Abordagem para o desenvolvimento de um etiquetador de alta acur��cia para o Portugu��s do Brasil

A etiquetagem morfossint��tica �� uma tarefa b��sica requerida por muitas aplica����es de processamento de linguagem natural, tais como an��lise gramatical e tradu����o autom��tica, e por aplica����es de processamento de fala, por exemplo, s��ntese de fala. Essa tarefa consiste em etiquetar palavras em uma senten��a com as suas categorias gramaticais. Apesar dessas aplica����es requererem etiquetadores que demandem maior precis��o, os etiquetadores do estado da arte ainda alcan��am acur��cia de 96 a 97%. Nesta tese, s��o investigados recursos de corpus e de software para o desenvolvimento de um etiquetador com acur��cia superior �� do estado da arte para o portugu��s brasileiro. Centrada em uma solu����o h��brida que combina etiquetagem probabil��stica com etiquetagem baseada em regras, a proposta de tese se concentra em um estudo explorat��rio sobre o m��todo de etiquetagem, o tamanho, a qualidade, o conjunto de etiquetas e o g��nero dos corpora de treinamento e teste, al��m de avaliar a desambiguiza����o de palavras novas ou desconhecidas presentes nos textos a serem etiquetados. Quatro corpora foram usados nos experimentos: CETENFolha, Bosque CF 7.4, Mac-Morpho e Selva Cient��fica. O modelo de etiquetagem proposto partiu do uso do m��todo de aprendizado baseado em transforma����o(TBL) ao qual foram adicionadas tr��s estrat��gias, combinadas em uma arquitetura que integra as sa��das (textos etiquetados) de duas ferramentas de uso livre, o TreeTagger e o ���-TBL, com os m��dulos adicionados ao modelo. No modelo de etiquetador treinado com o corpus Mac-Morpho, de g��nero jornal��stico, foram obtidas taxas de acur��cia de 98,05% na etiquetagem de textos do Mac-Morpho e 98,27% em textos do Bosque CF 7.4, ambos de g��nero jornal��stico. Avaliou-se tamb��m o desempenho do modelo de etiquetador h��brido proposto na etiquetagem de textos do corpus Selva Cient��fica, de g��nero cient��fico. Foram identificadas necessidades de ajustes no etiquetador e nos corpora e, como resultado, foram alcan��adas taxas de acur��cia de 98,07% no Selva Cient��fica, 98,06% no conjunto de teste do Mac-Morpho e 98,30% em textos do Bosque CF 7.4. Esses resultados s��o significativos, pois as taxas de acur��cia alcan��adas s��o superiores ��s do estado da arte, validando o modelo proposto em busca de um etiquetador morfossint��tico mais confi��vel.

Learning for well being : Studies using the International Adult Literacy Survey

This thesis is a collection of five independent but closely related studies. The overall purpose is to approach the analysis of learning outcomes from a perspective that combines three major elements, namely lifelonglifewide learning, human capital, and the benefits of learning. The approach is based on an interdisciplinary perspective of the human capital paradigm. It considers the multiple learning contexts that are responsible for the development of embodied potential ��� including formal, nonformal and informal learning ��� and the multiple outcomes ��� including knowledge, skills, economic, social and others��� that result from learning. The studies also seek to examine the extent and relative influence of learning in different contexts on the formation of embodied potential and how in turn that affects economic and social well being. The first study combines the three major elements, lifelonglifewide learning, human capital, and the benefits of learning into one common conceptual framework. This study forms a common basis for the four empirical studies that follow. All four empirical studies use data from the International Adult Literacy Survey (IALS) to investigate the relationships among the major elements of the conceptual framework presented in the first study. <u>Study I. A conceptual framework for the analysis of learning outcomes</u> This study brings together some key concepts and theories that are relevant for the analysis of learning outcomes. Many of the concepts and theories have emerged from varied disciplines including economics, educational psychology, cognitive science and sociology, to name only a few. Accordingly, some of the research questions inherent in the framework relate to different disciplinary perspectives. The primary purpose is to create a common basis for formulating and testing hypotheses as well as to interpret the findings in the empirical studies that follow. In particular, the framework facilitates the process of theorizing and hypothesizing on the relationships and processes concerning lifelong learning as well as their antecedents and consequences. <u>Study II. Determinants of literacy proficiency: A lifelong-lifewide learning perspective</u> This study investigates lifelong and lifewide processes of skill formation. In particular, it seeks to estimate the substitutability and complementarity effects of learning in multiple settings over the lifespan on literacy skill formation. This is done by investigating the predictive capacity of major determinants of literacy proficiency that are associated with a variety of learning contexts including school, home, work, community and leisure. An identical structural model based on previous research is fitted to the IALS data for 18 countries. The results show that even after accounting for all factors, education remains the most important predictor of literacy proficiency. In all countries, however, the total effect of education is significantly mediated through further learning occurring at work, at home and in the community. Therefore, the job and other literacy related factors complement education in predicting literacy proficiency. This result points to a virtual cycle of lifelong learning, particularly to how educational attainment influences other learning behaviours throughout life. In addition, results show that home background as measured by parents��� education is also a strong predictor of literacy proficiency, but in many countries this occurs only if a favourable home background is complemented with some post-secondary education. <u>Study III. The effect of literacy proficiency on earnings: An aggregated occupational approach using the Canadian IALS data</u> This study uses data from the Canadian Adult Literacy Survey to estimate the earnings return to literacy skills. The approach adapts a labour segmented view of the labour market by aggregating occupations into seven types, enabling the estimation of the variable impact of literacy proficiency on earnings, both within and between different types of occupations. This is done using Hierarchical Linear Modeling (HLM). The method used to construct the aggregated occupational classification is based on analysis that considers the role of cognitive and other skills in relation to the nature of occupational tasks. Substantial premiums are found to be associated with some occupational types even after adjusting for within occupational differences in individual characteristics such as schooling, literacy proficiency, labour force experience and gender. Average years of schooling and average levels of literacy proficiency at the between level account for over two-thirds of the premiums. Within occupations, there are significant returns to schooling but they vary depending on the type of occupations. In contrast, the within occupational return of literacy proficiency is not necessarily significant. The latter depends on the type of occupation. <u>Study IV: Determinants of economic and social outcomes from a lifewide learning perspective in Canada</u> In this study the relationship between learning in different contexts, which span the lifewide learning dimension, and individual earnings on the one hand and community participation on the other are examined in separate but comparable models. Data from the Canadian Adult Literacy Survey are used to estimate structural models, which correspond closely to the common conceptual framework outlined in Study I. The findings suggest that the relationship between formal education and economic and social outcomes is complex with confounding effects. The results indicate that learning occurring in different contexts and for different reasons leads to different kinds of benefits. The latter finding suggests a potential trade-off between realizing economic and social benefits through learning that are taken for either job-related or personal-interest related reasons. <u>Study V: The effects of learning on economic and social well being: A comparative analysis</u> Using the same structural model as in Study IV, hypotheses are comparatively examined using the International Adult Literacy Survey data for Canada, Denmark, the Netherlands, Norway, the United Kingdom, and the United States. The main finding from Study IV is confirmed for an additional five countries, namely that the effect of initial schooling on well being is more complex than a direct one and it is significantly mediated by subsequent learning. Additionally, findings suggest that people who devote more time to learning for job-related reasons than learning for personal-interest related reasons experience higher levels of economic well being. Moreover, devoting too much time to learning for personal-interest related reasons has a negative effect on earnings except in Denmark. But the more time people devote to learning for personal-interest related reasons tends to contribute to higher levels of social well being. These results again suggest a trade-off in learning for different reasons and in different contexts.

