UIMA: an architectural approach to unstructured information processing in the corporate research environment

Abstract

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.

Automatic Argumentation Extraction

“…This corpus will be annotated with tags suitable for analysis by many of the available Apache UIMA 1 Tools and components [15]. Annotation is underway and a need for clear definitions has already become evident.…”

Predicate Argument Structure Analysis for Use Case Description Modeling

“…By using the UIMA framework [12] with which linguistic analysis engines can be used as plug-ins, we can easily switch the analysis engines when the language of the data is changed. We can therefore get the predicate argument structures in several major languages by using offthe-shelf lexical processors.…”

CITOM: Incremental Construction of Topic Maps

“…Finally, we note that many text mining frameworks have been developed for linguistic processing of textual documents [35], [36] such as GATE (General Architecture for text Engineering). The latter could be used for semantic annotation and information extraction from text corpora.…”