A97-1050 correspondence using a geometric pattern recognition algorithm . The recognized patterns may
C96-2145 with an approximate finite state recognition algorithm close to a query tree . Following
C96-2122 Conclusion The paper has described a recognition algorithm for dependency grammar . The
C92-4199 system for comparison of the name recognition algorithms . The following results are for
C92-1058 recognizer , rather than a parser . A recognition algorithm collects a set of items that
A83-1022 columns it descri - bes . A name recognition algorithm ( described in Hafner ( 3 ) )
A00-2036 precompilation In this section we consider recognition algorithms that do not require off-line
C92-4199 single-character words . Thus , the name recognition algorithm sometimes overgenerates a personal
C92-1058 . It is usually described as a recognition algorithm . An item \ -LSB- i , A -- *
A00-2036 in contrast with bidirectional recognition algorithms . The result presented below
C96-2122 primary parser operations . Then the recognition algorithm consults the parse tables to
A94-1021 Reflecting these assumptions , the plan recognition algorithm works as follows : 1 . When an
C92-3167 according to clue words . This topic recognition algorithm can be used for any language
C67-1004 essentially a linguistic pattern recognition algorithm which , instead of matching portions
A00-2036 precompilation In this section we consider recognition algorithms that satisfy the CPP and allow
C82-1054 1965 ) ( henceforth G+P ) is a recognition algorithm , not a parsing algorithm . Thus
C00-1049 be used to drive a text-block recognition algorithm . Detecting the Potential for
C67-1023 Some Reco ~ nltion Rules . Our recognition algorithm was intended a ~ a frame of reference
C73-1017 extent determine the kind of the recognition algorithm . It seems obvious that a linguistic
D11-1089 compound , and most abbreviation recognition algorithms assume that the definition is
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