Software zur automatischen Tumordiagnose in der funktionalen MRT der weiblichen Brust

In den westlichen Industriel��ndern ist das Mammakarzinom der h��ufigste b��sartige Tumor der Frau. Sein weltweiter Anteil an allen Krebserkrankungen der Frau bel��uft sich auf etwa 21 %. Inzwischen ist jede neunte Frau bedroht, w��hrend ihres Lebens an Brustkrebs zu erkranken. Die alterstandardisierte Mortalit��trate liegt derzeit bei knapp 27 %.rnrnDas Mammakarzinom hat eine relative geringe Wachstumsrate. Die Existenz eines diagnostischen Verfahrens, mit dem alle Mammakarzinome unter 10 mm Durchmesser erkannt und entfernt werden, w��rden den Tod durch Brustkrebs praktisch beseitigen. Denn die 20-Jahres-��berlebungsrate bei Erkrankung durch initiale Karzinome der Gr����e 5 bis 10 mm liegt mit ��ber 95 % sehr hoch.rnrnMit der Kontrastmittel gest��tzten Bildgebung durch die MRT steht eine relativ junge Untersuchungsmethode zur Verf��gung, die sensitiv genug zur Erkennung von Karzinomen ab einer Gr����e von 3 mm Durchmesser ist. Die diagnostische Methodik ist jedoch komplex, fehleranf��llig, erfordert eine lange Einarbeitungszeit und somit viel Erfahrung des Radiologen.rnrnEine Computer unterst��tzte Diagnosesoftware kann die Qualit��t einer solch komplexen Diagnose erh��hen oder zumindest den Prozess beschleunigen. Das Ziel dieser Arbeit ist die Entwicklung einer vollautomatischen Diagnose Software, die als Zweitmeinungssystem eingesetzt werden kann. Meines Wissens existiert eine solche komplette Software bis heute nicht.rnrnDie Software f��hrt eine Kette von verschiedenen Bildverarbeitungsschritten aus, die dem Vorgehen des Radiologen nachgeahmt wurden. Als Ergebnis wird eine selbstst��ndige Diagnose f��r jede gefundene L��sion erstellt: Zuerst eleminiert eine 3d Bildregistrierung Bewegungsartefakte als Vorverarbeitungsschritt, um die Bildqualit��t der nachfolgenden Verarbeitungsschritte zu verbessern. Jedes kontrastanreichernde Objekt wird durch eine regelbasierte Segmentierung mit adaptiven Schwellwerten detektiert. Durch die Berechnung kinetischer und morphologischer Merkmale werden die Eigenschaften der Kontrastmittelaufnahme, Form-, Rand- und Textureeigenschaften f��r jedes Objekt beschrieben. Abschlie��end werden basierend auf den erhobenen Featurevektor durch zwei trainierte neuronale Netze jedes Objekt in zus��tzliche Funde oder in gut- oder b��sartige L��sionen klassifiziert.rnrnDie Leistungsf��higkeit der Software wurde auf Bilddaten von 101 weiblichen Patientinnen getested, die 141 histologisch gesicherte L��sionen enthielten. Die Vorhersage der Gesundheit dieser L��sionen ergab eine Sensitivit��t von 88 % bei einer Spezifit��t von 72 %. Diese Werte sind den in der Literatur bekannten Vorhersagen von Expertenradiologen ��hnlich. Die Vorhersagen enthielten durchschnittlich 2,5 zus��tzliche b��sartige Funde pro Patientin, die sich als falsch klassifizierte Artefakte herausstellten.rn

