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In 2010, ISO published a standard for syntactic annotation, ISO 24615:2010 (SynAF). Back then, the document specified a comprehensive reference model for the representation of syntactic annotations, but no accompanying XML serialisation. ISO’s subcommittee on language resource management (ISO TC 37/SC 4) is working on making the SynAF serialisation ISOTiger an additional part of the standard. This contribution addresses the current state of development of ISOTiger, along with a number of open issues on which we are seeking community feedback in order to ensure that ISOTiger becomes a useful extension to the SynAF reference model.
This paper describes a first version of an integrated e-dictionary translating possessive constructions from English to Zulu. Zulu possessive constructions are difficult to learn for non-mother tongue speakers. When translating from English into Zulu, a speaker needs to be acquainted with the nominal classification of nouns indicating possession and possessor. Furthermore, (s)he needs to be informed about the morpho-syntactic rules associated with certain combinations of noun classes. Lastly, knowledge of morpho-phonetic changes is also required, because these influence the orthography of the output word forms. Our approach is a novel one in that we combine e-lexicography and natural language processing by developing a (web) interface supporting learners, as well as other users of the dictionary to produce Zulu possessive constructions. The final dictionary that we intend to develop will contain several thousand nouns which users can combine as they wish. It will also translate single words and frequently used multiword expressions, and allow users to test their own translations. On request, information about the morpho-syntactic and morpho-phonetic rules applied by the system are displayed together with the translation. Our approach follows the function theory: the dictionary supports users in text production, at the same time fulfilling a cognitive function.
Alors que de nombreuses études en analyse conversationnelle se sont intéressées à la manière dont des locuteurs co-construisent un tour de parole (notamment sur le plan syntaxique et prosodique), la façon dont la co-construction est ensuite évaluée n'a pas encore été étudiée en profondeur au sein de la littérature interactionniste. Ici, nous étudions deux pratiques permettant à un locuteur de valider une co-construction, à savoir l'acquiescement simple et l'hétéro-répétition de la complétion. En menant une analyse séquentielle et multimodale de plusieurs séquences de co-construction en français, nous montrons qu’à travers ces deux procédés – qui semblent au premier abord similaires dans leur fonctionnement – les locuteurs effectuent une évaluation très différente : tandis que l'acquiescement simple valide la complétion proposée uniquement comme une version possible, l'hétéro-répétition la valide comme étant une complétion complètement adéquate. Cette contribution met en évidence que les interactants exploitent des ressources audibles aussi bien que visibles afin de manifester si et dans quel sens ils acceptent la complétion de leur tour de parole de la part d’un coparticipant. Nous soulignons l’importance d’étudier en détail les différents formatages possibles des tours évaluant une complétion afin de pouvoir distinguer différentes formes « d’acceptation » et de révéler la manière dont les locuteurs peuvent finement négocier leur position en tant que (co-)auteur ou destinataire d’un tour de parole.
Measuring the quality of metadata is only possible by assessing the quality of the underlying schema and the metadata instance. We propose some factors that are measurable automatically for metadata according to the CMD framework, taking into account the variability of schemas that can be defined in this framework. The factors include among others the number of elements, the (re-)use of reusable components, the number of filled in elements. The resulting score can serve as an indicator of the overall quality of the CMD instance, used for feedback to metadata providers or to provide an overview of the overall quality of metadata within a repository. The score is independent of specific schemas and generalizable. An overall assessment of harvested metadata is provided in form of statistical summaries and the distribution, based on a corpus of harvested metadata. The score is implemented in XQuery and can be used in tools, editors and repositories.
Die vorliegende empirische Untersuchung befasst sich mit einer Umfrage zur Wörterbuchbenutzung bei 41 Studentinnen und Studenten des Dipartimento di Filologia, Letteratura e Linguistica der Universität Pisa, dasselbe Department, an dem auch das deutsch-italienische sprachwissenschaftliche Online-Wörterbuch DIL erarbeitet worden ist (vgl. Flinz: 2011). Die schriftliche Umfrage wurde in Anlehnung an Hartmanns 5. Hypothese „An analysis of users´ needs should precede dictionary design“ (1989) durchgeführt. Die wichtigsten Ergebnisse waren von großer Bedeutung für die Gestaltung der makro- und mikrostrukturellen Eigenschaften des Fachwörterbuches. Die Ergebnisse der Untersuchung und die daraus folgenden Reflektionen werden in thematischen Kernblöcken vorgestellt.
We examine the task of relation extraction in the food domain by employing distant supervision. We focus on the extraction of two relations that are not only relevant to product recommendation in the food domain, but that also have significance in other domains, such as the fashion or electronics domain. In order to select suitable training data, we investigate various degrees of freedom. We consider three processing levels being argument level, sentence level and feature level. As external resources, we employ manually created surface patterns and semantic types on all these levels. We also explore in how far rule-based methods employing the same information are competitive.
We examine the task of separating types from brands in the food domain. Framing the problem as a ranking task, we convert simple textual features extracted from a domain-specific corpus into a ranker without the need of labeled training data. Such method should rank brands (e.g. sprite) higher than types (e.g. lemonade). Apart from that, we also exploit knowledge induced by semi-supervised graph-based clustering for two different purposes. On the one hand, we produce an auxiliary categorization of food items according to the Food Guide Pyramid, and assume that a food item is a type when it belongs to a category unlikely to contain brands. On the other hand, we directly model the task of brand detection using seeds provided by the output of the textual ranking features. We also harness Wikipedia articles as an additional knowledge source.
Automatic Food Categorization from Large Unlabeled Corpora and Its Impact on Relation Extraction
(2014)
We present a weakly-supervised induction method to assign semantic information to food items. We consider two tasks of categorizations being food-type classification and the distinction of whether a food item is composite or not. The categorizations are induced by a graph-based algorithm applied on a large unlabeled domain-specific corpus. We show that the usage of a domain-specific corpus is vital. We do not only outperform a manually designed open-domain ontology but also prove the usefulness of these categorizations in relation extraction, outperforming state-of-the-art features that include syntactic information and Brown clustering.
We report on the two systems we built for Task 1 of the German Sentiment Analysis Shared Task, the task on Source, Subjective Expression and Target Extraction from Political Speeches (STEPS). The first system is a rule-based system relying on a predicate lexicon specifying extraction rules for verbs, nouns and adjectives, while the second is a translation-based system that has been obtained with the help of the (English) MPQA corpus.
We present the German Sentiment Analysis Shared Task (GESTALT) which consists of two main tasks: Source, Subjective Expression and Target Extraction from Political Speeches (STEPS) and Subjective Phrase and Aspect Extraction from Product Reviews (StAR). Both tasks focused on fine-grained sentiment analysis, extracting aspects and targets with their associated subjective expressions in the German language. STEPS focused on political discussions from a corpus of speeches in the Swiss parliament. StAR fostered the analysis of product reviews as they are available from the website Amazon.de. Each shared task led to one participating submission, providing baselines for future editions of this task and highlighting specific challenges. The shared task homepage can be found at https://sites.google.com/site/iggsasharedtask/.