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BACKGROUND: Cultural competence development in the formative process of healthcare professionals is crucial for the provision of culturally appropriate health care. This educational issue is highly relevant in the growing multicultural composition of southern Chile. The objective of this study was to examine how the healthcare professions curricula at the Universidad de La Frontera, in La Araucanía Region, prepares future professionals to respond to patients' cultural needs. METHOD: A sequential transformative mixed methods design composed of two phases was carried out. Phase 1 reviewed all printed material and documentation to explore content that developed cross-cultural skills and competencies in the curricula. In Phase 2 semi-structured interviews were conducted with academics with responsibilities for the development of the curriculum in each career, to detect how academics envisage the incorporation of cultural competence in the curricula. RESULTS: Regarding curricular contents, findings indicated that the healthcare professions curricula at The Universidad de La Frontera have similar approaches to the inclusion of CCT in subjects' syllabuses, with inclusion of the different CCT, particularly in the Dental and Medical curricula. However, this coverage showed significant variations in the undergraduate healthcare curricula. The analysis revealed that themes around the Ethics and human values for professional practice; the Psychosocial and cultural determinants of health; the Relationship health-family-community, and to a lesser extent, the Clinician-patient relationship were well covered in the courses. On the other hand, Inequalities in health was the theme with the least contact time in all three courses. Academics called for a better organisation of the inclusion of CCT in the curricula. They also highlighted the challenges of maintaining the dominant paradigm underlying healthcare models, practices, and orientations within the academic staff and health disc

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Fluorine-18 labelled prosthetic groups (PGs) are often necessary for radiolabelling sensitive biological molecules such as peptides and proteins. Several shortcomings, however, often diminish the final yield of radiotracer. In an attempt to provide higher yielding and operationally efficient tools for radiolabelling biological molecules, we describe herein the first radiochemical synthesis of [18F]ethenesulfonyl fluoride ([18F]ESF) and its Michael conjugation with amino acids and proteins. The synthesis of [18F]ESF was optimised using a microfluidic reactor under both carrier-added (c.a.) and no-carrier-added (n.c.a.) conditions, affording, in a straightforward procedure, 30-50% radiochemical yield (RCY) for c.a. [18F]ESF and 60-70% RCY for n.c.a. [18F]ESF. The conjugation reactions were performed at room temperature using 10 mg/mL precursor in aqueous/organic solvent mixtures for 15 min. The radiochemical stability of the final conjugates was evaluated in injectable formulation and rat serum, and resulted strongly substrate dependent and generally poor in rat serum. Therefore, in this work we have optimised a straightforward synthesis of [18F]ESF and its Michael conjugation with model compounds, without requiring chromatographic purification. However, given the general low stability of the final products, further studies will be required for improving conjugate stability, before assessing the use of this PG for PET imaging.

Over the last 40 years, Artificial Intelligence has established itself in the legal domain. A lot of research has been done concerning the modeling of law and legal reasoning. However, one obstacle still remains and has been neglected by the Artificial Intelligence and Law community: the natural language barrier. In this paper, we will give a short overview of the few projects in the legal domain which use natural language processing techniques to achieve their goals. We believe the time has come for computational linguists to actively participate in this field and apply the recent advances in natural language processing to it.

Attempto Controlled English (ACE) is a mature and expressive knowledge representation language. To test the limits of ACE's expressivity we describe ACE and its associated tools in ACE itself. While this abstract and the reference section use unconstrained English, the rest of the article is expressed in ACE and can be submitted to the Attempto Parsing Engine (APE). The plain text version of the content of this paper and the lexicon needed to parse it can be found online.

In the project “Text+Berg” we digitize all yearbooks of the Swiss Alpine Club from 1864 until today. The books comprise articles in German, French and Italian, a total of around 100,000 pages. This paper describes the corpus and the project phases towards its digitalization. We then focus on the classification of named entities, in particular geographic entities. We explore the usefulness of a large list of geographical names that is distributed by the Swiss Federal Office of Topography. A first experiment indicates that the recognition and classification of geographical names remains difficult despite the large gazetteer.

In this paper we present an evaluation of rule-based morphological components for German for use in an interactive editing environment. The criteria for the evaluation are deduced from the intended use of these components, namely availability, performance, programming interfaces, and analysis quality. We evaluated systems developed and maintained since decades as well as new systems. However, we note serious general shortcomings when looking closer at recent implementations and come to the conclusion that the oldest system is the only one that satisfies our requirements.