Learning Two-Person Interaction Models for Responsive Synthetic Humanoids

Imitation learning is a promising approach for generating life-like behaviors of virtual humans and humanoid robots. So far, however, imitation learning has been mostly restricted to single agent settings where observed motions are adapted to new environment conditions but not to the dynamic behavior of interaction partners. In this paper, we introduce a new imitation learning approach that is based on the simultaneous motion capture of two human interaction partners. From the observed interactions, low-dimensional motion models are extracted and a mapping between these motion models is learned. This interaction model allows the real-time generation of agent behaviors that are responsive to the body movements of an interaction partner. The interaction model can be applied both to the animation of virtual characters as well as to the behavior generation for humanoid robots.

Learning Curves of Temporary Workers in Manual Order Picking Activities

Person-to-stock order picking is highly flexible and requires minimal investment costs in comparison to automated picking solutions. For these reasons, tradi-tional picking is widespread in distribution and production logistics. Due to its typically large proportion of manual activities, picking causes the highest operative personnel costs of all intralogistics process. The required personnel capacity in picking varies short- and mid-term due to capacity requirement fluctuations. These dynamics are often balanced by employing minimal permanent staff and using seasonal help when needed. The resulting high personnel fluctuation necessitates the frequent training of new pickers, which, in combination with in-creasingly complex work contents, highlights the im-portance of learning processes in picking. In industrial settings, learning is often quantified based on diminishing processing time and cost requirements with increasing experience. The best-known industrial learning curve models include those from Wright, de Jong, Baloff and Crossman, which are typically applied to the learning effects of an entire work crew rather than of individuals. These models have been validated in largely static work environments with homogeneous work contents. Little is known of learning effects in picking systems. Here, work contents are heterogeneous and individual work strategies vary among employees. A mix of temporary and steady employees with varying degrees of experience necessitates the observation of individual learning curves. In this paper, the individual picking performance development of temporary employees is analyzed and compared to that of steady employees in the same working environment.

Learning through life

Web 2.0 und soziale Netzwerke gaben erste Impulse f��r neue Formen der Online-Lehre, welche die umfassende Vernetzung von Objekten und Nutzern im Internet nachhaltig einsetzen. Die Vielf��ltigkeit der unterschiedlichen Systeme erschwert aber deren ganzheitliche Nutzung in einem umfassenden Lernszenario, das den Anforderungen der modernen Informationsgesellschaft gen��gt. In diesem Beitrag wird eine auf dem Konnektivismus basierende Plattform f��r die Online-Lehre namens ���Wiki-Learnia��� pr��sentiert, welche alle wesentlichen Abschnitte des lebenslangen Lernens abbildet. Unter Einsatz zeitgem����er Technologien werden nicht nur Nutzer untereinander verbunden, sondern auch Nutzer mit dedizierten Inhalten sowie ggf. zugeh��rigen Autoren und/oder Tutoren verkn��pft. F��r ersteres werden verschiedene Kommunikations-Werkzeuge des Web 2.0 (soziale Netzwerke, Chats, Foren etc.) eingesetzt. Letzteres fu��t auf dem sogenannten ���Learning-Hub���-Ansatz, welcher mit Hilfe von Web-3.0-Mechanismen insbesondere durch eine semantische Metasuchmaschine instrumentiert wird. Zum Aufzeigen der praktischen Relevanz des Ansatzes wird das mediengest��tzte Juniorstudium der Universit��t Rostock vorgestellt, ein Projekt, das Sch��ler der Abiturstufe aufs Studium vorbereitet. Anhand der speziellen Anforderungen dieses Vorhabens werden der enorme Funktionsumfang und die gro��e Flexibilit��t von Wiki-Learnia demonstriert.

Developing a Web-Based Course in Phases

Introduction: Distance education has grown in popularity and usage. At the same time, enrollments at postsecondary institutions continue to increase. This places significant growth pressures on institutions of higher learning. Institutions providing nursing education have historically faced limited faculty resources. This has made it difficult to meet demand for distance education; and an ���all-at-once��� approach to course development does little to ease this problem. In this approach, resources are expended up front before a course is offered. [See PDF for complete abstract].

Evidence Based Nursing Research Project

Introduction: A need for baccalaureate prepared nurses to find and use evidence in practice exists. Whereas using this evidence in practice may be a masters level expectation, current practice demands that baccalaureate prepared nurses acquire a basic understanding of how to use evidence in practice. Nursing students at the senior level have had exposure to critiquing research, however, they have difficulty translating evidence to practice. [See PDF for complete abstract]

Cerebellar Pathology Does Not Impair Performance on Identification or Categorization Tasks

In comparison to the basal ganglia, prefrontal cortex, and medial temporal lobes, the cerebellum has been absent from recent research on the neural substrates of categorization and identification, two prominent tasks in the learning and memory literature. To investigate the contribution of the cerebellum to these tasks, we tested patients with cerebellar pathology (seven with bilateral degeneration, six with unilateral lesions, and two with midline damage) on rule-based and information-integration categorization tasks and an identification task. In rule-based tasks, it is assumed that participants learn the categories through an explicit reasoning process. In information-integration tasks, optimal performance requires the integration of information from multiple stimulus dimensions, and participants are typically unaware of the decision strategy. The identification task, in contrast, required participants to learn arbitrary, color-word associations. The cerebellar patients performed similar to matched controls on all three tasks and performance did not vary with the extent of cerebellar pathology. Although the interpretation of these null results requires caution, these data contribute to the current debate on cerebellar contributions to cognition by providing boundary conditions on understanding the neural substrates of categorization and identification, and help define the functional domain of the cerebellum in learning and memory.

