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<?xml version="1.0" standalone="yes"?> <Paper uid="C00-1023"> <Title>Word Sense Disambiguation of Adjectives Using Probabilistic Networks</Title> <Section position="3" start_page="0" end_page="152" type="metho"> <SectionTitle> 2 Problem Formulation </SectionTitle> <Paragraph position="0"> WSD can t)e described as a classification task, where the i th sense (W#i) of a word (W) is classified as the correct tag, given a word and usually some surrounding context. For example, to disambiguate the adjective &quot;great&quot; in the sentence &quot;Tile great hm'riealm devastated the region&quot;, a \VSI) system should disambiguate &quot;great&quot; as left.q(: in .s'izc rather than the good or c:cccllent meaning. Using l)robability notations, this t)rocedure can be st~ted as maxi(Pr(great#i \[ &quot;great&quot;, &quot;ihe&quot;, &quot;lnn'ricane&quot;, &quot;devastated&quot;, &quot;the&quot;, &quot;region&quot;)). That is, given the word &quot;great&quot; and its context, elassit~y the sense great#/ with the highest probability as the correct o11e. However, a large COllteX\[;~ sll(:h as the whole sentence, is rarely used, due to the dilliculty in estimating the 1)robability of this particular set of words oceurring. Therefore, the context is usually narrowed, such as ,z number of surromMing words.</Paragraph> <Paragraph position="1"> Additionally, surrollnding syntactic tbatures aim semantic knowledge are sometimes used. The difficulty is in choosing the right context, or the set of features, that will ol)timize the classification. A larger (:ontext intl)roves t;11(! classiti(:ation a(:(:ura(:y al the exl)ense of increasing the mlml)er of l)arameters (typically learned fr(nn large training data).</Paragraph> <Paragraph position="2"> in our BHI) model, a minimal context (:Oml)OSe(l of only the adje(:tive, noun and the llOllll;S Selllalltie features obtained fl'()m \VordNel is used. Using the al)ove examl)le , only &quot;great&quot;, &quot;hurri(:ane&quot; and hun'icane's t)atures encoded in WordNet's hierarchy, as in hurricane lISA cyclone lISA windstorm ISA violent storm..., are used its (;Olltext.</Paragraph> <Paragraph position="3"> Therefore, the classitieation 1)ertbrnmd 1)y Bill) (:an t)e written as nlaxi(lh(greatC/~i \[ &quot;great&quot;, &quot;\]nlrricane&quot;, cyt:loltc, windslorm ...)), or more generically, ,m(,,:~( l'r(,dj#il(,dj , ,,>,,.,,, < N \],',; >)), ,vher(, <N/Zs> denotes the n(mn features. By using the Bayesian inversion fornmla, this equati()n l)e(:onw.s</Paragraph> <Paragraph position="5"> This eontext is chosen because it does not; need an annotated training set, and these semantic fe.atures are used to l)uiht a belief about the nouns ,:ill adjective SellSe typically lnodifies, i.e., the se\]ectional preferences of a(ljectiv('.s. For examlfle , having learned about lmrrieane, the system can infer the most probable dismnbiguation of &quot;great typhoon&quot;, &quot;great tornado&quot;, or ltlore distal concepts such as earthquakes and tloods.</Paragraph> </Section> <Section position="4" start_page="152" end_page="153" type="metho"> <SectionTitle> 3 Establishing the Parameters </SectionTitle> <Paragraph position="0"> As shown in equation 1, BH1) requires two parameters: 1) the likelihood term Pr(adj, noun,< NFs > ladj#i) and 2) the prior term Pr(adj#i). The prior term represents the knowledge of how frequently a sense of an adjective is used without any contextual infornmtion. Dn&quot; example, if great#2 (sense: 9ood, excellent) is nsed frequently while great#l is less commonly used, then Pr(great#2) wtmld be larger than Pr(great#l), in l)rOl)ortion to the usage of the two senses. Although WordNet orders the senses of a t)olysenl(ms word according to usage, the actual t)rot)ortions are not quantified. Therefore, to (:omtmte the priors, one can iterate over all English nouns and sunl the instmlces of great#l-noun versus great#-2-noun t)airs. But siilce we assume that no training set exists (the worst possible ease of the sparse data l)rol)lem), these counts need to 1)e estinmted from ilMireet sources.