To provide AI researchers with modern tools for dealing with the explosive growth of the research literature in their field, we introduce a new platform, AI Research Navigator, that combines classical keyword search with neural retrieval to discover and organize relevant literature. The system provides search at multiple levels of textual granularity, from sentences to aggregations across documents, both in natural language and through navigation in a domain specific Knowledge Graph. We give an overview of the overall architecture of the system and of the components for document analysis, question answering, search, analytics, expert search, and recommendations.

We analyze the performance of encoder-decoder neural models and compare them with well-known established methods. The latter represent different classes of traditional approaches that are applied to the monotone sequence-to-sequence tasks OCR post-correction, spelling correction, grapheme-to-phoneme conversion, and lemmatization. Such tasks are of practical relevance for various higher-level research fields including digital humanities, automatic text correction, and speech recognition. We investigate how well generic deep-learning approaches adapt to these tasks, and how they perform in comparison with established and more specialized methods, including our own adaptation of pruned CRFs.

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.

This paper documents a pilot study conducted as part of the development of a new corpus processing system at the Institut für Deutsche Sprache in Mannheim and in the context of the ISO TC37 SC4/WG6 activity on the suggested work item proposal “Corpus Query Lingua Franca”. We describe the first phase of our research: the initial formulation of functionality criteria for query language evaluation and the results of the application of these criteria to three representatives of corpus query languages, namely COSMAS II, Poliqarp, and ANNIS QL. In contrast to previous works on query language evaluation that compare a range of existing query languages against a small number of queries, our approach analyses only three query languages against criteria derived from a suite of 300 use cases that cover diverse aspects of linguistic research.