Internazionalizzare le imprese locali: localizzazione del sito web GreenVet per l'azienda APA-CT di Forlì

Cette étude s’encadre dans le projet Language Toolkit, qui dérive de la collaboration entre la Chambre de Commerce de Forlì-Cesena et la Scuola di Lingue e Letterature, Traduzione e Interpretazione de Forlì. L’objectif du projet est de permettre aux étudiants de dernière année de faculté de connaître l’univers des entreprises. Grâce à la collaboration avec APA-CT srl de Forlì, leader dans le secteur de la phytothérapie vétérinaire, et spécialisée dans la production de compléments alimentaires naturels destinés aux animaux, on a réalisé la localisation en français du site web GreenVet. Cette dissertation se compose de quatre chapitres. Le premier chapitre offre une présentation de APA-CT srl et de ses produits. De plus, on définit les détails du projet de localisation du site web GreenVet. Dans le deuxième chapitre on propose, en premier lieu, une réflexion sur les langues spéciales et sur la terminologie scientifique. En deuxième lieu, on aborde, aussi bien du point de vue théorique que du point de vue pratique (à travers des exemples), les caractéristiques de la terminologie de la phytothérapie vétérinaire. En troisième lieu, on explique la méthodologie de travail adoptée pendant la réalisation du projet de localisation, qui a été divisé en deux sous-phases : d’abord, on a effectué une recherche terminologique pour délimiter le domaine en question et, après, on a crée des ressources (corpus, glossaire) utiles pour la traduction. Le troisième chapitre offre une réflexion à propos de la localisation des sites web. Ensuite, on propose une analyse contrastive entre les sites web italiens et français traitant de la phytothérapie vétérinaire. Finalement, le quatrième chapitre est dédié à l’activité pratique de localisation. On a analysé le texte de départ au niveau de la structure, des aspects linguistiques et on a individué les difficultés de traduction. Après, on explique la méthodologie de travail suivie et les stratégies adoptées pendant la traduction.

Tradurre per le aziende: localizzazione del sito web di Tecnopress S.a.s.

La presente tesi magistrale si inserisce nell’ambito del progetto Language Toolkit e nasce in particolare dalla collaborazione tra il presente autore e l’azienda Tecnopress S.a.s. Nell’ottica dell’internazionalizzazione della propria attività l’azienda ha richiesto che il proprio sito web venisse sottoposto ad un processo di localizzazione dall’italiano verso l’inglese. Sulla base di queste premesse si è deciso di dividere il lavoro in tre parti: una fase teorica, una fase preparatoria e una fase pratica. La prima fase si è occupata di gettare le basi teoriche necessarie per affrontare consapevolmente l’incarico assegnato. Alla luce delle conclusioni della fase teorica è stata constatata l’esigenza di modificare la natura dell’intervento che si era preventivato di effettuare sul sito web. Fulcro della localizzazione è infatti il locale, ovvero la combinazione di regione socioculturale e lingua presa a riferimento per un processo di localizzazione web. Nelle richieste avanzate dall’azienda figuravano esclusivamente indicazioni relative alla lingua (l’inglese), non già alla regione socioculturale su cui modellare l’intervento di localizzazione del sito. Si è rivelato pertanto necessario procedere autonomamente alla definizione di un locale per il presente progetto, che è stato fatto coincidere con la macrozona europea e cui è stato associato un modello ibrido di English Lingua Franca e Simplified Technical English. Il focus dell’intervento non è stato dunque una regione geografica circoscritta ma una realtà socioculturale molto più ampia e variegata: l’adozione di un locale così ampio e il rispetto dell’indicazione sulla lingua hanno portato il presente autore a optare per un intervento di standardizzazione web. Chiude il presente lavoro una fase operativa, durante la quale sono state create le risorse necessarie al progetto (si segnala in particolare un corpus su misura) e sono state effettuate la standardizzazione e una serie di considerazioni traduttive.

