A Development Environment for Configurable Meta-Annotators in a Pipelined NLP Architecture

Information extraction from large data repositories is critical to Information Management solutions. In addition to prerequisite corpus analysis, to determine domain-specific characteristics of text resources, developing, refining and evaluating analytics entails a complex and lengthy process, typically requiring more than just domain expertise. Modern architectures for text processing, while facilitating reuse and (re-)composition of analytical pipelines, place additional constraints upon the analytics development, as domain experts need not only configure individual annotator components, but situate these within a fully functional annotator pipeline. We present the and current status, of a tool for configuring model-driven annotators, which abstracts away from annotator implementation details, pipeline composition constraints, and data management. Instead, the tool embodies support for all stages of ontology-centric model development cycle

• Type: Conference
• Year: 2008
• Where: LREC
• Authors: Youssef Drissi, Branimir Boguraev, David Ferrucci, Paul Keyser, Anthony Levas
• Keywords: Corpus Analysis, Domain Experts, Education, Functional Annotator Pipeline, LREC 2008