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The CLARIN Research Infrastructure: Resources and Tools for eHumanities Scholars

CLARIN is the short name for the Common Language Resources and Technology Infrastructure, which aims at providing easy and sustainable access for scholars in the humanities and social sciences to digital language data and advanced tools to discover, explore, exploit, annotate, analyse or combine them, independent of where they are located. CLARIN is in the process of building a networked federation of European data repositories, service centers and centers of expertise, with single sign-on access for all members of the academic community in all participating countries. Tools and data from different centers will be interoperable so that data collections can be combined and tools from different sources can be chained to perform complex operations to support researchers in their work. Interoperability of language resources and tools in the federation of CLARIN Centers is ensured by adherence to TEI and ISO standards for text encoding, by the use of persistent identifiers, and by the observance of common protocols. The purpose of the present paper is to give an overview of language resources, tools, and services that CLARIN presently offers.


Introduction
The digital age has opened up entirely new ways for researchers in all fields of science to gain easy access to large amounts of electronically available data, tools and services to analyse and visualize the contents of such datasets, and even more importantly to collaborate on joint research projects, relying on virtual research environments and geographically distributed research infrastructures as a backbone for such collaborations. The term e-Science has been coined to refer to this new paradigm for conducting research. This recent trend has also extended to research in the humanities and social sciences, where this innovative approach is subsumed by the terms eHumanities or digital humanities.

Goals of CLARIN
CLARIN is the short name for the Common Language Resources and Technology Infrastructure, which aims at providing easy and sustainable access for scholars in the humanities and social sciences (HSS) to digital language data (in written, spoken, video or multimodal form) and advanced tools to discover, explore, exploit, annotate, analyse or combine them, independent of where they are located. CLARIN  Netherlands. Norway has the status of an observer country and is expected to join as a full member shortly, as are several other European countries. CLARIN is in the process of building a networked federation of European data repositories, service centers and centers of expertise, with single sign-on access for all members of the academic community in all participating countries. Tools and data from different centers will be interoperable so that data collections can be combined and tools from different sources can be chained to perform complex operations to support researchers in their work. At this moment the CLARIN infrastructure is still under construction, but a number of participating centers are already offering access services to data, tools and expertise. The purpose of the present paper is to give an overview of language resources, tools, and services that CLARIN presently offers.

Data Discovery, Acquisition, Data Curation
Every eHumanities project, regardless of whether it is a quantitative or a qualitative study, is grounded in a dataset. In a quantitative study, such a dataset is typically subjected to statistical analysis of some sort, in order to (dis-)confirm a particular hypothesis. In a qualitative study, the initial hypothesis may not be completely clear yet and is often developed in the process of exploring a dataset.

Reference Datasets
Often investigators know at the outset of their project, which datasets need to be consulted. Such datasets are typically reference sets that are well-known in the scientific community and that are often consulted to ensure that the results obtained in a particular study can be compared to previous studies performed on the same dataset. The federation of CLARIN centers offers a high number of such reference datasets for the languages represented by the CLARIN member countries. The   (Hamp & Feldweg 1997;Henrich & Hinrichs 2010), and Norwegian (Fjeld & Nygaard, L. 2010). In addition to reference datasets, CLARIN provides access to an extensive set of metadata records. The Virtual Language Observatory (VLO; van Uytvanck et al. 2010) currently contains more than 500.000 metadata records to language resources and tools. Facetted search and a visual map provide easy-to-use interfaces for HSS scholars to locate language resources and tools that match the needs in a particular research project. The screen shot in Figure 1 shows the VLO web application and displays the metadata for a resource hosted by the CLARIN Center in Munich as the result of a facetted VLO search for speech recordings for the German language.

Creation of New Resources
If an eHumanities study requires the construction of new digital datasets, this is usually due to the fact that no existing resource can provide the answer to a particular research question. In fact, it may be the main goal of the project to fill a gap in the existing set of resources for a particular research question or for a particular language. Resource creation is a costly effort, especially if significant manual effort is required. Special care must therefore be taken that such data creation efforts (i) adhere to best practises or standards for text encoding whenever possible and (ii) are guided by a data management plan that covers the entirely data lifecycle from data collection, to data sharing and data archiving.
HSS scholars often lack the necessary experience or access to data repositories to meet these expectations. The CLARIN-D User Guide (Herold & Lemnitzer 2012) provides practical information on the use of standards for language resources and on following good practises in data creation.

Query Tools and Federated Content Search
Since datasets available in electronic form are typically very large, CLARIN centers support HSS scholars by providing powerful and easy-to-use query tools for many of the resources described above. Access is greatly facilitated if such query tools are realized as web applications and thus available in any web browser. Two good examples of this kind are the web application for querying the German Text Archive (Jurish & Thomas & Wiegand 2014). In addition to query interfaces for individual resources, CLARIN offers a Federated Content Search (FCS) functionality that enables HSS scholars to construct a virtual corpus collection hosted by different CLARIN centers and to query this virtual corpus via a common search interface. Currently, nine CLARIN centers in Germany and in the Netherlands make more than 20 resources available to the linguistic researches via the common interface of the CLARIN-D Federated Content Search, and this number is steadily growing. The CLARIN Center at the University of Oslo also provides

