Using Classification to Generate Text

USING CLASSIFICATION TO GENERATE TEXT Ehud Reiter* and Chris Mellish t Department of Artificial Intelligence University of Edinburgh 80 South Bridge Edinburgh EH1 1HN BRITAIN ABSTRACT The IDAS natural-language generation system uses a KL-ONE type classifier to perform content determination, surface realisation, and part of text planning. Generation-by-classification allows IDAS to use a single representation and reasoning com- ponent for both domain and linguistic knowledge, which is difficult for systems based on unification or systemic generation techniques. Introduction Classification is the name for the procedure of automatically inserting new classes into the cor- rect position in a KL-ONE type class taxonomy [Brachman and Schmolze, 1985]. When combined with an attribute inheritance system, classifica- tion provides a general pattern-matching and uni- fication capability that can be used to do much of the processing needed by NL generation sys- tems, including content-determination, surface- realisation, and portions of text planning. Classi- fication and inheritance are used in this manner by the IDAS natural language generation system [Re- iter et al., 1992], and their use has allowed IDAS to use a single knowledge representation system for both linguistic and domain knowledge. IDAS and I1 IDAS IDAS is a natural-language generation system that generates on-line documentation and help mes- sages for users of complex equipment. It supports user-tailoring and has a hypertext-like interface that allows users to pose follow-up questions. The input to IDAS is a point in question space, which specifies a basic question type (e.g., What-is-it), a component the question is being asked about (e.g., Computer23), the user's task (e.g. Replace-Part), the user's expertise-level *E-mail address is E. ReiterQed. ac .uk rE-mail address is C.NellishQed.ac.uk 265 (e.g., Skilled), and the discourse in-focus list. The generation process in IDAS uses the three stages described in [Grosz et al., 1986]: • Content Determination: A content-determin- ation rule is chosen based on the inputs; this rule specifies what information from the KB should be communicated to the user, and what overall format the response should use. • Text Planning: An expression in the ISI Sentence Planning Language (SPL) [Kasper, 1989] is formed from the information speci- fied in the content-determination rule. • Surface Realisation: The SPL is converted into a surface form, i.e., actual words interspersed with text-formatting commands. I1 I1 is the knowledge representation system used in IDAS to represent domain knowledge, grammar rules, lexicons, user tasks, user-expertise models, and content-determination rules. The I1 system includes: • an automatic classifier; • a default-inheritance system that inherits properties from superclass to subclass, us- ing Touretsky's [1986] minimal inferential dis- tance principle to resolve conflicts; • various support tools, such as a graphical browser and editor. An I1 knowledge base (KB) consists of classes, roles, and user-expertise models. User-expertise models are represented as KB overlays, in a simi- lar fashion to the FN system [Reiter, 1990]. Roles are either definitional or assertional; only defini- tional roles are used in the classification process. Roles can be defined as having one filler or an arbi- trary number of fillers, …

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