Arquitectura de sitios Web de bibliotecas universitarias : El sistema de bibliotecas de la Universidad Nacional de La Plata, Argentina

Este trabajo descriptivo exploratorio se propone analizar la arquitectura de información (AI) de sitios Web de bibliotecas de la Universidad Nacional de La Plata (UNLP), Argentina. Se analizaron 17 bibliotecas y se aplicó una grilla para recabar 10 aspectos relevantes. Los resultados fueron: 1. Ubicación del sitio Web de la biblioteca: 9 sitios incluidos en la página principal de la facultad. 2. Etiquetado de contenidos: terminología simple, sin jergas; no hay homogeneidad entre las bibliotecas. 3. Capacidad de búsqueda: 62 por ciento positiva, 38 por ciento negativa. 4. Sistema de búsqueda: simple 43 por ciento, compleja 10 por ciento, con ayudas 10 por ciento, ninguno 38 por ciento. 5. Sistemas de navegación: globales 5 por ciento, jerárquicos 79 por ciento, locales 5 por ciento, ninguno 11 por ciento. 6. Herramientas de navegación: barras 16 por ciento, frames o marcos 30 por ciento, índices 2 por ciento, mapas de sitio 7 por ciento, menús horizontales 9 por ciento, menús verticales 35 por ciento. 7. Sindicación de contenidos RSS: 3 sitios. 8. Otros servicios: chat 7 por ciento, descarga de documentos 16 por ciento, envío de formularios 14 por ciento, instructivos 21 por ciento, links a otras páginas 23 por ciento, tutoriales 5 por ciento, otros 14 por ciento. 9. Accesibilidad Web: 1 sitio. 10. Otras observaciones: ninguna. Se concluye que el desarrollo de los sitios es dispar y se recomienda considerar pautas de AI como parte de la cooperación en la red de bibliotecas de la UNLP

Intervenciones críticas en la poesía última (Prácticas de articulación y agendas complementarias)

Indagaremos algunas dominantes en la intervención crítica sobre el género poético en lengua española de las últimas décadas, proponiendo alternativas diferentes a las convencionales operaciones de recorte y exclusión de grupos y tendencias, a los que nos tiene acostumbrada la crítica mayoritaria, desde antologías y suplementos literarios, preocupada por rotular, oponer y disgregar, diseñando mapas de polaridades irreconciliables y nombres propios consagrados como oráculos, sin nexo con sus coetáneos, asimilados como adversarios, y potenciando rivalidades personales, o comportamientos públicos de los actores por encima de sus escrituras. Para superar estas reductivas miradas se hace necesario ensayar propuestas de articulación: suturar fisuras imaginarias para integrar praxis materiales; imaginar políticas de intercambio textual e ideológico; admitir un trazado de zonas de confluencia permeables; asumir que los poetas vehiculizan identidades móviles y receptivas, a favor de un diálogo inter-local mutuamente enriquecedor.

Arquitectura de sitios Web de bibliotecas universitarias : El sistema de bibliotecas de la Universidad Nacional de La Plata, Argentina

Este trabajo descriptivo exploratorio se propone analizar la arquitectura de información (AI) de sitios Web de bibliotecas de la Universidad Nacional de La Plata (UNLP), Argentina. Se analizaron 17 bibliotecas y se aplicó una grilla para recabar 10 aspectos relevantes. Los resultados fueron: 1. Ubicación del sitio Web de la biblioteca: 9 sitios incluidos en la página principal de la facultad. 2. Etiquetado de contenidos: terminología simple, sin jergas; no hay homogeneidad entre las bibliotecas. 3. Capacidad de búsqueda: 62 por ciento positiva, 38 por ciento negativa. 4. Sistema de búsqueda: simple 43 por ciento, compleja 10 por ciento, con ayudas 10 por ciento, ninguno 38 por ciento. 5. Sistemas de navegación: globales 5 por ciento, jerárquicos 79 por ciento, locales 5 por ciento, ninguno 11 por ciento. 6. Herramientas de navegación: barras 16 por ciento, frames o marcos 30 por ciento, índices 2 por ciento, mapas de sitio 7 por ciento, menús horizontales 9 por ciento, menús verticales 35 por ciento. 7. Sindicación de contenidos RSS: 3 sitios. 8. Otros servicios: chat 7 por ciento, descarga de documentos 16 por ciento, envío de formularios 14 por ciento, instructivos 21 por ciento, links a otras páginas 23 por ciento, tutoriales 5 por ciento, otros 14 por ciento. 9. Accesibilidad Web: 1 sitio. 10. Otras observaciones: ninguna. Se concluye que el desarrollo de los sitios es dispar y se recomienda considerar pautas de AI como parte de la cooperación en la red de bibliotecas de la UNLP

