Korpuslinguistik
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We present an approach to an aspect of managing complex access scenarios to large and heterogeneous corpora that involves handling user queries that, intentionally or due to the complexity of the queried resource, target texts or annotations outside of the given user’s permissions. We first outline the overall architecture of the corpus analysis platform KorAP, devoting some attention to the way in which it handles multiple query languages, by implementing ISO CQLF (Corpus Query Lingua Franca), which in turn constitutes a component crucial for the functionality discussed here. Next, we look at query rewriting as it is used by KorAP and zoom in on one kind of this procedure, namely the rewriting of queries that is forced by data access restrictions.
Beyond Citations: Corpus-based Methods for Detecting the Impact of Research Outcomes on Society
(2020)
This paper proposes, implements and evaluates a novel, corpus-based approach for identifying categories indicative of the impact of research via a deductive (top-down, from theory to data) and an inductive (bottom-up, from data to theory) approach. The resulting categorization schemes differ in substance. Research outcomes are typically assessed by using bibliometric methods, such as citation counts and patterns, or alternative metrics, such as references to research in the media. Shortcomings with these methods are their inability to identify impact of research beyond academia (bibliometrics) and considering text-based impact indicators beyond those that capture attention (altmetrics). We address these limitations by leveraging a mixed-methods approach for eliciting impact categories from experts, project personnel (deductive) and texts (inductive). Using these categories, we label a corpus of project reports per category schema, and apply supervised machine learning to infer these categories from project reports. The classification results show that we can predict deductively and inductively derived impact categories with 76.39% and 78.81% accuracy (F1-score), respectively. Our approach can complement solutions from bibliometrics and scientometrics for assessing the impact of research and studying the scope and types of advancements transferred from academia to society.
Co-reference annotation and resources: a multilingual corpus of typologically diverse languages
(2002)
This article introduces a dialogue corpus containing data from two typologically different languages, Japanese and Kilivila. The corpus is annotated in accordance with language specific annotation schemes for co-referential and similar relations. The article describes the corpus data, the properties of language specific co-reference in the two languages and a methodology for its annotation. Examples from the corpus show how this methodology is used in the workflow of the annotation process.
This paper describes a corpus of Japanese task-oriented dialogues, i.e. its data, annotations, analysis methodology and preliminary results for the modeling of co-referential phenomena. Current corpus based approaches to co-reference concentrate on textual data from English or other European languages. Hence, the emerging language-general models of co-reference miss input from dialogue data of non-European languages. We aim to fill this gap and contribute to a model of co-reference on various language-specific and language-general levels.
This paper proposes a methodology for querying linguistic data represented in different corpus formats. Examples of the need for queries over such heterogeneous resources are the corpus-based analysis of multimodal phenomena like the interaction of gestures and prosodic features, or syntax-related phenomena like information structure which exceed the expressive power of a tree-centered corpus format. Query languages (QLs) currently under development are strongly connected to corpus formats, like the NITE Object Model (NOM, Carletta et al., 2003) or the Meta-Annotation Infrastructure for ATLAS (MAIA, Laprun and Fiscus, 2002). The parallel development of linguistic query languages and corpus formats is due to the fact that general purpose query languages like XQuery (Boag et al., 2003) do not fulfill the changing needs of linguistically motivated queries, e.g. to give access to (non-)hierarchically organized, theory and language dependent annotations of multi modal signals and/or text. This leads to the problem that existing corpus formats and query languages are hard to reuse. They have to be re developed and re-implemented time-consumingly and expensively for unforeseen tasks. This paper describes an approach for overcoming these problems and a sample application.
The present paper describes Corpus Query Lingua Franca (ISO CQLF), a specification designed at ISO Technical Committee 37 Subcommittee 4 “Language resource management” for the purpose of facilitating the comparison of properties of corpus query languages. We overview the motivation for this endeavour and present its aims and its general architecture. CQLF is intended as a multi-part specification; here, we concentrate on the basic metamodel that provides a frame that the other parts fit in.
This paper deals with the problem of how to interrelate theory-specific treebanks and how to transform one treebank format to another. Currently, two approaches to achieve these goals can be differentiated. The first creates a mapping algorithm between treebank formats. Categories of a source format are transformed into a target format via a given set of general or language-specific mapping rules. The second relates treebanks via a transformation to a general model of linguistic categories, for example based on the EAGLES recommendations for syntactic annotations of corpora, or relying on the HPSG framework. This paper proposes a new methodology as a solution for these desiderata.