ATLAS: A Flexible and Extensible Architecture for Linguistic Annotation

Published

May 1, 2000

Author(s)

S Bird, D Day, John S. Garofolo, J Henderson, Christophe Laprun, M Liberman

Abstract

We describe a formal model for annotating linguistic artifacts, from which we derive an application programming interface (API) to a suite of tools for manipulating these annotations. The abstract logical model provides for a range of storage formats and promotes the reuse of tools that interact through this API. We focus first on Annotation Graphs, a graph model for annotations on linear signals (such as text and speech) indexed by intervals, for which efficient database storage and querying techniques are applicable. We note how a wide range of existing annotated corpora can be mapped to this annotation graph model. This model is then generalized to encompass a wider variety of linguistic signals including both naturally occurring phenomena (as recorded images, video, multi-modal interactions, etc.) as well as the derived resources that are increasingly important to the engineering of natural language processing systems (such as word lists, dictionaries, aligned bilingual corpora, etc.). We conclude with a review of the current efforts towards implementing key pieces of this architecture.

Proceedings Title

International Conference on Language Resources and Evaluation | 2nd | | European Language Resources and Evaluation

Volume

3

Conference Dates

May 1, 2000

Conference Location

Athens, 1, GR

Conference Title

International Conference on Language Resources and Evaluation

Pub Type

Conferences

Download Paper

Local Download

Keywords

annotation, API, architecture, graphs, linguistic, query, reuse, signals

Citation

Bird, S. , Day, D. , Garofolo, J. , Henderson, J. , Laprun, C. and Liberman, M. (2000), ATLAS: A Flexible and Extensible Architecture for Linguistic Annotation, International Conference on Language Resources and Evaluation | 2nd | | European Language Resources and Evaluation, Athens, 1, GR, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=151499 (Accessed December 30, 2024)