La divulgación científica de análisis sensorial de vinos en soporte web

En esta tesis se trabaja sobre la hipótesis de que el componente didáctico del discurso divulgativo queda delimitado por estrategias discursivas originadas en el tratamiento modal y actualizadas en los niveles funcional, situacional, semántico y formal-gramatical. El objetivo es caracterizar tales estrategias para identificar tendencias en la realización lingüísticodiscursiva del componente didáctico. El corpus se ha formado teniendo en cuenta soporte (web), formato (hipertexto) y dominio disciplinar (Análisis Sensorial de Vinos). La metodología es, fundamentalmente, cualitativo-ejemplar, basada en el modelo multinivel propuesto por Ciapuscio (2003) para el análisis de textos especializados. Los resultados sugieren que en el nivel funcional, el componente didáctico se distingue por el predominio de los términos positivos de las categorías modales epistémica (función informar) y ética (función dirigir); en el nivel situacional, por tres tipos de construcciones discursivas: la del enunciador experto, la del enunciatario lego y la de la pertenencia del lego a la comunidad especializada; en el nivel semántico, por la estandarización de partes textuales y por el predominio tanto de axiologización eufórica ética y cognoscitiva, como de secuencias expositivas y de procedimientos explicativos causales, descriptivos e ilustrativos; en el nivel formal, por recursos paratextuales e hipertextuales que refuerzan la actualización del componente didáctico.

Uso de imágenes satelitales de alta resolución en el estudio de los bosques : análisis bibliométrico a través de Web of Science (1985-2015)

Las técnicas de teledetección constituyen una herramienta indispensable para caracterizar los tipos de hábitat en áreas extensas y detectar los cambios ambientales resultantes de los procesos naturales y las actividades antrópicas. Estas potencialidades se incrementaron con el lanzamiento de los satélites de alta resolución espacial. En este marco constituye una herramienta de análisis para varias disciplinas como la gestión del territorio, la ecología y la conservación. Actualmente los ecosistemas de bosques presentan alta vulnerabilidad debido a los cambios de uso del suelo, la fragmentación y la invasión de especies exóticas siendo objeto de estudio de varias disciplinas. Este trabajo se realizó con el objetivo de analizar la producción científica que vincula el uso de imágenes satelitales de alta resolución con los ecosistemas de bosques. Se conformó un corpus documental de 979 registros obtenidos de la base Web of Science (WOS) (1985-2015). Mediante la aplicación de metodologías de los Estudios Métricos de la Información combinadas con Análisis de Redes Sociales se identificaron los autores y las instituciones con mayor producción y las publicaciones más relevantes del dominio. Se identificaron cinco frentes de investigación; a) vinculado a los aspectos técnicos de la teledetección; b) cambio climático y bosques; c) conservación de la biodiversidad; y dos relacionados con el tipo de ecosistema de bosque: d) bosques tropicales y e) bosques boreales. Estos resultados evidencian que el estudio de los bosques templados (presentes en nuestra región) y sus principales amenazas no se ha desarrollado en el corpus estudiado. Se señala el aporte de los EMI en la orientación de líneas de investigación relevantes para la región.

Uso de imágenes satelitales de alta resolución en el estudio de los bosques : análisis bibliométrico a través de Web of Science (1985-2015)

