The social perception of heroes and murders: Effects of gender-inclusive language in media reports

The way media depict women and men can reinforce or diminish gender stereotyping. Which part does language play in this context? Are roles perceived as more gender-balanced when feminine role nouns are used in addition to masculine ones? Research on gender-inclusive language shows that the use of feminine-masculine word pairs tends to increase the visibility of women in various social roles. For example, when speakers of German were asked to name their favorite "heroine or hero in a novel," they listed more female characters than when asked to name their favorite "hero in a novel." The research reported in this article examines how the use of gender-inclusive language in news reports affects readers' own usage of such forms as well as their mental representation of women and men in the respective roles. In the main experiment, German participants (N = 256) read short reports about heroes or murderers which contained either masculine generics or gender-inclusive forms (feminine-masculine word pairs). Gender-inclusive forms enhanced participants' own usage of gender-inclusive language and this resulted in more gender-balanced mental representations of these roles. Reading about "heroines and heroes" made participants assume a higher percentage of women among persons performing heroic acts than reading about "heroes" only, but there was no such effect for murderers. A post-test suggested that this might be due to a higher accessibility of female exemplars in the category heroes than in the category murderers. Importantly, the influence of gender-inclusive language on the perceived percentage of women in a role was mediated by speakers' own usage of inclusive forms. This suggests that people who encounter gender-inclusive forms and are given an opportunity to use them, use them more themselves and in turn have more gender-balanced mental representations of social roles.

Does gender-fair language pay off? The social perception of professions from a cross-linguistic perspective

In many languages, masculine forms (e.g., German Lehrer, “teachers, masc.”) have traditionally been used to refer to both women and men, although feminine forms are available, too. Feminine-masculine word pairs (e.g., German Lehrerinnen und Lehrer, “teachers, fem. and teachers, masc.”) are recommended as gender-fair alternatives. A large body of empirical research documents that the use of gender-fair forms instead of masculine forms has a substantial impact on mental representations. Masculine forms activate more male representations even when used in a generic sense, whereas word pairs (e.g., German Lehrerinnen und Lehrer, “teachers, fem. and teachers, masc.”) lead to a higher cognitive inclusion of women (i.e., visibility of women). Some recent studies, however, have also shown that in a professional context word pairs may be associated with lesser status. The present research is the first to investigate both effects within a single paradigm. A cross-linguistic (Italian and German) study with 391 participants shows that word pairs help to avoid a male bias in the gender-typing of professions and increase women's visibility; at the same time, they decrease the estimated salaries of typically feminine professions (but do not affect perceived social status or competence). This potential payoff has implications for language policies aiming at gender-fairness.

Use of cognitive interviewing in the development of a Spanish language genetic knowledge instrument

Study purpose. Genetic advances are significantly impacting healthcare, yet recent studies of ethnic group participation in genetic services demonstrate low utilization rates by Latinos. Limited genetic knowledge is a major barrier. The purpose of this study was to field test items in a Spanish-language instrument that will be used to measure genetic knowledge relevant to type 2 diabetes among members of the ethnically heterogeneous U.S. Latino community. Accurate genetic knowledge measurement can provide the foundation for interventions to enhance genetic service utilization. ^ Design. Three waves of cognitive interviews were conducted in Spanish to field test 44 instrument items Thirty-six Latinos, with 12 persons representative of Mexican, Central and South American, and Cuban heritage participated, including 7 males and 29 females between 22 and 60 years of age; 17 participants had 12 years or less of education. ^ Methods. Text narratives from transcriptions of audiotaped interviews were qualitatively analyzed using a coding strategy to indicate potential sources of response error. Through an iterative process of instrument refinement, codes that emerged from the data were used to guide item revisions at the conclusion of each phase; revised items were examined in subsequent interview waves. ^ Results. Inter-cultural and cross-cultural themes associated with difficulties in interpretation and grammatical structuring of items were identified; difficulties associated with comprehension reflected variations in educational level. Of the original 44 items, 32 were retained, 89% of which were revised. Six additional items reflective of cultural knowledge were constructed, resulting in a 38-item instrument. ^ Conclusions. Use of cognitive interviewing provided a valuable tool for detecting both potential sources of response error and cultural variations in these sources. Analysis of interview data guided successive instrument revisions leading to improved item interpretability and comprehension. Although testing in a larger sample will be essential to test validity and reliability, the outcome of field testing suggests initial content validity of a Spanish-language instrument to measure genetic knowledge relative to type 2 diabetes. ^ Keywords. Latinos, genetic knowledge, instrument development, cognitive interviewing ^