We introduce the task of word and phrase-level polarity annotation for German as part of an attempt to develop a compositional theory of clause-level polarity determination. Thus, annotations should give access to the nested building blocks, the structural strata of polarity composition. Therefore and in contrast to existing polarity-tagged corpora, we annotate not exclusively on the basis of surface strings, but argue that proper polarity annotation of complex phrases requires access to their syntactic structures. We discuss the principles of our treebank design, and present the inter-annotator agreement of our kick-off annotations on a test suite of 270 sentences that was compiled specifically to contain interesting polarity combinations.

We introduce an incremental model for coreference resolution that competed in the CoNLL 2011 shared task (open regular). We decided to participate with our baseline model, since it worked well with two other datasets. The benefits of an incremental over a mention-pair architecture are: a drastic reduction of the number of candidate pairs, a means to overcome the problem of underspecified items in pairwise classification and the natural integration of global constraints such as transitivity. We do not apply machine learning, instead the system uses an empirically derived salience measure based on the dependency labels of the true mentions. Our experiments seem to indicate that such a system already is on par with machine learning approaches.

We introduce our incremental coreference resolution system for the BioNLP 2011 Shared Task on Protein/Gene Znteraction. The benefits of an incremental architecture over a mentionpair model are: a reduction of the number of candidate pairs, a means to overcome the problem of underspecified items in pair-wise classification and the natural integration of global constraints such as transitivity. A filtering system takes into account specific features of different anaphora types. We do not apply Machine Learning, instead the system classifies with an empirically derived salience measure based on the dependency labels of the true mentions. The OntoGene pipeline is used for preprocessing.

The work presented here is an approach to Sentiment Analy- sis from a rule-based, compositional perspective. The proposed approach is characterized by three major points: (a) rules are automatically learned from annotated corpora using Inductive Logic Programming and represented as Prolog sets of clauses, (b) the focus is on the noun-phrase (NP) level, and (c) learning is performed on deep-parsed structures. We describe the process of annotating a collection of some 3000 German NPs of medium to quite complex structure, as well as the empirical evaluation of our implementation, in comparison with commonly used classifiers and a handcrafted rule-based system.

We introduce a Web-based CALL architecture that facilitates the construction of learner-customized multiple choice tests in a cross-lingual tandem language learning environment. Mistakes made by the learner are manually corrected and classified by his tandem partner, who acts as a tutor. If the learner has problems to identify and correct his mistakes, or if he likes to practice, he can generate two kinds of tests that are automatically produced on the basis of the tutor’s error classifications. We describe the NLP components behind our test generator, give a preliminary evaluation of the quality of the generated tests, and present the current state of our Web application.

We introduce an incremental entity-mention model for coreference resolution. Our experiments show that it is superior to a non-incremental version in the same environment. The benefits of an incremental architecture are: a reduction of the number of candidate pairs, a means to overcome the problem of underspecified items in pairwise classification and the natural integration of global constraints such as transitivity. Additionally, we have defined a simple salience measure that - coupled with the incremental model - proved to establish a challenging baseline which seems to be on par with machine learning based systems of the 2010's SemEval shared task.

We have manually curated a polarity lexicon for German, comprising word polarities and polarity strength values of about 8,000 words: nouns, verbs and adjectives. The decisions were primarily carried out using the synsets from GermaNet, a WordNet-like lexical database. In an evaluation on German novels, it turned out that the stock of adjectives was too small. We carried out experiments to automatically learn new subjective adjectives together with their polarity orientation and polarity strength. For this purpose, we applied a corpus-based approach that works with pairs of coordinated adjectives extracted from a large German newspaper corpus. In the context of this work, we evaluated two subtasks in detail. First, how good are we at reproducing the polarity classification – including our three- level strength measure – contained in our initial lexicon by machine learning methods. Second, because adding of training material did not improve the results at the expected rate, we evaluated the human intercoder agreement on polarity classifications in an experiment. The results show that judgements about the strength of polarity do vary considerably between different persons. Given these problems related to the design and automatic augmentation of polarity lexicons, we have successfully experimented with a semi-automatically approach where a list of reliable candidate words (here: adjectives) is generated to ease the manual annotation process.