</Paragraph> <Section position="1" start_page="152" end_page="153" type="sub_section"> <SectionTitle> 3.1 The Sparse Data Problem </SectionTitle> <Paragraph position="0"> The techuique used to address data sI)arsity, as first proposed by Mihalcea and Moldovan (1998), treal;s the Internet as a cortms to automatically (lisambiguate word 1)airs. Using the previous examl)le , to disambiguate the adjective in &quot;great hurricane&quot;, two synonym lists of (&quot;great, large, t)ig&quot;) and (&quot;great, neat, good&quot;) are retrieved from VC/ordNet. (SOllle synonyms ~111(1 el;her SellSeS i/re olllitted here for 1)revity.) Two queries, (&quot;great hurricane&quot; or &quot;large hurrit:ane?' or &quot;t)ig hurricane&quot;) and (&quot;great hurricane&quot; or &quot;neat hurricane&quot; or &quot;good hurriemm&quot;), are issued to Altavista, which reporl, s that 1100 and 914: 1)ages contain these terlllS, respectively. The query with the higher count (#1) is classitied its the correct sense. For fllrther details, please refer to Mihaleca anti Moldovau (1998).</Paragraph> <Paragraph position="1"> In our luo(lel, the (:omd;s front Altavista are im:orl)orated as \[)aralltel;er estilllatiollS within our probabilislh: frlt111(~work. Jill addition to disaml)igualing lhe adjectives, we also need to estimale lhe usage of the a(ljec|;ive#i-noml pair. For simt)licil;y , the counts fronl Altavista are assigued wholesale 1o the disambiguated adjective sense, e.g., the usage of great#l-hurricane is 1100 times and great#2hurricane is zero times. This is a great simplification since in many adjective-noun pairs nmltil)le meanings are likely. For instance, in &quot;great sl;eak&quot;, both sense of '&quot;great&quot; (large steak vs. tasty steak) are e, qually likely. However, given no other information, this sinlt)lification is used as a gross apt)roximation of Counts(adj#i-noun), which becoines Pr(adj#i-noun) by dividing the counts by a nornmlizing constant, ~ Com~ts(adj#i-all nouns). These prolmbilities are then used to compute the priors, described in the next section.</Paragraph> <Paragraph position="2"> Using this technique, two major probleins are addressed. Not only are the adjectives automatically disambiguated, but the number of occurrenees of the word pairs is also estimated. The need lot haudannotated semantic corpora is thus avoided. However, the statistics gathered by this technique are at)proxinmtions, so the noise they introduce does require supervised training to nfinimize error, as will lm described.</Paragraph> </Section> <Section position="2" start_page="153" end_page="153" type="sub_section"> <SectionTitle> 3.2 Computing the Priors </SectionTitle> <Paragraph position="0"> Using the methods described above, the priors cats be automatically computed by iterating over all nouns and smnnfing tile counts tbr each adjective sense. Untbrtunately, the automatic disambiguation of tim adjective is not reliable enough and results it, inaccurate priors. Therefore, manual classification of assigning nouns into one of the adjective senses is needed, constituting the first of two manual tasks needed by this model. However, instead of classifying all English nouns, Altavista is again used to provide collocation data on 5,000 nouns for each adjective. The collocation frequency is then sorted and the top 100 nouns are manually classified. For example, the top 10 nouns that collocate after &quot;great&quot; are &quot;deal&quot;, &quot;site&quot;, &quot;job&quot;, &quot;place&quot;, &quot;time&quot;, &quot;way&quot;, &quot;American&quot;, &quot;page&quot;, &quot;book&quot;, and &quot;work&quot;. They are then all classified as being modified by the great#2 sense except for tile last one, which is classified into another sense, as defined by WordNet. Tim prior for each sense is then coinputed by smmning the counts Don, t)airing the adjective with the nouns classified into that sense and dividing by the sum of all adjective-noun pairs. The top 100 collocated nouns fbr each adjective are used as an al)proximation for all adjective-noun pairs since considering all nouns would be impractical.