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The present article describes the first stage of the KorAP project, launched recently at the Institut für Deutsche Sprache (IDS) in Mannheim, Germany. The aim of this project is to develop an innovative corpus analysis platform to tackle the increasing demands of modern linguistic research. The platform will facilitate new linguistic findings by making it possible to manage and analyse primary data and annotations in the petabyte range, while at the same time allowing an undistorted view of the primary linguistic data, and thus fully satisfying the demands of a scientific tool. An additional important aim of the project is to make corpus data as openly accessible as possible in light of unavoidable legal restrictions, for instance through support for distributed virtual corpora, user-defined annotations and adaptable user interfaces, as well as interfaces and sandboxes for user-supplied analysis applications. We discuss our motivation for undertaking this endeavour and the challenges that face it. Next, we outline our software implementation plan and describe development to-date.
Research today is often performed in collaborated projects composed of project partners with different backgrounds and from different institutions and countries. Standards can be a crucial tool to help harmonizing these differences and to create sustainable resources. However, choosing a standard depends on having enough information to evaluate and compare different annotation and metadata formats. In this paper we present ongoing work on an interactive, collaborative website that collects information on standards in the field of linguistics as a means to guide interested researchers.
This document presents ongoing work related to spoken language data within a project that aims to establish a common and unified infrastructure for the sustainable provision of linguistic primary research data at the Institut für Deutsche Sprache (IDS). In furtherance of its mission to “document the German language as it is currently used”, the project expects to enable the research community to access a broad empirical base of working material via a single platform. While the goal is to eventually cover all linguistically relevant digital resources of the IDS, including lexicographic information systems such as the IDS German Vocabulary Portal, OWID, written language corpora such as the IDS German Reference Corpus, DeReKo, and spoken language corpora such as the IDS German Speech Corpus for Research and Teaching, FOLK, the work presented here predominantly focuses on the latter type of data, i.e. speech corpora. Within this context, the present document pictures the project’s contributions to the development of standards and best practice guidelines concerning data storage, process documentation and legal issues for the sustainable preservation and long-term accessibility of primary linguistic research data.
Linguistic query systems are special purpose IR applications. As text sizes, annotation layers, and metadata schemes of language corpora grow rapidly, performing complex searches becomes a highly computational expensive task. We evaluate several storage models and indexing variants in two multi-processor/multi-core environments, focusing on prototypical linguistic querying scenarios. Our aim is to reveal modeling and querying tendencies – rather than absolute benchmark results – when using a relational database management system (RDBMS) and MapReduce for natural language corpus retrieval. Based on these findings, we are going to improve our approach for the efficient exploitation of very large corpora, combining advantages of state-of-the-art database systems with decomposition/parallelization strategies. Our reference implementation uses the German DeReKo reference corpus with currently more than 4 billion word forms, various multi-layer linguistic annotations, and several types of text-specific metadata. The proposed strategy is language-independent and adaptable to large-scale multilingual corpora.
This paper presents an annotation scheme for English modal verbs together with sense-annotated data from the news domain. We describe our annotation scheme and discuss problematic cases for modality annotation based on the inter-annotator agreement during the annotation. Furthermore, we present experiments on automatic sense tagging, showing that our annotations do provide a valuable training resource for NLP systems.