Towards a Workbench for Acquisition of Domain Knowledge from Natural Language

Towards a Workbench for Acquisition of Domain Knowledge from Natural Language Andrei Mikheev and Steven Finch HCRC Language Technology Group University of Edinburgh 2 Buccleuch Place, Edinburgh EH8 9LW, Scotland, UK E-maih Andrei.MikheevQed.ac.uk Abstract In this paper we describe an architecture and functionality of main components of a workbench for an acquisition of do- main knowledge from large text corpora. The workbench supports an incremental process of corpus analysis starting from a rough automatic extraction and or- ganization of lexico-semantic regularities and ending with a computer supported analysis of extracted data and a semi- automatic refinement of obtained hypo- theses. For doing this the workbench em- ploys methods from computational lin- guistics, information retrieval and kno- wledge engineering. Although the work- bench is currently under implementation some of its components are already im- plemented and their performance is il- lustrated with samples from engineering for a medical domain. 1 Introduction One of the standard methods for the extraction of domain knowledge (or domain schema in ano- ther terminology) from texts is known as Distribu- tional Analysis (Hirshman 1986). It is based on the identification of the sublanguage specific co- occurrence properties of words in the syntactic re- lations in which they occur in the texts. These co- occurrence properties indicate important seman- tic characteristics of the domain: classes of ob- jects and their hierarchical inclusion, properties of these classes, relations among them, lexico- semantic patterns for referring to certain concep- tual propositions, etc. This knowledge about do- main in the form it is extracted is not quite sui- table to be included into the knowledge base and require a post-processing of the linguistically trai- ned knowledge engineer. This is known as a con- ceptual analysis of the acquired lingistic data. In general all this is a time consuming process and often requires the help of a domain expert. Ho- wever, it seems to be possible to automate some tasks and facilitate human intervention in many parts using a combination of NLP and statistical techniques for data extraction, type oriented pat- terns for conceptual characterization of this data and an intuitive user interface. All these resources are to be put together into a Knowledge Acquisition Workbench (KAWB) which is under development at LTG of the Uni- versity of Edinburgh. The workbench supports an incremental process of corpus analysis star- ting from a rough automatic extraction and orga- nization of lexico-semantic regularities and ending with a computer supported analysis of extracted data and a refinement of obtained hypotheses. 2 KAW Architecture The workbench we are aiming at integrates com- putational tools and a user interface to support phases of data extraction, data analysis and hypo- theses refinement. The target domain description consists of words grouped into domain-specific se- mantic categories which can be further refined into a conceptual type lattice (CTL) and lexico- semantic patterns further refined into conceptual structures as shown elsewhere in the paper. KAW architecture is displayed in figure 1. A data extraction module provides the kno- wledge engineer …

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