Unsupervised Learning of a Rule-based Spanish Part of Speech Tagger

Unsupervised Learning of a Rule-based Spanish Part of Speech Tagger Chinatsu Aone a, nd Kevin Hausman Sysl, clns l{.cs(;eu'<:t~ al~(l Al)l)li<:{d;ions (~orpora.1;ioll (SIIA) 4300 Vair l,akcs (]ottt% l<'a, it'fax, VA 22033 }Lo I1 cc((i]Sl'{i,. (:o i i i ~ h ~1,1 is i / 1311 ((1~ s l-iL co l i l Abstract This I)at)cr d(~scrihcs a, SI);ulish l'arl.-ol: Speech (I'()S) l~a.gger which al,i)lies aad extends Ih'ill's a, lguril;hn~ for 'u,.super- vised learuing of ruh',- based l, aggcrs (llrill, 1995). First, we discuss our general ;ttt- proach including extensions we rim(It 1;~) the algorithm in order t,o hamllc ml kllOWl~ w(H'(IH a,l~d pa.ra, ll~(d,crize [ea.l'llillg; and ta.gging ot)l;ions. Ncx L we rCl)orl, and amdyzc our eXl)cri~mm,al rcsull;s us- iug dill'crenl, l)ara~ilel:crs. Thcll, wc (h~ s('ril)e our "hyhri(l" a.t)l)roach which was ll(~C{~Hs;-tl'y ill Ol'(l(w 1,o oV(~l'(:(Hl~l(~ ;1 ['un(la mcni, al li~lit;al, ion in Ih'ill's origiual al- goril, hnL Finally, wc cOral)at(> our tag- ger wil;h Ilid(Icn Mark()v Model (IlMM)- based (,aggers. :l Introduction We have develol)ed a. Spanish l'arb-(,l:-HI)('(~ch (I)()S) 'l'a.g:g(~r which al)l)lies and extends Ih'ill's alg(,rilJ~u for unSUl)crvised l('a.rniug (llrill, l.q!),5) to cr(~a.l;e a. set of rules (;hal, r(~(luce the aml)iguil.y of I'()S tags on words. We have ch()scu an un- supervised Ica','ning algori/,hn~ l)(~ca.u,s(~ il, does not require a. larg;(' I)()S-l;agged t,raining (-orl)us. Since there was n() I)()S-t.agged Spanish c(,rt)us avail- abh' 1;() us and since creating a large hand-l,;tgp;(xl corltus is both cosl, ly aud I)r()ne l,o inconsislamcy, Gc decision was also a l)ra, ci, ical one. Wc have de- cided 1;o develop a rule-based I[,;xggcr l}(!causc such a. tagger lea.rus a sel, of declarative rules m~d also because we wautt'd 1,o c(tml)are it, with Ili(Id(:n M arkov M odd (I 1M M)-/)ascd 1;aggers. We extcude<l Ih'ill's algol'itlnu in scwwal ways. l"irsl,, we cxtcnd(;d it, (,o Imn(Ih~ unknowu words in the training and test texl,s. Scc(m(l, w(., i)aram(~ terized learniug and t;ag,ghw; ol)tions. Finally, wc cxp(;rinmnl, ed wil;h a "hyhrid" solul, io,, where we tls(~d a. v(;ry sinful[ Iltlllll)cr o[' hm~(I-(lis:nnhigual,(~d texi;s during training to overcom(~ a tiu~(lan~(ml, a.l limitation in tit(: learning algorithm. 2 Conipollenl;s ()ur Spa,ish l'()N l,agg(w consisLs of l, hre(~ (:()lHl)(~ nenl;s: the lnil, ial St, al,e Anuol, a l.or, the l,carncr, ;Ul(l l, he l{.uh" 'l';tggcr, c;tch o[' which is d(~scrih~'(l helow. 2.1 Initial State Annotator This conq)onent is used t()assign all I>ossil)le I>()S l.ag~ 1.o a. given Spanish word. It consisl;s of h~xicon h>()kul) , nl(>rphol()gical analysis, a.nd ult. k.(.wn word ha, ndlin~,,. 'l'he Spanish I'()S l,ag s.~l, us(~d in 1,his w()rk c(msisl,s ()f l,hc l'olh,wi.v; l;~gs: AI)J. AI)V, BI+, (form of scr ()r ~s/ar), (II,()(~1( 'I'IME, (;()I,()N, (;()MMA, (;ON.l, I)ATE, I)1,'/1', HAVE (form uf ha&', 9 , IIYI'IIEN, LET'I'I,~I{,, I,I'AREN, M()I)EL, MUI,TII'IAEH,, N, NUMBI{,, 1 ), I)EI{,I()I), I)I{,EI~'JX, PI{,(), J)I{,()PN, QIJEH-. MAll, I(, QIJ()TE, IlL)MAN, I{.I'AI{EN, SEMI- (;()l,()N, SI,ASll, SIJIRX)N.I, S//FVIX, TIIEILE (hater used iu "l,h(~re" collsl, ructi()ns), WII[)ET (,:,.*l/il,,',,), WIJN J' (q,t,r), Wt[l'l' ( da,~d~O, ;.,d V (S(> 'l'ahl(' ;/). 2.].1 L(;xic(m Lookup and Morl)h()logi(-al Analysis Iinlil{e Ib'ill's English l;aggcr …

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