Intervenciones críticas en la poesía última (Prácticas de articulación y agendas complementarias)

Indagaremos algunas dominantes en la intervención crítica sobre el género poético en lengua española de las últimas décadas, proponiendo alternativas diferentes a las convencionales operaciones de recorte y exclusión de grupos y tendencias, a los que nos tiene acostumbrada la crítica mayoritaria, desde antologías y suplementos literarios, preocupada por rotular, oponer y disgregar, diseñando mapas de polaridades irreconciliables y nombres propios consagrados como oráculos, sin nexo con sus coetáneos, asimilados como adversarios, y potenciando rivalidades personales, o comportamientos públicos de los actores por encima de sus escrituras. Para superar estas reductivas miradas se hace necesario ensayar propuestas de articulación: suturar fisuras imaginarias para integrar praxis materiales; imaginar políticas de intercambio textual e ideológico; admitir un trazado de zonas de confluencia permeables; asumir que los poetas vehiculizan identidades móviles y receptivas, a favor de un diálogo inter-local mutuamente enriquecedor.

Arquitectura de sitios Web de bibliotecas universitarias : El sistema de bibliotecas de la Universidad Nacional de La Plata, Argentina

Este trabajo descriptivo exploratorio se propone analizar la arquitectura de información (AI) de sitios Web de bibliotecas de la Universidad Nacional de La Plata (UNLP), Argentina. Se analizaron 17 bibliotecas y se aplicó una grilla para recabar 10 aspectos relevantes. Los resultados fueron: 1. Ubicación del sitio Web de la biblioteca: 9 sitios incluidos en la página principal de la facultad. 2. Etiquetado de contenidos: terminología simple, sin jergas; no hay homogeneidad entre las bibliotecas. 3. Capacidad de búsqueda: 62 por ciento positiva, 38 por ciento negativa. 4. Sistema de búsqueda: simple 43 por ciento, compleja 10 por ciento, con ayudas 10 por ciento, ninguno 38 por ciento. 5. Sistemas de navegación: globales 5 por ciento, jerárquicos 79 por ciento, locales 5 por ciento, ninguno 11 por ciento. 6. Herramientas de navegación: barras 16 por ciento, frames o marcos 30 por ciento, índices 2 por ciento, mapas de sitio 7 por ciento, menús horizontales 9 por ciento, menús verticales 35 por ciento. 7. Sindicación de contenidos RSS: 3 sitios. 8. Otros servicios: chat 7 por ciento, descarga de documentos 16 por ciento, envío de formularios 14 por ciento, instructivos 21 por ciento, links a otras páginas 23 por ciento, tutoriales 5 por ciento, otros 14 por ciento. 9. Accesibilidad Web: 1 sitio. 10. Otras observaciones: ninguna. Se concluye que el desarrollo de los sitios es dispar y se recomienda considerar pautas de AI como parte de la cooperación en la red de bibliotecas de la UNLP

Intervenciones críticas en la poesía última (Prácticas de articulación y agendas complementarias)

Indagaremos algunas dominantes en la intervención crítica sobre el género poético en lengua española de las últimas décadas, proponiendo alternativas diferentes a las convencionales operaciones de recorte y exclusión de grupos y tendencias, a los que nos tiene acostumbrada la crítica mayoritaria, desde antologías y suplementos literarios, preocupada por rotular, oponer y disgregar, diseñando mapas de polaridades irreconciliables y nombres propios consagrados como oráculos, sin nexo con sus coetáneos, asimilados como adversarios, y potenciando rivalidades personales, o comportamientos públicos de los actores por encima de sus escrituras. Para superar estas reductivas miradas se hace necesario ensayar propuestas de articulación: suturar fisuras imaginarias para integrar praxis materiales; imaginar políticas de intercambio textual e ideológico; admitir un trazado de zonas de confluencia permeables; asumir que los poetas vehiculizan identidades móviles y receptivas, a favor de un diálogo inter-local mutuamente enriquecedor.