Work Flows for Data Annotation
Language data that are annotated with linguistic information can be searched with high accuracy for specific data patterns. The CLARIN Centers in Oslo, Prague, Tübingen and at the Dutch Language Union offer linguistically annotated corpora, so-called If a collection of language resources does not contain sufficient linguistic information, for example if the word forms in a corpus have not been lemmatized, it is impossible to obtain meaningful word frequency distributions. Likewise, if an HSS scholar wants to search for all person names in a very large newspaper corpus in order obtain an overview of who is currently in the news, then the person names in such a corpus needs to be marked up. CLARIN offers support for HSS scholars who need to add annotations of this kind. The web application WebLicht (Hinrichs & Hinrichs & Zastrow 2010), hosted by the CLARIN Center in Tübingen, is a tool-suite for automatic annotation of text corpora. Linguistic tools such as tokenizers, part of speech taggers can be combined into custom processing chains. The resulting annotations can then be visualized in an appropriate way, such as in a table or tree format. WebLichts's graphical user interface in Figure 2 shows the annotation of a German text corpus with five different subtypes of named entities (location, person, organization, geo-political entity, and other) in table format along with the processing chain of five different web services that produced these layers of linguistic annotation. Recently the WebLicht tool suite has been extended to spoken language. This can be achieved with the integration of the webservice WebMaus (Kisler & Schiel & Sloetjes 2012), which is provided by the CLARIN Center in Munich. WebMaus takes as input an audio file and its transcription and automatically aligns the speech signal with its transcriptions. The WebLicht tool can then further annotate the transcriptions so that via the automatic alignment, a user can find the relevant portions of the speech signal for particular data patterns.

Data Visualization
A key aspect to a successful eHumanities project involves visualization tools that render the data analysis results in an easy-to-grasp fashion. This is particularly important if the datasets involved are very large. Accordingly, data visualization has become an active research field in its own right within digital humanities research. While CLARIN cannot and does not want to claim that it can provide a comprehensive suite of eHumanities visualization tools, it can already support HSS scholars with a number of helpful applications. The CLARIN Center at the University of Copenhagen has developed a visualization tool for parallel inspection of word nets. The Microcomparative Morphosyntactic Research (MIMORE) tool offered by the CLARIN Center at the Meertens Institute in Amsterdam enables researchers to investigate morphosyntactic variation in Dutch dialects. MIMORE accesses three related databases with a common on-line search engine. Figure 3 shows the MIMORE search results for the 2 nd person singular form bist of the copula verb zein (English: be). This verb form is restricted particular dialects of Dutch and differs from bent, the corresponding verb form in Standard Dutch. Since the language data in the three databases searchable by MIMORE contain geographical references, the search results can be automatically plotted on a geographic map. Visualizations of this kind provide a natural way to identify dialectal variation, as in the case of the word bist: The results all cluster in the northern part of the Netherlands and along the border with Germany. This is not surprising since German uses the same verb form.
The CLARIN Center at the University of Tübingen provides access to the web application CiNaViz (short for: City Name Visualisation). CiNaViz is a search interface for more than a million geographical locations all over Europe and automatically displays the search results on a geographical map. Figure 4 shows the distribution of city names ending in bach (red), beck (blue) and bek (green) in Central Europe. Interestingly, the dividing line between bach and beck follows the famous Benrath Line that separates northern from southern German dialects. As with the MIMORE tool, CiNaViz offers a valuable tool for HSS scholars interested in language variation.

Data Sharing and Data Archiving
The last stage in an eHumanities project typically involves the sharing, publishing and archiving of the datasets that were created, annotated or analysed during the project. CLARIN provides support for this phase of an eHumanities project by providing SimpleStore and OwnCloud solutions for data sharing and for collaborative work on the same dataset. Many

Technical Aspects
Interoperability of language resources and tools in the federation of CLARIN Centers is ensured by adherence to TEI and ISO standards for text encoding, by the use of persistent identifiers as long-lasting references to digital language data as well as by the observance of common protocols: Shibboleth for user authentication and authorization, SRU/CQL for Federated Contents Search, and OAI-PMH for metadata harvesting.

Conclusion and Outlook
Here we could describe only a subset of the resources and tools available in CLARIN. Section 10 (References) of this paper provides URLs for many of the ones mentioned in the present paper. For comprehensive and up-to-date information about all CLARIN resources and tools, we refer interested readers to the CLARIN homepage: http://www.clarin.eu/.

Acknowledgements
We would like to thank all CLARIN centers for their profound contributions to the sharing of resources, tools, and knowledge as well as all CLARIN ERIC members for their financial support in the preparation and construction of the CLARIN infrastructure. All CLARIN centers owe a special thank you to Peter Wittenburg of the Max-Planck Institute for Psycholinguistics in Nijmegen, whose vision was essential in the conception of CLARIN and in the blue print for its architecture. The research and development of the first author are supported by grants from the Bundesministerium für Bildung und Forschung (BMBF), from the Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, and from the Eberhard Karls Universität Tübingen. The second author expresses his gratitude to Utrecht University for their long-standing support as the host institution of CLARIN ERIC. Both authors are grateful to their colleagues on the Board of Directors of CLARIN ERIC for their support and for their comments on earlier drafts of this paper.