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web

OntoTag - A Linguistic and Ontological Annotation Model Suitable for the Semantic Web 1. INTRODUCTION. LINGUISTIC TOOLS AND ANNOTATIONS: THEIR LIGHTS AND SHADOWS Computational Linguistics is already a consolidated research area. It builds upon the results of other two major ones, namely Linguistics and Computer Science and Engineering, and it aims at developing computational models of human language (or natural language, as it is termed in this area). Possibly, its most well-known applications are the different tools developed so far for processing human language, such as machine translation systems and speech recognizers or dictation programs. These tools for processing human language are commonly referred to as linguistic tools. Apart from the examples mentioned above, there are also other types of linguistic tools that perhaps are not so well-known, but on which most of the other applications of Computational Linguistics are built. These other types of linguistic tools comprise POS taggers, natural language parsers and semantic taggers, amongst others. All of them can be termed linguistic annotation tools. Linguistic annotation tools are important assets. In fact, POS and semantic taggers (and, to a lesser extent, also natural language parsers) have become critical resources for the computer applications that process natural language. Hence, any computer application that has to analyse a text automatically and ���intelligently��� will include at least a module for POS tagging. The more an application needs to ���understand��� the meaning of the text it processes, the more linguistic tools and/or modules it will incorporate and integrate. However, linguistic annotation tools have still some limitations, which can be summarised as follows: 1. Normally, they perform annotations only at a certain linguistic level (that is, Morphology, Syntax, Semantics, etc.). 2. They usually introduce a certain rate of errors and ambiguities when tagging. This error rate ranges from 10 percent up to 50 percent of the units annotated for unrestricted, general texts. 3. Their annotations are most frequently formulated in terms of an annotation schema designed and implemented ad hoc. A priori, it seems that the interoperation and the integration of several linguistic tools into an appropriate software architecture could most likely solve the limitations stated in (1). Besides, integrating several linguistic annotation tools and making them interoperate could also minimise the limitation stated in (2). Nevertheless, in the latter case, all these tools should produce annotations for a common level, which would have to be combined in order to correct their corresponding errors and inaccuracies. Yet, the limitation stated in (3) prevents both types of integration and interoperation from being easily achieved. In addition, most high-level annotation tools rely on other lower-level annotation tools and their outputs to generate their own ones. For example, sense-tagging tools (operating at the semantic level) often use POS taggers (operating at a lower level, i.e., the morphosyntactic) to identify the grammatical category of the word or lexical unit they are annotating. Accordingly, if a faulty or inaccurate low-level annotation tool is to be used by other higher-level one in its process, the errors and inaccuracies of the former should be minimised in advance. Otherwise, these errors and inaccuracies would be transferred to (and even magnified in) the annotations of the high-level annotation tool. Therefore, it would be quite useful to find a way to (i) correct or, at least, reduce the errors and the inaccuracies of lower-level linguistic tools; (ii) unify the annotation schemas of different linguistic annotation tools or, more generally speaking, make these tools (as well as their annotations) interoperate. Clearly, solving (i) and (ii) should ease the automatic annotation of web pages by means of linguistic tools, and their transformation into Semantic Web pages (Berners-Lee, Hendler and Lassila, 2001). Yet, as stated above, (ii) is a type of interoperability problem. There again, ontologies (Gruber, 1993; Borst, 1997) have been successfully applied thus far to solve several interoperability problems. Hence, ontologies should help solve also the problems and limitations of linguistic annotation tools aforementioned. Thus, to summarise, the main aim of the present work was to combine somehow these separated approaches, mechanisms and tools for annotation from Linguistics and Ontological Engineering (and the Semantic Web) in a sort of hybrid (linguistic and ontological) annotation model, suitable for both areas. This hybrid (semantic) annotation model should (a) benefit from the advances, models, techniques, mechanisms and tools of these two areas; (b) minimise (and even solve, when possible) some of the problems found in each of them; and (c) be suitable for the Semantic Web. The concrete goals that helped attain this aim are presented in the following section. 2. GOALS OF THE PRESENT WORK As mentioned above, the main goal of this work was to specify a hybrid (that is, linguistically-motivated and ontology-based) model of annotation suitable for the Semantic Web (i.e. it had to produce a semantic annotation of web page contents). This entailed that the tags included in the annotations of the model had to (1) represent linguistic concepts (or linguistic categories, as they are termed in ISO/DCR (2008)), in order for this model to be linguistically-motivated; (2) be ontological terms (i.e., use an ontological vocabulary), in order for the model to be ontology-based; and (3) be structured (linked) as a collection of ontology-based <Subject, Predicate, Object> triples, as in the usual Semantic Web languages (namely RDF(S) and OWL), in order for the model to be considered suitable for the Semantic Web. Besides, to be useful for the Semantic Web, this model should provide a way to automate the annotation of web pages. As for the present work, this requirement involved reusing the linguistic annotation tools purchased by the OEG research group (http://www.oeg-upm.net), but solving beforehand (or, at least, minimising) some of their limitations. Therefore, this model had to minimise these limitations by means of the integration of several linguistic annotation tools into a common architecture. Since