A Reestimation Algorithm for Probabilistic Recursive Transition Network
نویسندگان
چکیده
(;enter for Artificial Intelligence (;omputer Science l)epartme'at Abstract Prob~bilistic l{,ecursive Tr~msition Network(Pl~TN) is an elevated version of t{51'N to model and process languages es in stoch~st, ic parameters. The representation is a direct derivation front the H,TN and keeps much the spirit of ltidden Markov Model at the same tint(,. We present a reestimation algorithm ['or Ptl,TN that is ~ variation of Inside-Ontside algorithm that comput, es the vMues of the probabilistic parameters from sample sentences (parsed or unparsed). In this pal)er , we introduce a network representation , Probabilistic Recursive Transitio. Network that is directly derived fl'Oln R'CN and ItMM, and present an estimation algorithm lot tile proba-bilistic paraHteters. PR;12N is a ][]TN mJgmented with probabilities in the transitions ~md states and with the lexical distributions in the transitions , or is the Hidden Markov Model augmented with a stack that makes some traltsitions deter ministic. The paramete.r esthnation of PI{;I'N is devel oped as a wu'iation of Inside()utside algorithm. The hlsidc ()utside algorithm has becn applied e(,10t, I;o ~, ,.~* recently by Jelinek (1{t9{/) and ],ari (1991). The algorithm was first introduced by Baker in 1.979 and is the context free lmtguage version o[ Forward-.Backw~rd algorithm in IIid-. Science altd Teclntology l"oundation) under tit{= title "A Study mt the Bnilding '[~echni(lues for [txdmst Km~wledge based Systems" from 19911 through 1994. den Markov Models. Its theoretical lbund~Ltion is laid by Baam aud Weh:h in the late 6l)'s, which in tarn is a type of the F,M Mgorithm in statistics (Rabiner, 1989). Kupiec (1991) introduced a trellis based estimation on Mgorithm of Hidden SCFG that ae commodates both ilnside-Outside ~dgorithm and l!brward-.Backward ",flgorithm. The meaning of our work can be sought from the use of more plain topology of I{TN, whereas Kupiec's work is a unilied version of tbrward-.backword and Inside Outside ~lgorithms. Nonetheless, the implemen. ration of reestimation Mgorittun carries no more theoretical significance than the applicative efli ciency and variation for differing representations since B~ker first apt)lied it to CI"Gs. A probabilistic ff.l.'N (PRTN, hereafter) denoted by A is ~ 4 tuple. A is ~ transition m~trix containing tr~n.sition probabilities, ~tnd 13 is aiL observation matrix containing probability distribution of the words ob servable at each terminM transition where row and column correspond to terminM transitions and a list of words respective, ly. F specilies the types of transitions, and D2 denotes a stack. The first two model …
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