Las técnicas de teledetección constituyen una herramienta indispensable para caracterizar los tipos de hábitat en áreas extensas y detectar los cambios ambientales resultantes de los procesos naturales y las actividades antrópicas. Estas potencialidades se incrementaron con el lanzamiento de los satélites de alta resolución espacial. En este marco constituye una herramienta de análisis para varias disciplinas como la gestión del territorio, la ecología y la conservación. Actualmente los ecosistemas de bosques presentan alta vulnerabilidad debido a los cambios de uso del suelo, la fragmentación y la invasión de especies exóticas siendo objeto de estudio de varias disciplinas. Este trabajo se realizó con el objetivo de analizar la producción científica que vincula el uso de imágenes satelitales de alta resolución con los ecosistemas de bosques. Se conformó un corpus documental de 979 registros obtenidos de la base Web of Science (WOS) (1985-2015). Mediante la aplicación de metodologías de los Estudios Métricos de la Información combinadas con Análisis de Redes Sociales se identificaron los autores y las instituciones con mayor producción y las publicaciones más relevantes del dominio. Se identificaron cinco frentes de investigación; a) vinculado a los aspectos técnicos de la teledetección; b) cambio climático y bosques; c) conservación de la biodiversidad; y dos relacionados con el tipo de ecosistema de bosque: d) bosques tropicales y e) bosques boreales. Estos resultados evidencian que el estudio de los bosques templados (presentes en nuestra región) y sus principales amenazas no se ha desarrollado en el corpus estudiado. Se señala el aporte de los EMI en la orientación de líneas de investigación relevantes para la región.

Uso de imágenes satelitales de alta resolución en el estudio de los bosques : análisis bibliométrico a través de Web of Science (1985-2015)

Las técnicas de teledetección constituyen una herramienta indispensable para caracterizar los tipos de hábitat en áreas extensas y detectar los cambios ambientales resultantes de los procesos naturales y las actividades antrópicas. Estas potencialidades se incrementaron con el lanzamiento de los satélites de alta resolución espacial. En este marco constituye una herramienta de análisis para varias disciplinas como la gestión del territorio, la ecología y la conservación. Actualmente los ecosistemas de bosques presentan alta vulnerabilidad debido a los cambios de uso del suelo, la fragmentación y la invasión de especies exóticas siendo objeto de estudio de varias disciplinas. Este trabajo se realizó con el objetivo de analizar la producción científica que vincula el uso de imágenes satelitales de alta resolución con los ecosistemas de bosques. Se conformó un corpus documental de 979 registros obtenidos de la base Web of Science (WOS) (1985-2015). Mediante la aplicación de metodologías de los Estudios Métricos de la Información combinadas con Análisis de Redes Sociales se identificaron los autores y las instituciones con mayor producción y las publicaciones más relevantes del dominio. Se identificaron cinco frentes de investigación; a) vinculado a los aspectos técnicos de la teledetección; b) cambio climático y bosques; c) conservación de la biodiversidad; y dos relacionados con el tipo de ecosistema de bosque: d) bosques tropicales y e) bosques boreales. Estos resultados evidencian que el estudio de los bosques templados (presentes en nuestra región) y sus principales amenazas no se ha desarrollado en el corpus estudiado. Se señala el aporte de los EMI en la orientación de líneas de investigación relevantes para la región.

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 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 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 triple structure recommended for annotations in these works (which is isomorphic to the 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).

Intrgración de un corpus de textos biblingüe y un glosario del campo de la informática.

En este trabajo se presentan las principales características de Calíope, una aplicación web que es capaz de manejar un corpus y un glosario de términos en inglés y en español. La singularidad más importante de esta herramienta es que permite interrelacionar estos dos recursos. Así, por ejemplo, los resultados de la búsqueda de concordancias se pueden incorporar automáticamente a los ejemplos de uso del término correspondiente en el glosario; y desde la lista de palabras de un texto del corpus se pueden añadir términos al glosario o acceder a la información de un término que esté en el glosario.

Antipattern detection in web ontologies: an experiment using SPARQL queries

Ontology antipatterns are structures that reflect ontology modelling problems because they lead to inconsistencies, bad reasoning performance or bad formalisation of domain knowledge. We propose four methods for the detection of antipatterns using SPARQL queries.We conduct some experiments to detect antipattern in a corpus of OWL ontologies.

