Feature Structure Based Semantic Head Driven Generation

Feature Structure Based Semantic Head Driven Generation Gendchiro KIKUI ATR Interpreting ]~elephony Research Laboratories 2 2 IIikari dai, Seika-cho, Soraku-gun, Kyoto (;19 02 JAPAN kikui@atr- la.atr.co.jp Abstract This paper proposes a generation method for feature-structured)ased unification grammars. As comlx~red with fixed ~rity term notation, fea- ture structure notation is more tlexible for repre- senting 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 al- gorithm, 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 1 among ambiguous substructures. 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 apply- ing 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 knowl- edge. l:;ach tree represents a t?agment of a syn- tactic 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 re- stricted 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]. [ [Rein GO] [Agent ?agent [] ] [Instrument FOOT]]. while the term notation is : (1) instrument(go(Agent), foot) (2) These two notations seem to be equivalent, but there is a cruciN diflerence. A generation knowl- edge structure containing the fe~tture-based se- lnantics will still be unifiable even if the seman- tic input to be unified contains additional ma- terial. Thus the knowledge structure will be discovered and its syntactic information can he used for generation. By contrast, a term-based input with additiona.1 elements would not unify with the term-based semantic structure shown above. It would thus be necessary to create ad- ditional generation structures containing distinct (though partly overlN)ping) term-based seman- tic structures. Such additional structures are re- dundant ~tn(l cause superfluous output. For example, consider the a,ugmented feature structure (3). [ [Rein ~o] [Agent Ken] [Instrument FOOT] [Time I0 : OOmn] ] (3) it will indeed nnify with (1) above. But term- based input semantic structure (4) will not unify with term-based semantic structure (2). instrument(time(go(ken), 10:00am), foot). (4) To unifv (2), semantic ,structure (5) would a.lso be required. time(instzument(go(ken), foot), 10:00ma). (5) AcrEs DE COLING-92. NANTES. 23 28 AOt~q" 1992 3 2 PROC. OI; COLING 92. NANTES. AUG. 23 28. 1992 For this reason, …

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