The Language Application (LAPPS) Grid project is a collaborative effort among US partners Vassar College, Brandeis Uni- versity, Carnegie-Mellon University, and the Linguistic Data Consortium at the University of Pennsylvania, and is funded by the US National Science Foundation. The project is establishing a framework that enables language service discovery, composition, and reuse and supports state-of-the-art evaluation of natural language Processing (NLP) components. The project builds on the foundation laid in projects such as SILT, the Language Grid, PANACEA, and CLARIN, where significant progress towards interoperability of NLP tools and data has been made, as well as the momentum toward a comprehensive network of web services and resources within the NLP community.
The LAPPS Grid orchestrates access to and deployment of language resources and processing functions available from servers around the globe and enables users to add their own language resources, services, and even service grids to satisfy their particular needs. As such, the LAPPS Grid is ultimately a community-based project, to which services will be contributed by members of the community and existing service repositories and grids can be federated to enable universal access.
The specific goals of the LAPPS Grid project are to: (1) design, develop, and promote a Language Application Grid (LAPPS Grid) to support the development and deployment of integrated natural language applications by providing fully interoperable access to NLP tools and components together with language resources distributed across the globe; (2) establish a federation of grids and services that have been developed in locations throughout the world; (2) provide a state-of-the-art Open Advancement (OA) framework for component- and application-based evaluation; (3) provide easy access to language tools and resources for members of the NLP community as well as researchers in a wide range of social science and humanities disciplines, and (4) enable easy navigation through licensing issues for data and tools.