Consulta on-line basada en corpus de construcciones con preposición en el texto técnico en inglés y su traducción al español

Basándonos en la recopilación inicial de preposiciones, locuciones preposicionales, términos con preposición dependiente y phrasal verbs utilizados en el texto técnico realizada en otros proyectos anteriores del Departamento de Lingüística Aplicada a la Ciencia y a la Tecnología, el objetivo de este trabajo es completar, organizar, actualizar y dar visibilidad a esta información inicial. Tras realizar un proceso exhaustivo de verificación, unificación, clasificación y ampliación de la información existente, en caso necesario, el listado resultante se utiliza para elaborar un glosario de términos con preposición. El objetivo final de este proyecto es que este glosario esté a disposición de los usuarios, a través de una consulta on-line, en la página del ILLLab (http://illlab.euitt.upm.es/wordpress/), dependiente del Departamento de Lingüística Aplicada a la Ciencia y a la Tecnología. Para incluir en el glosario ejemplos actualizados de textos técnicos, se ha recopilado un corpus lingüístico de textos técnicos, tomando como base diferentes números de la revista IEEE Spectrum, en su edición digital, publicados entre los años 2009 y 2012. El objetivo de esta recopilación es la de ofrecer al consultante diferentes ejemplos de uso en el texto técnico de los distintos términos con preposición que componen el glosario, de manera que pueda acceder de manera rápida y sencilla a ejemplos de uso real de los términos que está buscando, con objeto de clarificar aspectos relacionados con su uso o, en su caso, facilitar su aprendizaje. Toda esta información, tanto el listado de términos con preposición como las frases pertenecientes al corpus recopilado, se incorpora a una base de datos, alojada dentro de la misma página web del ILLLab. A través de un formulario de consulta, a disposición del usuario en dicha página, se pueden obtener todos los términos recopilados que coincidan con los criterios de búsqueda introducidos. El usuario puede realizar dos tipos de búsqueda principales: por preposición o por término completo. Además, puede elegir una búsqueda global (entre todos los términos que integran el glosario) o parcial (en una sola de las categorías en las que se han dividido los diferentes términos, de acuerdo con su función gramatical). Por último, se presentan unas estadísticas de uso de los términos recopilados dentro de los diferentes textos que integran el corpus lingüístico, de manera que pueda establecerse una relación de los que aparecen con más frecuencia en el texto técnico. ABSTRACT. Based on the initial collection of prepositions, prepositional phrases, dependent prepositions and phrasal verbs used in technical texts collected on previous projects in the Department of Applied Linguistics to Science and Technology, the aim of this project is to improve, organize, update and provide visibility to this initial information. Following a process of verification, unification, classification and extension of existing information, if necessary, a glossary of terms with preposition is built. The ultimate objective of this project is to make this glossary available to users through an online consultation in the ILLLab webpage (http://illlab.euitt.upm.es/wordpress/). The administration of tis webpage depends of the Department of Applied Linguistics in Science and Technology. A linguistic corpus of technical texts has been compiled, based on different numbers of the IEEE Spectrum magazine, in its online edition, published between the years 2009 and 2012. The aim of this collection is to provide different examples of use in the technical text for the terms included in the glossary, so that examples of the actual use of the terms consulted can be easily and quickly accessed, in order to clarify doubts regarding their meaning or translation into Spanish and facilitate learning. All this information, both the list of terms with prepositional phrases as well as the corpus developed, is incorporated in a database. Through a searching form, the ILLLab's user may obtain all the terms matching the search criteria entered. The user can perform two types of main search: by preposition or by full term. Additionally, a global search can be selected (including all terms included in the glossary) or a partial one (including only one of the glossary's categories). Finally, some statistics of use are presented according to the various texts included in the corpus, so a relation of the most frequent prepositions in the technical text can be established.