</Paragraph> <Paragraph position="1"> To validate these priors, a Naive Bayes classifier that comt)utes</Paragraph> <Paragraph position="3"> is used, with the noun as the only context. This simpler likelihood term is approxinmted by the same Internet counts used to establish the priors, i.e., Counts(adj#i-noun) / normalizing constant. In Table 1, the accuracy of disambiguating 135 adjective-noun pairs Dora the br-a01 file of the semantically tagged corlms SemCor (Miller et al., 1993) is compared to the baseline, wlfich was calculated by using the first WordNet sense of the adjective. As mentioned earlier, disambiguating using, simply the highest count Dot** Altavista (&quot;Before Prior&quot; it, Table 1) achieved a low accuracy of 56%, whereas using the sense with the highest prior (&quot;Prior Only&quot;) is slightly better than the baseline. This result validates the fact that the priors established here preserve WordNet's ordering of sense usage, with tile improvement that tim relative usages between senses are now quantified.</Paragraph> <Paragraph position="4"> Combining both the prior and the likelihood terms did not significantly improve or degrade tile accuracy. This would indicate that either the likelihood term is uniformly distributed across tile i senses, which is contradicted by the accuracy without the priors (second row) being significantly higher than the average number of senses per adjective of 3.98, classifier to wflidate the priors. These results show that the priors established in this model are as accurate as the WordNet's ordering according to sense usage (Baseline).</Paragraph> <Paragraph position="5"> or, more likely that this parameter is subsumed by the priors due to the limited context. Therefore, more contextual infbrmation is needed to improve tim model's peribrmance.</Paragraph> </Section> <Section position="3" start_page="153" end_page="153" type="sub_section"> <SectionTitle> 3.3 Contextual Features </SectionTitle> <Paragraph position="0"> Instead of adding other types of context such as the surrounding words and syntactic features, tlm semantic features of tim noun (as encoded in the WordNet ISA hierarchy) is investigated for its effectiveness. These features are readily available and are organized into a well-defined structus'e. Tim hierarchy provides a systematic and intuitive method of distance measurements between feature vectors, i.e., the semantic distance between concepts. This prop-erty is very important for inthrring the classification of the novel pair &quot;great flood&quot; into the sense tha.t contains hurricane as a member of its prototypical nouns. These prototypical nouns describe tile selectional preferences of adjective senses of &quot;great&quot;, and the semantic distance between them and a new noun ineasures 1;11o &quot;semantic fit&quot; between the concepts. Tile closer the.y are, as with &quot;hurricane&quot; and &quot;flood&quot;, the higher the prot)ability of the likelihood term, whereas distal concepts such as &quot;hurricane&quot; and &quot;taste&quot; would have a lower value.</Paragraph> <Paragraph position="1"> Representing these prototyt)ical 11011218 prot)abilistically, however, is difficult due to the exponential number of 1)robal)ilit, ies with respect to the nmnber of features. For exmnl)le, ret)resenting hurricmm 1)eing I)resent in a selectional t)reference list requires 2 s probabilities since there are 8 features, or ISA l)arents, in the WordNet hierarchy. It, addition , tile sparse data t)roblem resurfaces because each one of the 2 s probabilities has to l)e quantified. To address these two issues, belief networks are used, as described it, detail in the next section.</Paragraph> </Section> </Section> <Section position="5" start_page="153" end_page="156" type="metho"> <SectionTitle> 4 Probabilistic Network's </SectionTitle> <Paragraph position="0"> There are many advantages to using Bayesian networks over the traditional probabilistic models. The most notable is that the mlmber of probabilities needed to represent the distribution can be signif icantly reduced by making independence assumptions between variables, with each node condition-</Paragraph> <Paragraph position="2"> shills 1)('tweeu a no(h; and its parents. '\]'he equation al; the l)ott()m shows how t.h(, (listrilmtion across all (if the variat)les is (:Oml)Ut(',(l.