Groundfish survey data from the German Bight from 1902-08, 1919-23, and 1930-1932 and ICES International Bottom Trawl Survey (IBTS) quarter 3 data from 1991 to 2009

Groundfish survey data from the German Bight from 1902-08, 1919-23, and 1930-1932 and ICES International Bottom Trawl Survey (IBTS) quarter 3 data from 1991 to 2009 were analysed with respect to species frequencies, maximum length, trends in catch-per-unit-effort, species richness parameters (SNR) and presence of large fish (Phi40), the latter defined as average presence of species per haul with specimens larger than 40 cm given. Four different periods are distinguished: (a) before 1914 with medium commercial CPUE and low landings, Phi40 approx. 2, high abundance in elasmobranchs and SNR conditions indicating highly diverse assemblages, (b) conditions immediately after 1918 with higher commercial CPUE, recovering landings, Phi40 at > 4 in 1919, and SNR conditions indicating highly diverse assemblages, (c) conditions from 1920 to the early 1930's with decreasing commercial CPUE, increased landings, decreasing Phi40, SNR conditions similar to later years indicating less diverse assemblages, and a decrease in elasmobranchs. In the IBTS series (d), Phi40 remains low indicating an increased rarity of large specimens, and SNR characteristics are similar to the third period. Dab, whiting and grey gurnard have increased considerably in the IBTS series as compared to the historic data. Phi40 is suggested an alternative indicator reflecting community functional diversity when weight based indicators cannot be applied.

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).

Phonotactic language recognition using i-vectors and phoneme posteriogram counts

This paper describes a novel approach to phonotactic LID, where instead of using soft-counts based on phoneme lattices, we use posteriogram to obtain n-gram counts. The high-dimensional vectors of counts are reduced to low-dimensional units for which we adapted the commonly used term i-vectors. The reduction is based on multinomial subspace modeling and is designed to work in the total-variability space. The proposed technique was tested on the NIST 2009 LRE set with better results to a system based on using soft-counts (Cavg on 30s: 3.15% vs 3.43%), and with very good results when fused with an acoustic i-vector LID system (Cavg on 30s acoustic 2.4% vs 1.25%). The proposed technique is also compared with another low dimensional projection system based on PCA. In comparison with the original soft-counts, the proposed technique provides better results, reduces the problems due to sparse counts, and avoids the process of using pruning techniques when creating the lattices.

Examining the Complex Relationships of Early Intervention on Language and Psychosocial Development in Families Facing Multiple Risks with a Focus on Children of Hispanic Immigrants

