Pattern matching, or querying, over annotations is a general purpose paradigm for inspecting, navigating, mining, and transforming annotation repositories - the common representation basis for modern pipelined text-processing frameworks. Configurability of such frameworks and expressiveness of feature structure-based annotation schemes account for the “high density” of some such annotation repositories. This particular characteristic makes challenging the design of a pattern matching engine, capable of interpreting (or imposing) flat patterns over an arbitrarily dense annotation lattice. We present an approach where a finite state device carries out the application of (compiled) grammars over what is, in effect, a linearized “projection” of a unique route through the lattice; a route derived by a mix of static pattern (grammar) analysis and interpretation of navigational directives within the extended grammar formalism. Our approach achieves a mix of finite state scanning and lattice traversal for expressive and efficient pattern matching in dense annotations stores.

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, do 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 design, 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 from corpus analysis and concept definition, to model development and testing, to large scale evaluation, to easy and rapid composition of text applications deploying these concept models. With our design, we aim to meet the needs of domain experts, who are not necessarily expert NLP practitioners.

In our work, we present an analysis of the TimeBank corpus---the only available reference sample of TimeML-compliant annotation---from the point of view of its utility as a training resource for developing automated TimeML annotators. We are encouraged by experimental results indicative of the potential of TimeBank; at the same time, closer inspection of causes for some systematic errors shows off certain deficiencies in the corpus, primarily to do with small size and inconsistent annotation. Our analysis suggests that even a reference resource, developed outside of a rigorous process of training corpus design and creation, can be extremely valuable for training and development purposes. The analysis also highlights areas of correction and improvement for evolving the current reference corpus into a community infrastructure resource.