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This article presents preliminary results indicating that speakers have a different pitch range when they speak a foreign language compared to the pitch variation that occurs when they speak their native language. To this end, a learner corpus with French and German speakers was analyzed. Results suggest that speakers indeed produce a smaller pitch range in the respective L2. This is true for both groups of native speakers. A possible explanation for this finding is that speakers are less confident in their productions, therefore, they concentrate more on segments and words and subsequently refrain from realizing pitch range more native-like. For language teaching, the results suggest that learners should be trained extensively on the more pronounced use of pitch in the foreign language.
Gegenstand des Aufsatzes sind Sätze mit so genannten inneren Objekten, das sind Akkusativobjekte, die im Wesentlichen intransitive Verben gelegentlich zu sich nehmen. Sie weisen die Besonderheit auf, dass das Objektsnomen und das Verb morphologisch, etymologisch und/oder semantisch miteinander verwandt sind. Aufgrund von Form- und vor allem Bedeutungsunterschieden lassen sich in beiden Sprachen verschiedene Gruppen von inneren Objekten ausmachen, die genauer beschrieben und unter sprachvergleichenden Gesichtspunkten betrachtet werden. Dazu werden u.a. die syntaktischen Eigenschaften von Sätzen mit inneren Objekten herangezogen. Einige auffallende sprachbezogene Unterschiede werden beschrieben, beispielsweise ist im Rumänischen bei einigen Verben ein präpositionaler Anschluss möglich, wo im Deutschen das innere Objekt ausschließlich im Akkusativ stehen kann. Sätze mit inneren Objekten können als ein Typ von Argumentstrukturmustern betrachtet werden. In diesem Sinne sind sie Form-Bedeutungs-Paare, deren Beziehungen untereinander innerhalb eines Konzepts von Familienähnlichkeiten dargestellt werden, wie man sie auch innerhalb anderer Cluster von Argumentstrukturmustern beobachten kann.
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 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.
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.
Communication across all language barriers has long been a goal of humankind. In recent years, new technologies have enabled this at least partially. New approaches and different methods in the field of Machine Translation (MT) are continuously being improved, modified, and combined, as well. Significant progress has already been achieved in this area; many automatic translation tools, such as Google Translate and Babelfish, can translate not only short texts, but also complete web pages in real time. In recent years, new advances are being made in the mobile area; Googles Translate app for Android and iOS, for example, can recognize and translate words within photographs taken by the mobile device (to translate a restaurant menu, for instance). Despite this progress, a “perfect” machine translation system seems to be an impossibility because a machine translation system, however advanced, will always have some limitations. Human languages contain many irregularities and exceptions, and consequently go through a constant process of change, which is difficult to measure or to be processed automatically. This paper gives a short introduction of the state of the art of MT. It examines the following aspects: types of MT, the most conventional and widely developed approaches, and also the advantages and disadvantages of these different paradigms.