposed algorithms is presented.