PoS-tagging the Web in Portuguese. National varieties, text typologies and spelling systems

The great amount of text produced every day in the Web turned it as one of the main sources for obtaining linguistic corpora, that are further analyzed with Natural Language Processing techniques. On a global scale, languages such as Portuguese - official in 9 countries - appear on the Web in several varieties, with lexical, morphological and syntactic (among others) differences. Besides, a unified spelling system for Portuguese has been recently approved, and its implementation process has already started in some countries. However, it will last several years, so different varieties and spelling systems coexist. Since PoS-taggers for Portuguese are specifically built for a particular variety, this work analyzes different training corpora and lexica combinations aimed at building a model with high-precision annotation in several varieties and spelling systems of this language. Moreover, this paper presents different dictionaries of the new orthography (Spelling Agreement) as well as a new freely available testing corpus, containing different varieties and textual typologies.

Web mining for translators: automatic construction of comparable, genre-driven corpora

The aim of this paper is to evaluate the efficacy of the application WebBootCaT to create specialised corpora automatically, investigating the translation of articles of association from Italian into English. The first section reflects on the relevant literature and proposes the utility of corpora for translators. The second section discusses the methodology employed, and the third section analyses the results obtained and comments on how language professionals could possibly exploit the application to its full. The fourth section provides a few concrete usage examples of the thus built corpora, to then conclude that WebBootCaT is a genuinely powerful tool that could be implemented by professional translators in order to save time and improve their translations in the long term.

The science and technology of corpus, and corpus for science and technology

Corpus Linguistics is a young discipline. The earliest work was done in the 1960s, but corpora only began to be widely used by lexicographers and linguists in the late 1980s, by language teachers in the late 1990s, and by language students only very recently. This course in corpus linguistics was held at the Departamento de Linguistica Aplicada, E.T.S.I. de Minas, Universidad Politecnica de Madrid from June 15-19 1998. About 45 teachers registered for the course. 30% had PhDs in linguistics, 20% in literature, and the rest were doctorandi or qualified English teachers. The course was designed to introduce the use of corpora and other computational resources in teaching and research, with special reference to scientific and technological discourse in English. Each participant had a computer networked with the lecturer’s machine, whose display could be projected onto a large screen. Application programs were loaded onto the central server, and telnet and a web browser were available. COBUILD gave us permission to access the 323 million word Bank of English corpus, Mike Scott allowed us to use his Wordsmith Tools software, and Tim Johns gave us a copy of his MicroConcord program.

Issues in creating a corpus for EAP pedagogy and research

UK universities are accepting increasing numbers of students whose L1 is not English on a wide range of programmes at all levels. These students require additional support and training in English, focussing on their academic disciplines. Corpora have been used in EAP since the 1980s, mainly for research, but a growing number of researchers and practitioners have been advocating the use of corpora in EAP pedagogy, and such use is gradually increasing. This paper outlines the processes and factors to be considered in the design and compilation of an EAP corpus (e.g., the selection and acquisition of texts, metadata, data annotation, software tools and outputs, web interface, and screen displays), especially one intended to be used for teaching. Such a corpus would also facilitate EAP research in terms of longitudinal studies, student progression and development, and course and materials design. The paper has been informed by the preparatory work on the EAP subcorpus of the ACORN corpus project at Aston University. © 2007 Elsevier Ltd. All rights reserved.

Merging and ranking answers in the semantic web:the wisdom of crowds

In this paper we propose algorithms for combining and ranking answers from distributed heterogeneous data sources in the context of a multi-ontology Question Answering task. Our proposal includes a merging algorithm that aggregates, combines and filters ontology-based search results and three different ranking algorithms that sort the final answers according to different criteria such as popularity, confidence and semantic interpretation of results. An experimental evaluation on a large scale corpus indicates improvements in the quality of the search results with respect to a scenario where the merging and ranking algorithms were not applied. These collective methods for merging and ranking allow to answer questions that are distributed across ontologies, while at the same time, they can filter irrelevant answers, fuse similar answers together, and elicit the most accurate answer(s) to a question.