Villefranche MedSeA mesocosm experiment 2013: perturbation studies and field studies

A joint mesocosm experiment took place in February/March 2013 in the bay of Villefranche in France as part of the european MedSeA project. Nine mesocosms (52 m**3) were deployed over a 2 weeks period and 6 different levels of pCO2 and 3 control mesocosms (about 450 µatm), were used, in order to cover the range of pCO2 anticipated for the end of the present century. During this experiment, the potential effects of these perturbations on chemistry, planktonic community composition and dynamics including: eucaryotic and prokaryotic species composition, primary production, nutrient and carbon utilization, calcification, diazotrophic nitrogen fixation, organic matter exudation and composition, micro-layer composition and biogas production were studied by a group of about 25 scientists from 8 institutes and 6 countries. This is one of the first mesocosm experiments conducted in oligotrophic waters. A blog dedicated to this experiment can be viewed at: http://medseavillefranche2013.obs-vlfr.fr.

Sediment community responses to marine versus terrigenous organic matter in the Whittard canyon

The Whittard canyon is a branching submarine canyon on the Celtic continental margin, which may act as a conduit for sediment and organic matter (OM) transport from the European continental slope to the abyssal sea floor. In situ stable-isotope labelling experiments (JC36-042-Spre01; JC36-100-Spre01) were conducted in the eastern and western branches of the Whittard canyon testing short term (3 - 7 day) responses of sediment communities to deposition of nitrogen-rich marine and nitrogen-poor terrigenous phytodetritus. Isotopic labels were traced into faunal biomass and bulk sediments, and the bacterial polar lipid fatty acids (PLFAs). These data files provide the data on macrofaunal and bacterial uptake of the isotopically-labelled organic carbon and nitrogen, and macrofaunal community composition at the two stations within the Whittard canyon

Experiment on marine fungus Microascus brevicaulis strain

The marine fungus Microascus brevicaulis strain LF580 is a non-model secondary metabolite producer with high yields of the two secondary metabolites scopularide A and B, which exhibit distinct activities against tumour cell lines. A mutant strain was obtained using UV mutagenesis, showing besides higher production levels faster growth and differences in pellet formation. Comparative proteomics were applied to gain deeper understanding of the regulation of production and of the physiology of this fungus and its mutant. For this purpose, an optimised protein extraction protocol was established. Here, we show the first proteome study of a marine fungus. In total, 4759 proteins were identified. The central metabolic pathway of LF580 could be mapped by using KEGG pathway analysis and GO annotation. Using iTRAQ labelling, 318 proteins were shown to be significantly regulated in the mutant strain: 189 were down- and 129 upregulated. Proteomics are a powerful tool for the understanding of regulatory aspects: The differences on proteome level could be attributed to a limited nutrient availability in wild type strain due to a strong pellet formation. This information can be applied to optimisation on strain and process level. The linkage between nutrient limitation and pellet formation in the non-model fungus M. brevicaulis is in consensus with the knowledge on model organisms like Aspergillus niger and Penicillium chrysogenum.

