Graph unification is the most expensive part of unification-based grammar parsing. It often takes over 90% of the total parsing time of a sentence. We focus on two speed-up elements in the design of unification algorithms: 1) elimination of excessive copying by only copying successful unifications, 2) Finding unification failures as soon as possible. We have developed a scheme to attain these two elements without expensive overhead through temporarily modifying graphs during unification to eliminate copying during unification. We found that parsing relatively long sentences (requiring about 500 top-level unifications during a parse) using our algorithm is approximately twice as fast as parsing the same sentences using Wroblewski's algorithm.

Graph unifi(:ation remains the nlost expensive part of unificatiou-b~Lsed grammar l)arsing. We fl)cus on (Hie 81}ee(l-u 1) elelltellt ill the design of llllifieation algorithms: avoidance of copying of umao(lifled sul)graph.s. We propose a method of attaining snch a design through a nlethod of structnre-sharing which avoids log(d) overheads often associated with structure-sharillg of graphs without any use of costly dependency pointers. The proposed scheme eliminates redundant copying whih~ maintaining the qua.sidc,qtructive scheme's ability to avoid over copying and early copying eomlfined with its ability to handle cyclk: structures without algorithnfie additions. 1 Motivation Despite recent efforts in improving graph unification algorithms, graph unification renlains the most expensive part of parsing, both in time and space. ATR's latest data fi'om the SL-TRANS large-scale speech-to-speech translation project ([Morimoto, et al, 1990]) show 80 to 90 percent of tot~ parsing time is still consumed by graph unification where 75 to 95 percent of time is consumed by graph copying funeti(ms. 1 Qu~si-Destruetive (Q-D) Graph Unification ([Tontabeehi, 1991]) was deveh)ped as a fiLst variation of non-destructive graph unification based upon the notion of time-sensitive 'qu~mi-destruction' of node structures. The Q-D algorithm was proposed I)~Lsed upon the following m:cepted obserwttion about graph unification: Unification does not always succeed. Copying is an expensive operation. The design of tit(', Q-D scheme was motiwttcd by the following two princil)les h~r frost gral)h unification ba,sed upon the above observations: • Copying should be performed only for successful unifications. • Unification failures should be found as soon as possible. *This research wa.8 (lone while the author was ~ Visiting Research Scientist at ATR Interpreting Telephony [O~search Laboratories. lBased on unpublished reports from Knowledge itnd Data Processing Dept. ATR. The observed tendency was that sen-tellCCS with very long parsing tillle requiting a large Ii|lltll~t~r of unification calls (over 200l} top-level calls) coll811lllcd extremely htrge proportion (over 93 percent) of total paraing time ft~r graph unification. Similar data tep0rted in [Kogure. 19901.

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&bstract 't'h~s pa~r describes file use of the Direct Memory Ac~ ~:css (DIdA) pmadig~n hi a practical ltatu~tl lmlguage in-~:c~f~tec, Advaaltages and disadvantages of DMA in such ~pplications art~ discussed. 'ihe DMA natural language inteffa~x~ 'I)M-.COMMAND ~ described in this paper is be.. tug u.~;c ¢l tk~r development of a knowledge-based machine translation system at rite Center for Machine 'lYanslation ((;NIT) ~t Ciancgie Melloli University.

Graph unification has become a central processing mechanism of many natural language systems due to the popularity of unification-based theories of computational linguistics. Despite the popularity of graph unification as the central processing mechanism, it remains the most expensive part of unification-based natural language processing. Graph unification alone often takes over 90% of total parsing time. As the criteria for efficient unification, we focus on two elements in the design of an efficient unification algorithm: 1) elimination of excessive copying and 2) quick detection of unification failures. We propose a scheme to attain these criteria without expensive overhead for reversing the changes made to the graph node structures based on the notion of quasi-destructive unification. Our experiments using an actual large scale grammar and also using a simulated grammar producing different unification success rates show that the quasi-destructive graph unification algorithm runs roughly at twice as fast as Wroblewski's non-destructive unification algorithm.