Probabilistic Tagging With Feature Structures

PROBABILISTIC TAGGING WITH FEATURI~ STR,UCTUR,I;3S n trc I(empe University of Stuttgart, Institute for (?omputational Linguistics, Azenbergstrage 12, 70174 Stuttgart, (lerniany, kellipe~_)ims.uni-stuttgart.de Abstract The described tagger is b,'used on a hidden Markov model and uses tags composed of features such as part- of speech, gender, etc. 'l?he contextual probability of a tag (state transition probability) is deduced from the contextual probabilities of its feature--value-pairs. This approach is advantageous when the available training corpus is small and the tag set large, which can be the case with morphologically rich languages. 1 INTRODUCTION 'l'he present article describes it probabillstic tagger based on a hidden Marl(or model (IIMM) (Rabiner, 1990) and employs tags which are fe,'iture structures. Their features concern part-of-speech (POS), gel,der, number, etc. and tlave only atouiie vahles. Usually, the contextual probability of a tag (state transition probability) is estimated dividing a trigrain frequency by a bigram frequency (second order II MM). With a large tag set resulting froin tire fact that the tags colitain besides or the POS a lot of lnorphologi- cal information, and with only a slnall training corpus available, most of these frequencies are too low for an exact estimation of contextual probabilities. Our feature structure tagger esthnates these prob- abilities by connecting contextual probabilities of the single fealvre-wdue-pai,'s (rv-pairs) of the tags (cf. sec. 2). Starting point for the iulph;nientation of the ['ea- ture structure tagger was a second-order-li'IvlM tagger (trigrams) b~med on a modilied version of the Viterbi algorithm (Viterbi, 1967; Chllrch, 1988) which we had earlier implemented in C (l(empe ,1994). 'Flier{: we modified tim calculus of the contextual probabilities of the tags in the above-described way (cf see. 4). A test of both tatters under the sanle conditions Oli a French corpus 1 has shown that tile feature structure tagger is clearly better when tim available training col pus is small and the tag set is large but the tags are decomlmsable into relatively few fv-pairs. 'l'he hitter can be the case with morphologically rich languages when the tags contain a lot of morphological infornia- tion (cf. see. 5). 11 inll nmch obliged to Achim Stein and Leo W,tuner, lto- nl~.UC~: l)ept., Univ. Stuttglirt, Gel'lll&liy, for t~rovidlng the cor- ptlS and it dictionary. 2 MATHEMATICAL BACK- GR.OUND In order to ~Lssign tags to a word sequence, a IIMM can be used where tim tagger selects among all possible tag sequences tile most probable one (Garside, Leech and Saulpson, 1987; (Tlnlrch, 1988; Brown e.t al., 1989; Rabiner, 1990). The joint probability of a tag sequence l -- I0...tN_ 1 given a word sequence lg., : ~v0...lON_-l is hi the case of a second order IIMM: *'(l, ,Z,) := ~t,, ,, • p(,v0lZ0). J,(ivl lZ,) ' N-1 l-{ (p(,.,I',),(l,I (1) i=2 The term rqo t, stands for tim initial slate probabil- ity, i.e. the probability that the sequence begins with the first two tags. N is tim nunlber of words in the sequence, i.e. the corpus size. "Phe term p(w¢]ll) is the probability of a word …

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