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The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years. The main conference papers deal with this topic from different points of view, involving flat as well as deep representations, automatic methods targeting annotation and hybrid symbolic and statistical processing, as well as new Machine Learning-based approaches, but also the creation of language resources for both machines and humans, and methods for testing the latter to optimize their human-machine interaction properties. In line with the general topic, KONVENS-2014 focuses on areas of research which involve this cooperation of information science and computational linguistics: for example learning-based approaches, (cross-lingual) Information Retrieval, Sentiment Analysis, paraphrasing or dictionary and corpus creation, management and usability.
The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years.
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.
So far, there have been few descriptions on creating structures capable of storing lexicographic data, ISO 24613:2008 being one of the latest. Another one is by Spohr (2012), who designs a multifunctional lexical resource which is able to store data of different types of dictionaries in a user-oriented way. Technically, his design is based on the principle of a hierarchical XML/OWL (eXtensible Markup Language/Web Ontology Language) representation model. This article follows another route in describing a model based on entities and relations between them; MySQL (usually referred to as: Structured Query Language) describes a database system of tables containing data and definitions of relations between them. The model was developed in the context of the project "Scientific eLexicography for Africa" and the lexicographic database to be built thereof will be implemented with MySQL. The principles of the ISO model and of Spohr's model are adhered to with one major difference in the implementation strategy: we do not place the lemma in the centre of attention, but the sense description — all other elements, including the lemma, depend on the sense description. This article also describes the contained lexicographic data sets and how they have been collected from different sources. As our aim is to compile several prototypical internet dictionaries (a monolingual Northern Sotho dictionary, a bilingual learners' Xhosa–English dictionary and a bilingual Zulu–English dictionary), we describe the necessary microstructural elements for each of them and which principles we adhere to when designing different ways of accessing them. We plan to make the model and the (empty) database with all graphical user interfaces that have been developed, freely available by mid-2015.
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.
Vernetzung statt Vereinheitlichung. Digitale Forschungsinfrastrukturen in den Geisteswissenschaften
(2014)
Die Entwicklung der digitalen Infrastruktur am Hamburger Zentrum für Sprachkorpora (HZSK) kann als Beispiel für die Evolution individueller technischer Einzellösungen hin zu fachspezifischen virtuellen Arbeits- und Forschungsumgebungen, die im Rahmen supranationaler Forschungsinfrastrukturen für die digitalen Geisteswissenschaften miteinander vernetzt sind, angesehen werden. Im Fokus steht im konkreten Fall des HZSK die Sicherung der langfristigen Zugänglichkeit von Forschungsdaten (multimedialen Daten gesprochener Sprache) durch die Entwicklung einer virtuellen Forschungsumgebung, die einerseits an die zentrenbasierte Forschungsinfrastruktur CLARIN-D angebunden ist und andererseits fachspezifische Benutzerschnittstellen schafft.
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/.