</Paragraph> <Paragraph position="3"> ally d(,l/endenl; ltl)on only il;s t)arenls (\]'earl, 1988). \]Pigllr(! \] shows all (LxCallll)\](; \]{ayesiall ll(H:Woll( rel)r(;senl;ing l;h(' disl;ribul;i()n \])(;\,B;(',,1);F,,\]&quot;). \]nsl;('a(l of having oue large (,able wilh 2&quot; lirol)al)ilit.i(~s (with all \]}o()\](;an nod(!s), the (lisl;ribution is rel)resenl(;(t by the (:onditional I)rolial)ility tal)les (C,\])Ts) at ea('h nod(,, su(:h as I>(B I l), F), requiring a (olal of only 2d prol)al)ilitie~s. Not only (lo th(, savings he(:ome more significant with larger networks, lmi; (.he sparse data t/r()l)h;m b(!(:omes m()r(; manag(;abh, as well. Th(! I;l';/illitlg~ s(!l; 11o lollg:er lice(is Ix) (:()v(!r all l)(!l'llllltal;ions (if l;he f(!a(;m'e sets, lml only smaller sul)sets dictated l)y l.he sets of Valial)h!s (if lira CPTs.</Paragraph> <Paragraph position="4"> The n(;l.w(/rk shown in Figure 1 lo()ks simihn to any pOll;ion of th(~ \Vor(1N(~l; hierar(:hy for a reason. In BII\]), belief networks with the same stru(:tur(; as the \Vor(tNet hierar(:hy are automatically (:onstru(:t;ed to rel)l&quot;esent the seh'cl;ional I)reference of an a(1.iective sense. S1)ecifically, the network rei)resents the prol)aliilistic (tistribul;ion over all of the i)rotol;yl)i(:al nouns of an a(1jective~i mid the nouns' semanti(: t'ealures, i.e., P(v,.ot,,,,,,,,..,., < v,.o~,oNF, > I,-!i#i). 'rh(; ,,s(, of Bayesian networks for WSI) has I)een l)rop()sed by others such as Wiebe eL al (1.998), but a different fl)rmulation is used in this mod('l. Th(, construction of the networks in BHD can be divided into three steps: defining 1) the training sets, 2) the structure, and 3) the probabilities, as described in the tbllowing sections.</Paragraph> <Section position="1" start_page="154" end_page="154" type="sub_section"> <SectionTitle> 4.1 ~lYaining Sets </SectionTitle> <Paragraph position="0"> The training set for each of the adje(:tive s(',nses is (;onstructed by ('.xi;ra(:l;ing l;he exenll)lary adje(:tivenoun pairs from tile WordNet glossary. The glossary contains the examlih; usage of the a(lje(:tiv('~s, and l;\]l(', nouns from thein are taken as the training sets</Paragraph> <Paragraph position="2"> The leaf nodes are 1.he nouns within the training set, mid lhe int(~rm('xlial.(; no(les r('th;(:l; the ISA hierar(:hy fr()m \\;()r(1Nel;. The 1)rol)ahilities al each node at(', used i;o (lisanfl)iguat(! novel a(lj(,(:l;ive-noun 1)airs.</Paragraph> <Paragraph position="3"> for th(, a(1.i('(:iives. For exmnl)h! , the nouns &quot;auk&quot;, &quot;oak&quot;, &quot;steak&quot;, &quot;delay&quot; and &quot;amount&quot; (:ompose the training set fl)r great#l (SellSC&quot; lalT/e iv, size,). Note l\]lal; WordNet in(:huh'd &quot;sl;e~al~&quot; ill l;he glossary of greal;#\], l)ul: il; al)l)ears thai 1;\]le 9ood or e:ccelhe'H,l, 5(!lise would lm lliOl'(! aplWol)rial;e. Neverlheh~ss, the }isis of exelnl)lary mmns are sysl:(;malit:aily rel;rh'vcd and nol ('dile(l.</Paragraph> <Paragraph position="4"> Th(&quot; sets (if l/r()tolypi(:al n(/mlS f(/r each a(lj(~(:liv(~ sense have to lie (lisaml)igual;ed lie(:ause, the S(~lllallli(: features (lifter 1)etween ambiguous nouns. Since these n(mns cmmol; lie autonmti(:ally disamlAguated with high accuracy, lhey have to be done mamm\]ly.</Paragraph> <Paragraph position="5"> This is the second t)art of (,lie mmmal process need(;d 1) 3&quot; BIID sitl(:(! the W(/rdNel; gh)ssary is not selnallti(:ally tagged.</Paragraph> </Section> <Section position="2" start_page="154" end_page="155" type="sub_section"> <SectionTitle> 4.2 Belief Network Struetm'e </SectionTitle> <Paragraph position="0"> The belief networks have the same structure as the \VordNet 1SA hierarchy with the ext;et)tion that the edges are direcl;ed from the child nodes to their parents. Ilhlstrated in Figure 2, the BItD-eonstructed network represents the selectional t)referelme of the to I) level nod(;, great#l. The leaf nodes are tile evi(len(:e nodes from the training set mM the internm(liale ilo(les are |;t1(; sema.ntie featm'es (if the leaf nodes. This organization emd)les the belief gathered from the leaf nodes 1;o lie tirot)agated u I) to the tol) level node during inferencing, as described in a later sec-LioIl. 1}11|; first, th(' l)robability table ae(:oml)anyiug ea(:h node needs to be constructed.