Abundant research has shown that poverty has negative influences on young child academic and psychosocial development, and unfortunately, disparities in school readiness between low and high income children can be seen as early the first year of life. The largest federal early care and education intervention for these vulnerable children is Early Head Start (EHS). To diminish these disparate child outcomes, EHS seeks to provide community based flexible programming for infants and toddlers and their families. Given how relatively recent these programs have been offered, little is known about the nuances of how EHS impacts infant and toddler language and psychosocial development. Using a framework of Community Based Participatory Research (CBPR) this paper had 5 goals: 1) to characterize the associations between domain specific and cumulative risk and child outcomes 2) to validate and explore these risk-outcome associations separately for Children of Hispanic immigrants (COHIs), 3) to explore relationships among family characteristics, multiple environmental factors, and dosage patterns in different EHS program types, 4) to examine the relationship between EHS dosage and child outcomes, and 5) to examine how EHS compliance impacts child internalizing and externalizing behaviors and emerging language abilities. Results of the current study showed that risks were differentially related to child outcomes. Poor maternal mental health was related to child internalizing and externalizing behaviors, but not related to emerging child language skills. Although child language skills were not related to maternal mental health, they were related to economic hardship. Additionally, parent level Spanish use and heritage orientation were associated with positive child outcomes. Results also showed that these relationships differed when COHIs and children with native-born parents were examined separately. Further, unique patterns emerged for EHS program use, for example families who participated in home-based care were less likely to comply with EHS attendance requirements. These findings provide tangible suggestions for EHS stakeholders: namely, the need to develop effective programming that targets engagement for diverse families enrolled in EHS programs.

Fénix: a flexible information exchange data model for natural language processing

In this paper we describe Fénix, a data model for exchanging information between Natural Language Processing applications. The format proposed is intended to be flexible enough to cover both current and future data structures employed in the field of Computational Linguistics. The Fénix architecture is divided into four separate layers: conceptual, logical, persistence and physical. This division provides a simple interface to abstract the users from low-level implementation details, such as programming languages and data storage employed, allowing them to focus in the concepts and processes to be modelled. The Fénix architecture is accompanied by a set of programming libraries to facilitate the access and manipulation of the structures created in this framework. We will also show how this architecture has been already successfully applied in different research projects.

German companies strengthen their cooperation with Russian gas suppliers. OSW Commentary No. 64, 2011-10-24

Germany’s decision to give up the use of nuclear energy will force it to find a conventional low-carbon energy source as a replacement; in the short term, in addition to coal, this is likely to be gas. Due to their continued high debt and the losses associated with the end of atomic power, German companies will not be able to spend large funds on investing in conventional energy. First of all, they will aim to raise capital and repay their debts. The money for this will come from selling off their less profitable assets; this will include sales on the gas market. This will create opportunities for natural gas exporters and extraction companies such as Gazprom to buy back some of the German companies’ assets (electricity companies, for example). The German companies will probably continue to seek to recover the costs incurred in the investment projects already underway, such as Nord Stream, the importance of which will grow after Russian gas imports increase. At the same time, because of their debts, the German companies will seek to minimise their investment costs by selling some shares on the conventional energy market, to Russian corporations among others; the latter would thus be able to increase their stake in the gas market in both Western (Germany, Great Britain, the Benelux countries) and Central Europe (Poland, the Czech Republic). It is possible that while establishing the details of cooperation between the Russian and German companies, Russia will try to put pressure on Germany to give up competing projects such as Nabucco. However, a well-diversified German energy market should be able to defend itself against attempts to increase German dependence on Russian gas supplies and the dictates of high prices.

The Fall of the East German Birth Rate After Unification: Crisis or Means of Adaptation? CES Germany & Europe Working Papers, No. 05.6, 26 June 1995

Since the fall of the Wall, Eastern Germans have drastically changed their demographic behavior. Marriages and births have dropped to an unprecedented low level. Our paper tracks birth rates of the East German population, past, present, and future. We propose a simulation model of future cohort fertility. The hypotheses we develop build on the historical record of reproductive behavior in the German Democratic Republic (GDR) since 1960 and on an analysis of the pattern of change between 1990 and 1994. The particular emphasis lies in the assumption that East German couples will rapidly westernize their family size by trying to reach completed fertility levels of the corresponding West German cohort. This implies that the resulting adaptation process includes the postunification crisis as a logical first step.