this integration required the interoperation of tools and their annotations, ontologies were proposed as the main technological component to make them effectively interoperate. From the very beginning, it seemed that the formalisation of the elements and the knowledge underlying linguistic annotations within an appropriate set of ontologies would be a great step forward towards the formulation of such a model (henceforth referred to as OntoTag). Obviously, first, to combine the results of the linguistic annotation tools that operated at the same level, their annotation schemas had to be unified (or, preferably, standardised) in advance. This entailed the unification (id. standardisation) of their tags (both their representation and their meaning), and their format or syntax. Second, to merge the results of the linguistic annotation tools operating at different levels, their respective annotation schemas had to be (a) made interoperable and (b) integrated. And third, in order for the resulting annotations to suit the Semantic Web, they had to be specified by means of an ontology-based vocabulary, and structured by means of ontology-based <Subject, Predicate, Object> triples, as hinted above. Therefore, a new annotation scheme had to be devised, based both on ontologies and on this type of triples, which allowed for the combination and the integration of the annotations of any set of linguistic annotation tools. This annotation scheme was considered a fundamental part of the model proposed here, and its development was, accordingly, another major objective of the present work. All these goals, aims and objectives could be re-stated more clearly as follows: Goal 1: Development of a set of ontologies for the formalisation of the linguistic knowledge relating linguistic annotation. Sub-goal 1.1: Ontological formalisation of the EAGLES (1996a; 1996b) de facto standards for morphosyntactic and syntactic annotation, in a way that helps respect the <Unit, Attribute, Value> triple structure recommended for annotations in these works (which is isomorphic to the <Subject, Predicate, Object> triple structures used in the context of the Semantic Web). Sub-goal 1.2: Incorporation into this preliminary ontological formalisation of other existing standards and standard proposals relating the levels mentioned above, such as those currently under development within ISO/TC 37 (the ISO Technical Committee dealing with Terminology, which deals also with linguistic resources and annotations). Sub-goal 1.3: Generalisation and extension of the recommendations in EAGLES (1996a; 1996b) and ISO/TC 37 to the semantic level, for which no ISO/TC 37 standards have been developed yet. Sub-goal 1.4: Ontological formalisation of the generalisations and/or extensions obtained in the previous sub-goal as generalisations and/or extensions of the corresponding ontology (or ontologies). Sub-goal 1.5: Ontological formalisation of the knowledge required to link, combine and unite the knowledge represented in the previously developed ontology (or ontologies). Goal 2: Development of OntoTag���s annotation scheme, a standard-based abstract scheme for the hybrid (linguistically-motivated and ontological-based) annotation of texts. Sub-goal 2.1: Development of the standard-based morphosyntactic annotation level of OntoTag���s scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996a) and also the recommendations included in the ISO/MAF (2008) standard draft. Sub-goal 2.2: Development of the standard-based syntactic annotation level of the hybrid abstract scheme. This level should include, and possibly extend, the recommendations of EAGLES (1996b) and the ISO/SynAF (2010) standard draft. Sub-goal 2.3: Development of the standard-based semantic annotation level of OntoTag���s (abstract) scheme. Sub-goal 2.4: Development of the mechanisms for a convenient integration of the three annotation levels already mentioned. These mechanisms should take into account the recommendations included in the ISO/LAF (2009) standard draft. Goal 3: Design of OntoTag���s (abstract) annotation architecture, an abstract architecture for the hybrid (semantic) annotation of texts (i) that facilitates the integration and interoperation of different linguistic annotation tools, and (ii) whose results comply with OntoTag���s annotation scheme. Sub-goal 3.1: Specification of the decanting processes that allow for the classification and separation, according to their corresponding levels, of the results of the linguistic tools annotating at several different levels. Sub-goal 3.2: Specification of the standardisation processes that allow (a) complying with the standardisation requirements of OntoTag���s annotation scheme, as well as (b) combining the results of those linguistic tools that share some level of annotation. Sub-goal 3.3: Specification of the merging processes that allow for the combination of the output annotations and the interoperation of those linguistic tools that share some level of annotation. Sub-goal 3.4: Specification of the merge processes that allow for the integration of the results and the interoperation of those tools performing their annotations at different levels. Goal 4: Generation of OntoTagger���s schema, a concrete instance of OntoTag���s abstract scheme for a concrete set of linguistic annotations. These linguistic annotations result from the tools and the resources available in the research group, namely ��� Bitext���s DataLexica (http://www.bitext.com/EN/datalexica.asp), ��� LACELL���s (POS) tagger (http://www.um.es/grupos/grupo-lacell/quees.php), ��� Connexor���s FDG (http://www.connexor.eu/technology/machinese/glossary/fdg/), and ��� EuroWordNet (Vossen et al., 1998). This schema should help evaluate OntoTag���s underlying hypotheses, stated below. Consequently, it should implement, at least, those levels of the abstract scheme dealing with the annotations of the set of tools considered in this implementation. This includes the morphosyntactic, the syntactic and the semantic levels. Goal 5: Implementation of OntoTagger���s configuration, a concrete instance of OntoTag���s abstract architecture for this set of linguistic tools and annotations. This configuration (1) had to use the schema generated in the previous goal; and (2) should help support or refute the hypotheses of this work as well (see the next section). Sub-goal 5.1: Implementation of the decanting