Master of Science / Department of Computing and Information Sciences / Doina Caragea / As genomes are sequenced, a major challenge is their annotation -- the identification of genes and regulatory elements, their locations and their functions. For years, it was believed that one gene corresponds to one protein, but the discovery of alternative splicing provided a mechanism for generating different gene transcripts (isoforms) from the same genomic sequence. In the recent years, it has become obvious that a large fraction of genes undergoes alternative splicing. Thus, understanding alternative splicing is a problem of great interest to biologists. Supervised machine learning approaches can be used to predict alternative splicing events at genome level. However, supervised approaches require large amounts of labeled data to produce accurate classifiers. While large amounts of genomic data are produced by the new sequencing technologies, labeling these data can be costly and time consuming. Therefore, semi-supervised learning approaches that can make use of large amounts of unlabeled data, in addition to small amounts of labeled data are highly desirable. In this work, we study the usefulness of a semi-supervised learning approach, co-training, for classifying exons as alternatively spliced or constitutive. The co-training algorithm makes use of two views of the data to iteratively learn two classifiers that can inform each other, at each step, with their best predictions on the unlabeled data. We consider three sets of features for constructing views for the problem of predicting alternatively spliced exons: lengths of the exon of interest and its flanking introns, exonic splicing enhancers (a.k.a., ESE motifs) and intronic regulatory sequences (a.k.a., IRS motifs). Naive Bayes and Support Vector Machine (SVM) algorithms are used as based classifiers in our study. Experimental results show that the usage of the unlabeled data can result in better classifiers as compared to those obtained from the small amount of labeled data alone. In addition to semi-supervised approaches, we also also study the usefulness of graph based transductive learning approaches for predicting alternatively spliced exons. Similar to the semi-supervised learning algorithms, transductive learning algorithms can make use of unlabeled data, together with labeled data, to produce labels for the unlabeled data. However, a classification model that could be used to classify new unlabeled data is not learned in this case. Experimental results show that graph based transductive approaches can make effective use of the unlabeled data.

Récemment, nous avons pu observer un intérêt grandissant pour l'application de l'analogie formelle à l'analyse morphologique. L'intérêt premier de ce concept repose sur ses parallèles avec le processus mental impliqué dans la création de nouveaux termes basée sur les relations morphologiques préexistantes de la langue. Toutefois, l'utilisation de ce concept reste tout de même marginale due notamment à son coût de calcul élevé.Dans ce document, nous présenterons le système à base de graphe Moranapho fondé sur l'analogie formelle. Nous démontrerons par notre participation au Morpho Challenge 2009 (Kurimo:10) et nos expériences subséquentes, que la qualité des analyses obtenues par ce système rivalise avec l'état de l'art. Nous analyserons aussi l'influence de certaines de ses composantes sur la qualité des analyses morphologiques produites. Nous appuierons les conclusions tirées de nos analyses sur des théories bien établies dans le domaine de la linguistique. Ceci nous permet donc de fournir certaines prédictions sur les succès et les échecs de notre système, lorsqu'appliqué à d'autres langues que celles testées au cours de nos expériences. / Recently, we have witnessed a growing interest in applying the concept of formal analogy to unsupervised morphology acquisition. The attractiveness of this concept lies in its parallels with the mental process involved in the creation of new words based on morphological relations existing in the language. However, the use of formal analogy remain marginal partly due to their high computational cost. In this document, we present Moranapho, a graph-based system founded on the concept of formal analogy. Our participation in the 2009 Morpho Challenge (Kurimo:10) and our subsequent experiments demonstrate that the performance of Moranapho are favorably comparable to the state-of-the-art. We studied the influence of some of its components on the quality of the morphological analysis produced as well. Finally, we will discuss our findings based on well-established theories in the field of linguistics. This allows us to provide some predictions on the successes and failures of our system when applied to languages other than those tested in our experiments.

Récemment, nous avons pu observer un intérêt grandissant pour l'application de l'analogie formelle à l'analyse morphologique. L'intérêt premier de ce concept repose sur ses parallèles avec le processus mental impliqué dans la création de nouveaux termes basée sur les relations morphologiques préexistantes de la langue. Toutefois, l'utilisation de ce concept reste tout de même marginale due notamment à son coût de calcul élevé.Dans ce document, nous présenterons le système à base de graphe Moranapho fondé sur l'analogie formelle. Nous démontrerons par notre participation au Morpho Challenge 2009 (Kurimo:10) et nos expériences subséquentes, que la qualité des analyses obtenues par ce système rivalise avec l'état de l'art. Nous analyserons aussi l'influence de certaines de ses composantes sur la qualité des analyses morphologiques produites. Nous appuierons les conclusions tirées de nos analyses sur des théories bien établies dans le domaine de la linguistique. Ceci nous permet donc de fournir certaines prédictions sur les succès et les échecs de notre système, lorsqu'appliqué à d'autres langues que celles testées au cours de nos expériences. / Recently, we have witnessed a growing interest in applying the concept of formal analogy to unsupervised morphology acquisition. The attractiveness of this concept lies in its parallels with the mental process involved in the creation of new words based on morphological relations existing in the language. However, the use of formal analogy remain marginal partly due to their high computational cost. In this document, we present Moranapho, a graph-based system founded on the concept of formal analogy. Our participation in the 2009 Morpho Challenge (Kurimo:10) and our subsequent experiments demonstrate that the performance of Moranapho are favorably comparable to the state-of-the-art. We studied the influence of some of its components on the quality of the morphological analysis produced as well. Finally, we will discuss our findings based on well-established theories in the field of linguistics. This allows us to provide some predictions on the successes and failures of our system when applied to languages other than those tested in our experiments.

Tato práce se zabývá problematikou odpovídání na otázky nad strukturovanými daty. Ve většině případů jsou strukturovaná data reprezentována pomocí propojených grafů, avšak ukrytí koncové struktury dát je podstatné pro využití podobných systémů jako součástí rozhraní s přirozeným jazykem. Odpovídající systém byl navržen a vyvíjen v rámci této práce. V porovnání s tradičními odpovídajícími systémy, které jsou založené na lingvistické analýze nebo statistických metodách, náš systém zkoumá poskytnutý graf a ve výsledků generuje sémantické vazby na základě vstupních párů otázka-odpověd'. Vyvíjený systém je nezávislý na struktuře dát, ale pro účely vyhodnocení jsme využili soubor dát z Wikidata a DBpedia. Kvalita výsledného systému a zkoumaného přístupu byla vyhodnocena s využitím připraveného datasetu a standartních metrik.