The Importance of Foreign Language Skills in the Labour Markets of Central and Eastern Europe: An assessment based on data from online job portals. CEPS Special Report No. 129/January 2016

In a globalised world, knowledge of foreign languages is an important skill. Especially in Europe, with its 24 official languages and its countless regional and minority languages, foreign language skills are a key asset in the labour market. Earlier research shows that over half of the EU27 population is able to speak at least one foreign language, but there is substantial national variation. This study is devoted to a group of countries known as the Visegrad Four, which comprises the Czech Republic, Hungary, Poland and Slovakia. Although the supply of foreign language skills in these countries appears to be well-documented, less is known about the demand side. In this study, we therefore examine the demand for foreign language skills on the Visegrad labour markets, using information extracted from online job portals. We find that English is the most requested foreign language in the region, and the demand for English language skills appears to go up as occupations become increasingly complex. Despite the cultural, historical and economic ties with their German-speaking neighbours, German is the second-most-in-demand foreign language in the region. Interestingly, in this case there is no clear link with the complexity of an occupation. Other languages, such as French, Spanish and Russian, are hardly requested. These findings have important policy implications with regards to the education and training offered in schools, universities and job centres.

Comparative Analysis of Existing Guidelines for Gender-Fair Language within the ITN LCG Network. ITN-LCG, Workpackage B: Report on Milestone 1

Guidelines for a gender-fair use of the languages represented in the ITN LCG network were analyzed comparatively for specific criteria. All institutional or governmental guidelines aim at attenuating male-biased representations that are brought about by certain grammatical structures of the respective language. These guidelines primarily focus on the use of masculine forms as generics because they reduce the visibility of women in language. The comparison shows that guidelines for English, a language without grammatical gender, emphasize neutralization as a means of referring to both sexes. This differs from grammatical gender languages, such as German and Italian, in which feminine-masculine word-pairs are recommended in order to avoid the masculine bias. The guidelines all aim to promote the formulation of comprehensive and readable texts that are free of discrimination.

Multiple regulatory variants located in cell type-specific enhancers within the PKP2 locus form major risk and protective haplotypes for canine atopic dermatitis in German shepherd dogs.

BACKGROUND Canine atopic dermatitis (CAD) is a chronic inflammatory skin disease triggered by allergic reactions involving IgE antibodies directed towards environmental allergens. We previously identified a ~1.5 Mb locus on canine chromosome 27 associated with CAD in German shepherd dogs (GSDs). Fine-mapping indicated association closest to the PKP2 gene encoding plakophilin 2. RESULTS Additional genotyping and association analyses in GSDs combined with control dogs from five breeds with low-risk for CAD revealed the top SNP 27:19,086,778 (p = 1.4 × 10(-7)) and a rare ~48 kb risk haplotype overlapping the PKP2 gene and shared only with other high-risk CAD breeds. We selected altogether nine SNPs (four top-associated in GSDs and five within the ~48 kb risk haplotype) that spanned ~280 kb forming one risk haplotype carried by 35 % of the GSD cases and 10 % of the GSD controls (OR = 5.1, p = 5.9 × 10(-5)), and another haplotype present in 85 % of the GSD cases and 98 % of the GSD controls and conferring a protective effect against CAD in GSDs (OR = 0.14, p = 0.0032). Eight of these SNPs were analyzed for transcriptional regulation using reporter assays where all tested regions exerted regulatory effects on transcription in epithelial and/or immune cell lines, and seven SNPs showed allelic differences. The DNA fragment with the top-associated SNP 27:19,086,778 displayed the highest activity in keratinocytes with 11-fold induction of transcription by the risk allele versus 8-fold by the control allele (pdifference = 0.003), and also mapped close (~3 kb) to an ENCODE skin-specific enhancer region. CONCLUSIONS Our experiments indicate that multiple CAD-associated genetic variants located in cell type-specific enhancers are involved in gene regulation in different cells and tissues. No single causative variant alone, but rather multiple variants combined in a risk haplotype likely contribute to an altered expression of the PKP2 gene, and possibly nearby genes, in immune and epithelial cells, and predispose GSDs to CAD.

Chips from a German workshop.

v.1 Recent essays and addresses -- v.2. Biographical essays -- v.3. Essays on language and literature -- v.4. Essays on mythology and folk-lore.