processes that facilitate the classification and separation of the results of those linguistic resources that provide annotations at several different levels (on the one hand, LACELL���s tagger operates at the morphosyntactic level and, minimally, also at the semantic level; on the other hand, FDG operates at the morphosyntactic and the syntactic levels and, minimally, at the semantic level as well). Sub-goal 5.2: Implementation of the standardisation processes that allow (i) specifying the results of those linguistic tools that share some level of annotation according to the requirements of OntoTagger���s schema, as well as (ii) combining these shared level results. In particular, all the tools selected perform morphosyntactic annotations and they had to be conveniently combined by means of these processes. Sub-goal 5.3: Implementation of the merging processes that allow for the combination (and possibly the improvement) of the annotations and the interoperation of the tools that share some level of annotation (in particular, those relating the morphosyntactic level, as in the previous sub-goal). Sub-goal 5.4: Implementation of the merging processes that allow for the integration of the different standardised and combined annotations aforementioned, relating all the levels considered. Sub-goal 5.5: Improvement of the semantic level of this configuration by adding a named entity recognition, (sub-)classification and annotation subsystem, which also uses the named entities annotated to populate a domain ontology, in order to provide a concrete application of the present work in the two areas involved (the Semantic Web and Corpus Linguistics). 3. MAIN RESULTS: ASSESSMENT OF ONTOTAG���S UNDERLYING HYPOTHESES The model developed in the present thesis tries to shed some light on (i) whether linguistic annotation tools can effectively interoperate; (ii) whether their results can be combined and integrated; and, if they can, (iii) how they can, respectively, interoperate and be combined and integrated. Accordingly, several hypotheses had to be supported (or rejected) by the development of the OntoTag model and OntoTagger (its implementation). The hypotheses underlying OntoTag are surveyed below. Only one of the hypotheses (H.6) was rejected; the other five could be confirmed. H.1 The annotations of different levels (or layers) can be integrated into a sort of overall, comprehensive, multilayer and multilevel annotation, so that their elements can complement and refer to each other. ��� CONFIRMED by the development of: o OntoTag���s annotation scheme, o OntoTag���s annotation architecture, o OntoTagger���s (XML, RDF, OWL) annotation schemas, o OntoTagger���s configuration. H.2 Tool-dependent annotations can be mapped onto a sort of tool-independent annotations and, thus, can be standardised. ��� CONFIRMED by means of the standardisation phase incorporated into OntoTag and OntoTagger for the annotations yielded by the tools. H.3 Standardisation should ease: H.3.1: The interoperation of linguistic tools. H.3.2: The comparison, combination (at the same level and layer) and integration (at different levels or layers) of annotations. ��� H.3 was CONFIRMED by means of the development of OntoTagger���s ontology-based configuration: o Interoperation, comparison, combination and integration of the annotations of three different linguistic tools (Connexor���s FDG, Bitext���s DataLexica and LACELL���s tagger); o Integration of EuroWordNet-based, domain-ontology-based and named entity annotations at the semantic level. o Integration of morphosyntactic, syntactic and semantic annotations. H.4 Ontologies and Semantic Web technologies (can) play a crucial role in the standardisation of linguistic annotations, by providing consensual vocabularies and standardised formats for annotation (e.g., RDF triples). ��� CONFIRMED by means of the development of OntoTagger���s RDF-triple-based annotation schemas. H.5 The rate of errors introduced by a linguistic tool at a given level, when annotating, can be reduced automatically by contrasting and combining its results with the ones coming from other tools, operating at the same level. However, these other tools might be built following a different technological (stochastic vs. rule-based, for example) or theoretical (dependency vs. HPS-grammar-based, for instance) approach. ��� CONFIRMED by the results yielded by the evaluation of OntoTagger. H.6 Each linguistic level can be managed and annotated independently. ��� REJECTED: OntoTagger���s experiments and the dependencies observed among the morphosyntactic annotations, and between them and the syntactic annotations. In fact, Hypothesis H.6 was already rejected when OntoTag���s ontologies were developed. We observed then that several linguistic units stand on an interface between levels, belonging thereby to both of them (such as morphosyntactic units, which belong to both the morphological level and the syntactic level). Therefore, the annotations of these levels overlap and cannot be handled independently when merged into a unique multileveled annotation. 4. OTHER MAIN RESULTS AND CONTRIBUTIONS First, interoperability is a hot topic for both the linguistic annotation community and the whole Computer Science field. The specification (and implementation) of OntoTag���s architecture for the combination and integration of linguistic (annotation) tools and annotations by means of ontologies shows a way to make these different linguistic annotation tools and annotations interoperate in practice. Second, as mentioned above, the elements involved in linguistic annotation were formalised in a set (or network) of ontologies (OntoTag���s linguistic ontologies). ��� On the one hand, OntoTag���s network of ontologies consists of ��� The Linguistic Unit Ontology (LUO), which includes a mostly hierarchical formalisation of the different types of linguistic elements (i.e., units) identifiable in a written text; ��� The Linguistic Attribute Ontology (LAO), which includes also a mostly hierarchical formalisation of the different types of features that characterise the linguistic units included in the LUO; ��� The Linguistic Value Ontology (LVO), which includes the corresponding formalisation of the different values that the attributes in the LAO can take; ��� The OIO (OntoTag���s Integration Ontology), which ��� Includes the knowledge required to link, combine and unite the