Foundation of a component-based flexible registry for language resources and technology

Within the CLARIN e-science infrastructure project it is foreseen to develop a component-based registry for metadata for Language Resources and Language Technology. With this registry it is hoped to overcome the problems of the current available systems with respect to inflexible fixed schema, unsuitable terminology and interoperability problems. The registry will address interoperability needs by refering to a shared vocabulary registered in data category registries as they are suggested by ISO

Final FLaReNet deliverable: Language Resources for the Future - The Future of Language Resources

Language Technologies (LT), together with their backbone, Language Resources (LR), provide an essential support to the challenge of Multilingualism and ICT of the future. The main task of language technologies is to bridge language barriers and to help creating a new environment where information flows smoothly across frontiers and languages, no matter the country, and the language, of origin. To achieve this goal, all players involved need to act as a community able to join forces on a set of shared priorities. However, until now the field of Language Resources and Technology has long suffered from an excess of individuality and fragmentation, with a lack of coherence concerning the priorities for the field, the direction to move, not to mention a common timeframe. The context encountered by the FLaReNet project was thus represented by an active field needing a coherence that can only be given by sharing common priorities and endeavours. FLaReNet has contributed to the creation of this coherence by gathering a wide community of experts and making them participate in the definition of an exhaustive set of recommendations

Using the European Language Grid as a consumer

This chapter describes the European Language Grid cloud platform from the point of view of a consumer who wishes to access language resources or make use of language technology tools and services. Three aspects are discussed: 1. the webbased user interface (UI) for casual and non-technical users, 2. the underlying REST APIs that drive the UI but can also be called directly by third parties to integrate ELG functionality in their own tools, and 3. the Python Software Development Kit (SDK) that we have developed to simplify access to these APIs from Python code. The chapter concludes with a preview of the upcoming payment module that will enable the sale of commercial LT services and resources through ELG, and a discussion of how ELG compares and relates to other similar platforms and initiatives

Contributing to the European Language Grid as a provider

The ELG platform enables producers of language resources and language technology tools and services to upload, describe, share, and distribute their services and products as well as to describe their companies, academic organisations and projects. This chapter presents the functionalities offered through web-based user interfaces for describing LT resources or related entities with metadata and for managing their publication. It gives a detailed description of the options that providers of LT tools can exploit to integrate them into ELG as ready-to-deploy services and the tools that ELG offers in their support during the preparation, upload and integration phases. The tools and packaging recommendations for resources to be uploaded in ELG are also presented. The chapter concludes with a discussion of functionalities offered to providers by ELG and other related platforms

Discovering the Language of Wine Reviews: A Text Mining Account

It is widely held that smells and flavors are impossible to put into words. In this paper we test this claim by seeking predictive patterns in wine reviews, which ostensibly aim to provide guides to perceptual content. Wine reviews have previously been critiqued as random and meaningless. We collected an English corpus of wine reviews with their structured metadata, and applied machine learning techniques to automatically predict the winetextquotesingles color, grape variety, and country of origin. To train the three supervised classifiers, three different information sources were incorporated: lexical bag-of-words features, domain-specific terminology features, and semantic word embedding features. In addition, using regression analysis we investigated basic review properties, i.e., review length, average word length, and their relationship to the scalar values of price and review score. Our results show that wine experts do share a common vocabulary to describe wines and they use this in a consistent way, which makes it possible to automatically predict wine characteristics based on the review text alone. This means that odors and flavors may be more expressible in language than typically acknowledged