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Korpuslinguistik
(2012)
Das IDS, insbesondere der Programmbereich Korpuslinguistik, bekommt häufig Anfragen zum Wortbestand der deutschen Sprache, sei es, welche Wörter besonders häufig sind, sei es, nach (Listen von) Wörtern mit bestimmten Eigenschaften. Zu dem Themenschwerpunkt „häufigkeitsbasierte Wortlisten“ wurde unter dem Schlagwort DeReWo eine Plattform eingerichtet, auf der Erkenntnisse und Ergebnisse zu diesem Bereich erarbeitet und veröffentlicht werden (<www.ids-mannheim.de/kl/projekte/methode/derewo.html>). Die Frage nach dem „längsten Wort der deutschen Sprache“ hat zwar gewisse Berührungspunkte zu diesem Schwerpunkt, sie hebt sich aber doch ein wenig ab. Deshalb soll sie an dieser Stelle in Form eines fiktiven Gesprächs thematisiert werden (auch wenn eine konkrete Anfrage für eine Kindersendung den Anlass geliefert hat).
Eine angemessene, sachgemäße Diskussion über Stärken und Schwächen, Möglichkeiten und Grenzen der Korpuslinguistik ist überschattet von vielen Mythen, die sich mittlerweile eingebürgert haben und die in vielen Diskussionen – gerade unter Linguisten – immer wieder aufkommen. An dieser Stelle möchten wir einige der verbreitetsten Mythen zusammenstellen und die Hintergründe aus dieser korpuslinguistischen Perspektive erörtern.
New KARL (Knowledge Acquisition and Representation Language) allows to specify all parts of a problem-solving method (PSM). It is a formal language with a well-defined semantics and thus allows to represent PSMs precisely and unambiguously yet abstracting from implementation detail. In this paper it is shown how the language KARL has been modified and extended to New KARL to better meet the needs for the representation of PSMs. Based on a conceptual structure of PSMs new language primitives are introduced for KARL to specify such a conceptual structure and to support the configuration of methods. An important goal for this extension was to preserve three important properties of KARL: to be (i) a conceptual, (ii) a formal, and (iii) an executable language.
A topic in the field of knowledge acquisition is the reuse of components that are described at the knowledge level. Problems concern the description, indexing and retrieval of components. In our case there is the additional feature of integrating so called automated building blocks in a knowledge level description. This paper describes what knowledge level descriptions of components for reuse should look like, and proposes a way to describe assumptions and requirements that are to be made explicit. In the paper an extension of the “normal” knowledge acquisition setting is made in the direction of machine learning components.
The central issue in corpus-driven linguistics is the detection and description of patterns in language usage. The features that constitute the notion of a pattern can be computed to a certain extent by statistical (collocation) methods, but a crucial part of the notion may vary depending on applications and users. Thus, typically, any computed collocation cluster will have to be interpreted hermeneutically. Often it might be captured by a generalized, more abstract pattern. We present a generic process model that supports the recognition, interpretation, and expression of the patterns inside and of the relations between clusters. By this, clusters can be merged virtually according to any notion of a 'pattern', and their relations can be exploited for different applications
This introductory tutorial describes a strictly corpus-driven approach for uncovering indications for aspects of use of lexical items. These aspects include ‘(lexical) meaning’ in a very broad sense and involve different dimensions, they are established in and emerge from respective discourses. Using data-driven mathematical-statistical methods with minimal (linguistic) premises, a word’s usage spectrum is summarized as a collocation profile. Self-organizing methods are applied to visualize the complex similarity structure spanned by these profiles. These visualizations point to the typical aspects of a word’s use, and to the common and distinctive aspects of any two words.
Empirical synchronic language studies generally seek to investigate language phenomena for one point in time, even though this point in time is often not stated explicitly. Until today, surprisingly little research has addressed the implications of this time-dependency of synchronic research on the composition and analysis of data that are suitable for conducting such studies. Existing solutions and practices tend to be too general to meet the needs of all kinds of research questions. In this theoretical paper that is targeted at both corpus creators and corpus users, we propose to take a decidedly synchronic perspective on the relevant language data. Such a perspective may be realised either in terms of sampling criteria or in terms of analytical methods applied to the data. As a general approach for both realisations, we introduce and explore the FReD strategy (Frequency Relevance Decay) which models the relevance of language events from a synchronic perspective. This general strategy represents a whole family of synchronic perspectives that may be customised to meet the requirements imposed by the specific research questions and language domain under investigation.