knowledge represented in the LUO, the LAO and the LVO; ��� Can be viewed as a knowledge representation ontology that describes the most elementary vocabulary used in the area of annotation. ��� On the other hand, OntoTag���s ontologies incorporate the knowledge included in the different standards and recommendations for linguistic annotation released so far, such as those developed within the EAGLES and the SIMPLE European projects or by the ISO/TC 37 committee: ��� As far as morphosyntactic annotations are concerned, OntoTag���s ontologies formalise the terms in the EAGLES (1996a) recommendations and their corresponding terms within the ISO Morphosyntactic Annotation Framework (ISO/MAF, 2008) standard; ��� As for syntactic annotations, OntoTag���s ontologies incorporate the terms in the EAGLES (1996b) recommendations and their corresponding terms within the ISO Syntactic Annotation Framework (ISO/SynAF, 2010) standard draft; ��� Regarding semantic annotations, OntoTag���s ontologies generalise and extend the recommendations in EAGLES (1996a; 1996b) and, since no stable standards or standard drafts have been released for semantic annotation by ISO/TC 37 yet, they incorporate the terms in SIMPLE (2000) instead; ��� The terms coming from all these recommendations and standards were supplemented by those within the ISO Data Category Registry (ISO/DCR, 2008) and also of the ISO Linguistic Annotation Framework (ISO/LAF, 2009) standard draft when developing OntoTag���s ontologies. Third, we showed that the combination of the results of tools annotating at the same level can yield better results (both in precision and in recall) than each tool separately. In particular, 1. OntoTagger clearly outperformed two of the tools integrated into its configuration, namely DataLexica and FDG in all the combination sub-phases in which they overlapped (i.e. POS tagging, lemma annotation and morphological feature annotation). As far as the remaining tool is concerned, i.e. LACELL���s tagger, it was also outperformed by OntoTagger in POS tagging and lemma annotation, and it did not behave better than OntoTagger in the morphological feature annotation layer. 2. As an immediate result, this implies that a) This type of combination architecture configurations can be applied in order to improve significantly the accuracy of linguistic annotations; and b) Concerning the morphosyntactic level, this could be regarded as a way of constructing more robust and more accurate POS tagging systems. Fourth, Semantic Web annotations are usually performed by humans or else by machine learning systems. Both of them leave much to be desired: the former, with respect to their annotation rate; the latter, with respect to their (average) precision and recall. In this work, we showed how linguistic tools can be wrapped in order to annotate automatically Semantic Web pages using ontologies. This entails their fast, robust and accurate semantic annotation. As a way of example, as mentioned in Sub-goal 5.5, we developed a particular OntoTagger module for the recognition, classification and labelling of named entities, according to the MUC and ACE tagsets (Chinchor, 1997; Doddington et al., 2004). These tagsets were further specified by means of a domain ontology, namely the Cinema Named Entities Ontology (CNEO). This module was applied to the automatic annotation of ten different web pages containing cinema reviews (that is, around 5000 words). In addition, the named entities annotated with this module were also labelled as instances (or individuals) of the classes included in the CNEO and, then, were used to populate this domain ontology. ��� The statistical results obtained from the evaluation of this particular module of OntoTagger can be summarised as follows. On the one hand, as far as recall (R) is concerned, (R.1) the lowest value was 76,40% (for file 7); (R.2) the highest value was 97, 50% (for file 3); and (R.3) the average value was 88,73%. On the other hand, as far as the precision rate (P) is concerned, (P.1) its minimum was 93,75% (for file 4); (R.2) its maximum was 100% (for files 1, 5, 7, 8, 9, and 10); and (R.3) its average value was 98,99%. ��� These results, which apply to the tasks of named entity annotation and ontology population, are extraordinary good for both of them. They can be explained on the basis of the high accuracy of the annotations provided by OntoTagger at the lower levels (mainly at the morphosyntactic level). However, they should be conveniently qualified, since they might be too domain- and/or language-dependent. It should be further experimented how our approach works in a different domain or a different language, such as French, English, or German. ��� In any case, the results of this application of Human Language Technologies to Ontology Population (and, accordingly, to Ontological Engineering) seem very promising and encouraging in order for these two areas to collaborate and complement each other in the area of semantic annotation. Fifth, as shown in the State of the Art of this work, there are different approaches and models for the semantic annotation of texts, but all of them focus on a particular view of the semantic level. Clearly, all these approaches and models should be integrated in order to bear a coherent and joint semantic annotation level. OntoTag shows how (i) these semantic annotation layers could be integrated together; and (ii) they could be integrated with the annotations associated to other annotation levels. Sixth, we identified some recommendations, best practices and lessons learned for annotation standardisation, interoperation and merge. They show how standardisation (via ontologies, in this case) enables the combination, integration and interoperation of different linguistic tools and their annotations into a multilayered (or multileveled) linguistic annotation, which is one of the hot topics in the area of Linguistic Annotation. And last but not least, OntoTag���s annotation scheme and OntoTagger���s annotation schemas show a way to formalise and annotate coherently and uniformly the different units and features associated to the different levels and layers of linguistic annotation. This is a great scientific step ahead towards the global standardisation of this area, which is the aim of ISO/TC 37 (in particular, Subcommittee 4, dealing with the standardisation of linguistic annotations and resources).

