This paper describes the Linguistic Annotation Framework under development within ISO TC37 SC4 WG1. The Linguistic Annotation Framework is intended to serve as a basis for harmonizing existing language resources as well as developing new ones.

In this paper we present recent work on GATE, a widely-used framework and graphical development environment for creating and deploying Language Engineering components and resources in a robust fashion. The GATE architecture has facilitated the development of a number of successful applications for various language processing tasks (such as Information Extraction, dialogue and summarisation), the building and annotation of corpora and the quantitative evaluations of LE applications. The focus of this paper is on recent developments in response to new challenges in Language Engineering: Semantic Web, integration with Information Retrieval and data mining, and the need for machine learning support.

Every building, and every computer program, has an architecture: structural and organisational principles that underpin its design and construction. The garden shed once built by one of the authors had an ad hoc architecture, extracted (somewhat painfully) from the imagination during a slow and non-deterministic process that, luckily, resulted in a structure which keeps the rain on the outside and the mower on the inside (at least for the time being). As well as being ad hoc (i.e. not informed by analysis of similar practice or relevant science or engineering) this architecture is implicit: no explicit design was made, and no records or documentation kept of the construction process.

We present the RAGS (Reference Architecture for Generation Systems) framework, a specification of an abstract Natural Language Generation (NLG) system architecture to support sharing, re-use, comparison and evaluation of NLG technologies. We argue that the evidence from a survey of actual NLG systems calls for a different emphasis in a reference proposal from that seen in similar initiatives in information extraction and multimedia interfaces. We introduce the framework itself, in particular the two-level data model that allows us to support the complex data requirements of NLG systems in a flexible and coherent fashion, and describe our efforts to validate the framework through a range of implementations.

IBM Research has over 200 people working on Unstructured Information Management (UIM) technologies with a strong focus on Natural Language Processing (NLP). These researchers are engaged in activities ranging from natural language dialog, information retrieval, topic-tracking, named-entity detection, document classification and machine translation to bioinformatics and open-domain question answering. An analysis of these activities strongly suggested that improving the organization's ability to quickly discover each other's results and rapidly combine different technologies and approaches would accelerate scientific advance. Furthermore, the ability to reuse and combine results through a common architecture and a robust software framework would accelerate the transfer of research results in NLP into IBM's product platforms. Market analyses indicating a growing need to process unstructured information, specifically multilingual, natural language text, coupled with IBM Research's investment in NLP, led to the development of middleware architecture for processing unstructured information dubbed UIMA. At the heart of UIMA are powerful search capabilities and a data-driven framework for the development, composition and distributed deployment of analysis engines. In this paper we give a general introduction to UIMA focusing on the design points of its analysis engine architecture and we discuss how UIMA is helping to accelerate research and technology transfer.

The KIM platform provides a novel Knowledge and Information Management framework and services for automatic semantic annotation, indexing, and retrieval of documents. It provides a mature and semantically enabled infrastructure for scalable and customizable information extraction (IE) as well as annotation and document management, based on GATE.General Architecture for Text Engineering (GATE) (http://gate.ac.uk), leading NLP and IE platform developed at the University of Sheffield. Our understanding is that a system for semantic annotation should be based upon a simple model of real-world entity concepts, complemented with quasi-exhaustive instance knowledge. To ensure efficiency, easy sharing, and reusability of the metadata we introduce an upper-level ontology. Based on the ontology, a large-scale instance base of entity descriptions is maintained. The knowledge resources involved are handled by use of state-of-the-art Semantic Web technology and standards, including RDF(S) repositories, ontology middleware and reasoning. From a technical point of view, the platform allows KIM-based applications to use it for automatic semantic annotation, for content retrieval based on semantic queries, and for semantic repository access. As a framework, KIM also allows various IE modules, semantic repositories and information retrieval engines to be plugged into it. This paper presents the KIM platform, with an emphasis on its architecture, interfaces, front-ends, and other technical issues.

Generic software architectures aim to support re-use of components, focusing of research and development effort, and evaluation and comparison of approaches. In the field of natural language processing, generic frameworks for understanding have been successfully deployed to meet all of these aims, but nothing comparable yet exists for generation. The nature of the task itself, and the current methodologies available to research it, seem to make it more difficult to reach the necessary level of consensus to support generic proposals. Recent work has made progress towards establishing a generic framework for generation at the functional level, but left open the issue of actual implementation. In this paper, we discuss the requirements for such an implementation layer for generation systems, drawing on two initial attempts to implement it. We argue that it is possible and useful to distinguish “functional architecture” from “implementation architecture” for generation systems.

The development of large-scale dialog systems requires a flexible architecture model and adequate software support to cope with the challenge of system integration. This contributionOur current work in the context of the SmartKom project has been funded by the German Federal Ministry for Education and Research (BMBF) under grant 01 IL 905 K7. presents a general framework for building integrated natural-language and multimodal dialog systems. Our approach relies on a distributed component model that enables flexible re-use and extension of existing software modules and is able to deal with a heterogeneous software environment. A practical result of our research is the development of a sophisticated integration platform, called MULTIPLATFORM, which is based on the proposed framework. This MULTIPLATFORM testbed has been used in various large and mid-size research projects to develop integrated system prototypes.

We present the architecture and data model for TEXTRACT, a robust, scalable and configurable document analysis framework. TEXTRACT has been engineered as a pipeline architecture, allowing for rapid prototyping and application development by freely mixing reusable, existing, language analysis plugins and custom, new, plugins with customizable functionality. We discuss design issues which arise from requirements of industrial strength efficiency and scalability, and which are further constrained by plugin interactions, both among themselves, and with a common data model comprising an annotation store, document vocabulary and a lexical cache. We exemplify some of these by focusing on a meta-plugin: an interpreter for annotation-based finite state transduction, through which many linguistic filters can be implemented as stand-alone plugins. The framework and component plugins have been extensively deployed in both research and industrial environments, for a broad range of text analysis and mining tasks.