</Paragraph> </Section> <Section position="3" start_page="155" end_page="155" type="sub_section"> <SectionTitle> 4.3 Quantifying the Network </SectionTitle> <Paragraph position="0"> The two parameters the belief networks require are the CPTs tbr each intermediate node and the priors of the leaf nodes, such as P(great#l, hurricane). The latter is estilnated by tile counts obtained fronl Altavista, as described earlier, and a shortcut is used to specit~y the CPTs. Normally the CPTs ill a flflly specified Bayesian network contain all instantiations of the child and parent values and their corresponding probabilities. For example, the CPT at node D in Figure 1 would have four rows: er(D=tlE:t), Pr(D=tlE=f), Pr(D=flE=t), and Pr(D=flE=f ). This is needed to perform flfll inferencing, where queries can be issued for any instantiation of the variables. However, since the networks in this model are used only for one specific query, where all nodes are instantiated to be true, only the row with all w~riables equal to true, e.g., Pr(D=tlE=t), has to be specified. The nature of this query will be described in more detail in tile next section.</Paragraph> <Paragraph position="1"> To calculate the probability that an intermediate node and all of its parents are true, one divides the number of parents present by the number of possible parents as specified ill WordNet. hi Figure 2, the small clotted nodes denote the absent parents, which deternfine how the probabilities are specified at each node. Recall that tile parents in the belief network are actually the children in the. WordNet hierarchy, so this probability can be seen as the percentage of children actually present, hltuitively, this probability is a form of assigning weights to parts of the network where more related nouns are present in the training set, silnilar to the concept of semantic density. Tile probability, in conjunction with the structure of the belief network, also implicitly encodes the semantic distance between concepts without necessarily 1)enalizing concepts with deep hierarchies. A discount is taken at each ancestral node during inferencing (next section) only wlmn some of its WordNet children are absent in the network.</Paragraph> <Paragraph position="2"> Therefore, the semantic distance can be seen as the number of traversals 11I) the network weighted by the number of siblings present in tile tree (and not by direct edge counting).</Paragraph> </Section> <Section position="4" start_page="155" end_page="155" type="sub_section"> <SectionTitle> 4.4 Querying the Network </SectionTitle> <Paragraph position="0"> With the probability between nodes specified, the network becomes a representation of the selectional prefbrence of an adjective sense, with features from the WordNet ISA hierarchy providing additional knowledge on both semantic densities and semantic distances. To disambiguate a novel adjective-noun pair such as &quot;great flood&quot;, the great#l and great#2 networks (along with 7 other great#/networks not shown here) infer the likelihood that &quot;flood&quot; belongs to the network by comtmting the probability Pr(great, flood, <flood NFs>, proto nouns, <l)roto NFs> I adj #i), even though neither network has ever encountered the noun &quot;flood&quot; before.</Paragraph> <Paragraph position="1"> To perform these inferences, the noun and its features are tenlporarily inserted into the network according to the WordNet hierarchy (if not already present). The prior for this &quot;hypothetical evidence&quot; is obtained the same way as the training set, i.e., by querying Altavista, and the CPTs are updated to reflect this new addition. To calculate the probability at the top node, any Bayesian network inferencing algorithm can be used. However, a query where all nodes are instantiated to true is a special case since the probability can be comlmted by multiplying together all priors and the CPT entries where all variables are true.