Towards a lightweight mobile semantic-based approach for enhancing interaction with smart objects

This work describes a semantic extension for a user-smart object interaction model based on the ECA paradigm (Event-Condition-Action). In this approach, smart objects publish their sensing (event) and action capabilities in the cloud and mobile devices are prepared to retrieve them and act as mediators to configure personalized behaviours for the objects. In this paper, the information handled by this interaction system has been shaped according several semantic models that, together with the integration of an embedded ontological and rule-based reasoner, are exploited in order to (i) automatically detect incompatible ECA rules configurations and to (ii) support complex ECA rules definitions and execution. This semantic extension may significantly improve the management of smart spaces populated with numerous smart objects from mobile personal devices, as it facilitates the configuration of coherent ECA rules.

Cooperative Learning vs. Project Base Learning.

The Bologna Declaration and the implementation of the European Higher Education Area are promoting the use of active learning methodologies such as cooperative learning and project based learning. This study was motivated by the comparison of the results obtained after applying Cooperative Learning (CL) and Project Based Learning (PBL) to a subject of Computer Engineering. The fundamental hypothesis tested was whether the academic success achieved by the students of the first years was higher when CL was applied than in those cases to which PBL was applied. A practical case, by means of which the effectiveness of CL and PBL are compared, is presented in this work. This study has been carried out at the Universidad Polit��cnica de Madrid, where these mechanisms have been applied to the Operating Systems I subject from the Technical Engineering in Computer Systems degree (OSIS) and to the same subject from the Technical Engineering in Computer Management degree (OSIM). Both subjects have the same syllabus, are taught in the same year and semester and share also formative objectives. From this study we can conclude that students�� academic performance (regarding the grades given) is greater with PBL than with CL. To be more specific, the difference is between 0.5 and 1 point for the individual tests. For the group tests, this difference is between 2.5 and 3 points. Therefore, this study refutes the fundamental hypothesis formulated at the beginning. Some of the possible interpretations of these results are referred to in this study.

DAEDALUS at RepLab 2014: Detecting RepTrak reputation dimensions on tweets

This paper describes our participation at the RepLab 2014 reputation dimensions scenario. Our idea was to evaluate the best combination strategy of a machine learning classifier with a rule-based algorithm based on logical expressions of terms. Results show that our baseline experiment using just Naive Bayes Multinomial with a term vector model representation of the tweet text is ranked second among runs from all participants in terms of accuracy.

Dise��o de una red multinivel de conocimiento certificado y su aplicaci��n a la ense��anza universitaria

En la investigaci��n en e-Learning existe un inter��s especial en la adaptaci��n de los objetos de aprendizaje al estudiante, que se puede realizar por distintos caminos: considerando el perfil del estudiante, los estilos de aprendizaje, estableciendo rutas de aprendizaje, a trav��s de la tutor��a individualizada o utilizando sistemas de recomendaci��n. Aunque se han realizado avances en estas facetas de la adaptaci��n, los enfoques existentes aportan soluciones para un entorno espec��fico, sin que exista una orientaci��n que resuelva la adaptaci��n con una perspectiva m��s gen��rica, en el contexto de los objetos de aprendizaje y de la ense��anza. Esta tesis, con la propuesta de una ���red multinivel de conocimiento certificado��� aborda la adaptaci��n a los perfiles de los estudiantes, asume la reutilizaci��n de los objetos de aprendizaje e introduce la certificaci��n de los contenidos, sentando las bases de lo que podr��a ser una soluci��n global al aprendizaje. La propuesta se basa en reestructurar los contenidos en forma de red, en establecer distintos niveles de detalle para los contenidos de cada nodo de la red, para facilitar la adaptaci��n a los conocimientos previos del estudiante, y en certificar los contenidos con expertos. La ���red multinivel��� se implementa en una asignatura universitaria de grado, integr��ndola en los apuntes, y se aplica a la ense��anza. La validaci��n de la propuesta se realiza desde cuatro perspectivas: en las dos primeras, se realiza un an��lisis estad��stico para calcular la tasa de aceptaci��n y se aplica un modelo TAM, extrayendo los datos para realizar el an��lisis de una encuesta que cumplimentan los alumnos; en las otras dos, se analizan las calificaciones acad��micas y las encuestas de opini��n sobre la docencia. Se obtiene una tasa de aceptaci��n del 81% y se confirman el 90% de las hip��tesis del modelo TAM, se mejoran las calificaciones en un 21% y las encuestas de opini��n en un 9%, lo que valida la propuesta y su aplicaci��n a la ense��anza. ABSTRACT E-Learning research holds a special interest in the adaptation of learning objects to the student, which can be performed in different ways: taking into account the student profile or learning styles, by establishing learning paths, through individualized tutoring or using recommender systems. Although progress has been made in these types of adaptation, existing approaches provide solutions for a specific environment without an approach that addresses the adaptation from a more general perspective, that is, in the context of learning objects and teaching. This thesis, with the proposal of a ���certified knowledge multilevel network���, focuses on adapting to the student profile, it is based on the reuse of learning objects and introduces the certification of the contents, laying the foundations for what could be a global solution to learning. The proposal is based on restructuring the contents on a network setting different levels of depth in the contents of each node of the network to facilitate adaptation to the student���s background, and certify the contents with experts. The multilevel network is implemented in a university degree course, integrating it into the notes, and applied to teaching. The validation of the proposal is made from four perspectives: the first two, a statistical analysis is performed to calculate the rate of acceptance and the TAM model is applied, extracting data for analysis of a questionnaire-based survey completed by the students; the other two, academic qualifications and surveys about teaching are analyzed. The acceptance rate is 81%, 90% of TAM model assumptions are confirmed, academic qualifications are improved 21% and opinion survey 9%, which validates the proposal and its application to teaching.

Adults'Education and Agricultural Innovation: A Social Learning Approach

Social learning processes can be the basis of a method of agricultural innovation that involves expert and empirical knowledge. In this sense, the objective of this study was to determine the effectiveness and sustainability of an innovation process, understood as social learning, in a group of small farmers in the southern highlands of Peru. Innovative proposals and its permanence three years after the process finished were evaluated. It was observed that innovation processes generated are maintained over time; however, new innovations are not subsequently generated. We conclude that adult learning processes and innovation based on social learning are more effective and sustainable; however, the farmers internalization in innovation processes is given longer term.