Villefranche sur Mer: multistressors experiment March 2012

The effect of ocean warming and acidification was investigated on a natural plankton assemblage from an oligotrophic area, the bay of Villefranche (NW Mediterranean Sea). The assemblage was sampled in March 2012 and exposed to the following four treatments for 12 days: control ( 360 µatm, 14°C), elevated pCO2 ( 610 µatm, 14°C), elevated temperature ( 410 µatm, 17°C), and elevated pCO2 and temperature ( 690 µatm, 17°C). Nutrients were already depleted at the beginning of the experiment and the concentrations of chlorophyll a (chl a), heterotrophic prokaryotes and viruses decreased, under all treatments, throughout the experiment. There were no statistically significant effects of ocean warming and acidification, whether in isolation or combined, on the concentrations of nutrients, particulate organic matter, chl a and most of the photosynthetic pigments. Furthermore, 13C labelling showed that the carbon transfer rates from 13C-sodium bicarbonate into particulate organic carbon were not affected by seawater warming nor acidification. Rates of gross primary production followed the general decreasing trend of chl a concentrations and were significantly higher under elevated temperature, an effect exacerbated when combined to elevated pCO2 level. In contrast to the other algal groups, the picophytoplankton population (cyanobacteria, mostly Synechococcus) increased throughout the experiment and was more abundant in the warmer treatment though to a lesser extent when combined to high pCO2 level. These results suggest that under nutrient-depleted conditions in the Mediterranean Sea, ocean acidification has a very limited impact on the plankton community and that small species will benefit from warming with a potential decrease of the export and energy transfer to higher trophic levels.

Photosynthate translocation increases in response to low seawater pH in a coral-dinoflagellate symbiosis

This study has examined the effect of low seawater pH values (induced by an increased CO2 partial pressure) on the rates of photosynthesis, as well as on the carbon budget and carbon translocation in the scleractinian coral species Stylophora pistillata, using a new model based on 13C labelling of the photosynthetic products. Symbiont photosynthesis contributes to a large part of the carbon acquisition in tropical coral species, and it is thus important to know how environmental changes affect this carbon acquisition and allocation. For this purpose, nubbins of S. pistillata were maintained for six months at two pHTs (8.1 and 7.2, by bubbling seawater with CO2). The lowest pH value was used to tackle how seawater pH impacts the carbon budget of a scleractinian coral. Rates of photosynthesis and respiration of the symbiotic association and of isolated symbionts were assessed at each pH. The fate of 13C photosynthates was then followed in the symbionts and the coral host for 48 h. Nubbins maintained at pHT 7.2 presented a lower areal symbiont concentration, and lower areal rates of gross photosynthesis and carbon incorporation compared to nubbins maintained at pHT 8.1. The total carbon acquisition was thus lower under low pH. However, the total percentage of carbon translocated to the host as well as the amount of carbon translocated per symbiont cell were significantly higher under pHT 7.2 than under pHT 8.1 (70% at pHT 7.2 vs. 60% at pHT 8.1), such that the total amount of photosynthetic carbon received by the coral host was equivalent under both pHs (5.5 to 6.1 µg C/cm**2/h). Although the carbon budget of the host was unchanged, symbionts acquired less carbon for their own needs (0.6 compared to 1.8 µg C/cm**2/h), explaining the overall decrease in symbiont concentration at low pH. In the long term, such decrease in symbiont concentration might severely affect the carbon budget of the symbiotic association.

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).

A Satisfaction-based Model for Affect Recognition from Conversational Features in Spoken Dialog Systems

Detecting user affect automatically during real-time conversation is the main challenge towards our greater aim of infusing social intelligence into a natural-language mixed-initiative High-Fidelity (Hi-Fi) audio control spoken dialog agent. In recent years, studies on affect detection from voice have moved on to using realistic, non-acted data, which is subtler. However, it is more challenging to perceive subtler emotions and this is demonstrated in tasks such as labelling and machine prediction. This paper attempts to address part of this challenge by considering the role of user satisfaction ratings and also conversational/dialog features in discriminating contentment and frustration, two types of emotions that are known to be prevalent within spoken human-computer interaction. However, given the laboratory constraints, users might be positively biased when rating the system, indirectly making the reliability of the satisfaction data questionable. Machine learning experiments were conducted on two datasets, users and annotators, which were then compared in order to assess the reliability of these datasets. Our results indicated that standard classifiers were significantly more successful in discriminating the abovementioned emotions and their intensities (reflected by user satisfaction ratings) from annotator data than from user data. These results corroborated that: first, satisfaction data could be used directly as an alternative target variable to model affect, and that they could be predicted exclusively by dialog features. Second, these were only true when trying to predict the abovementioned emotions using annotator?s data, suggesting that user bias does exist in a laboratory-led evaluation.