Feature Structure Based Semantic Head Driven Generation

This paper proposes a generation method for feature-structured)ased unification grammars. As comlx~red with fixed ~rity term notation, feature structure notation is more tlexible for representing knowledge needed to generate idiom~ttic structures as well as genem~l constructions. The method enables feature strncture retrieval via nmltiple indices. The indexing mechanism, when used with a semantic head driven generation algorithm , attains efficient generation even when a large amount of generation knowledge must be considered. Our method can produce all possi ble structures in parNlet, using structure sharing among ambiguous substructures. 1 Introduction PracticM generation systems must lnwe linguistic knowledge of both specilic expressions like idioms and generM grammatical constructions, ;rod ttmy should efgtciently produce sm'face strings applying that knowledge [[][2]. In order to satisfy the first requirement, our system employs a set of trees annotated with fe,~ture structures to represent generation knowledge. l:;ach tree represents a t?agment of a syntactic strncture, and is paired with a semantic feature structure. We can describe idiomatic eonstructions, by making a tree which cont~tins lexical specifications and is paired with a specilie rather than general semautic structure. Because feature structures allow partial speeiiicatiom we can encode generation knowledge r;mgiug over multiple levels of generality in a. uniform way. llowever, notice that this property will be restricted if we use DCG or (tixed arity) term notation 1 Suppose there is a generation knowledge structure whose syntactic part is "go on foot". 'rim feat, tu'e structure notation of its semantic part will be sonmthing like: ~The flexibility of structure notation colnpated Lo tetln notation is also discussed il~ [4].