</Paragraph> <Paragraph position="2"> Ill Figure 3, tile network for great#l is shown with &quot;flood&quot; as tile hypothetical evidence added on the right. The CPT of the node &quot;natural phenomenon&quot; is updated to reflect the newly added evidence. The propagation of the probabilities from the leaf nodes up the network is shown and illustrates how discounts art taken at each intermediate node. Whenever more related concepts are 1)resent in the network, such as &quot;typhoon&quot; and &quot;tornado '~, less discounts are taken and thus a higher probability will result at the root node. Converso.ly, one can see that with a distal concept, such as &quot;taste&quot; (which is ill a completely different branch), the knowledge about &quot;hurricane&quot; will have little or no influence on disambiguating &quot;great taste&quot;.</Paragraph> <Paragraph position="3"> The calculation above can be computed in linear time with respect to the det/th of tlle query noun node (depth=5 in the case of flood#l) and not the the nmnber of nodes ill the network. This is important for scaling the network to represent the large nuinber of nouns needed to accurately model tile selectional preferences of adjective senses. Tile only cost incurred is storage for a summary probability of the children at each internlediate node and time for ut)dating these values when a new piece of evidence is added, which is also linear with respect to the depth of the node.</Paragraph> <Paragraph position="4"> Finally, the probabilities comt)uted by the inference algorithnl are combilmd with the priors established in the earlier section. Tile combined probabilities represent P(adj#i \] adj, noun, <NFs>), and tilt one with the highest probability is classified by BHD as tile most plausible sense of the adjective.</Paragraph> </Section> <Section position="5" start_page="155" end_page="156" type="sub_section"> <SectionTitle> 4.5 Evahmtion </SectionTitle> <Paragraph position="0"> To test the accuracy of BHD, the same procedure described earlier was used. Tile same 135 adjective-noun pairs from SelnCor were disambiguated by BHD and compared to the baseline. Table 2 shows the accuracy results froln evaluating either the first sense of the nouns or all senses of the nouns. The results of the accuracy without the priors Pr(adj#i) in- null .q'reat#l.. The left branch of the network has 1}{'.en omitted for clarity.</Paragraph> <Paragraph position="1"> dicate~ the imI}rovements l}rovided by the likeliho{}{t term alone. The itnl}r(}vement gain(~{l from the addii.ional conl;extual featm:es shows th(~ ell'{~{:liv{m{~ss of the belief networks, l!'~v(m with only 3 t}rol{)tyt)ica.l mmns l}er a(ljecl,ive se.nse (}n av{u'ag{~ (hardly a COml)lete deseril)tion of the sel(B(:tional pr(ff(w(u\]{:{;s); the gain is very encouraging. Wit;h the 1)ri(}rs t'a{:tored in, 13IID iml}r(}ved even flu:ttmr (81%), signifi('antly surllassing the baseline (75.6%), a feat a{:{:omplished 1)3; {rely (me (}ther m{}del that we are aware (}f (airi Sl;el;iIla and Nagao, 1998). Not;e thai; l;h{! l)esl ~/.C{;lll;}/{;y \v~ls a{'hieved by evaluating all senses of th{~ ll(}llllS} }IS exi)e{:ted, since the sele{:l;iollal t)r{~.fer{,Jl{'e is modele{t l;hr{mgh senmnti(: feai;mes (}t&quot; the glossary nouns, not just their word forms. The. r{;as{m for the good accuracy from using only the tirst noun sense is because 72% of them hal}pen to be the first; S0,IlSe. r\]_~heso results are very ('JH;ouragillg si\]lco 11o tagged corl)us and minimal training data were. used.</Paragraph> <Paragraph position="2"> \Y=e believe that with a bigger training set, \]3HD's t)erf(}rnlance will iml}rove even further.</Paragraph> </Section> <Section position="6" start_page="156" end_page="156" type="sub_section"> <SectionTitle> 4.6 Colnl)arison with Other Mo{h,Js </SectionTitle> <Paragraph position="0"> rE() our kn{}wledge, there are {rely two oLher sysl;enls that (lisanfl)iguate a.(lj{~{:tive-noml llairs from unrest.ri{:l;e{l l;ex{;. Results fl'om both models were evaluated against SemCor and thus a comparison is meaningful. In Table 3, each model's accuracy (as well as erence model (+SP), showing the improvements over the baseline by either considering the first llOllIl sense or all noun senses.</Paragraph> <Paragraph position="1"> curay with other inodels.</Paragraph> <Paragraph position="2"> lhe baseline) is provided since different adjet:tivenoun pairs were e, valuated. We find t;he BIt\]) resuits conlpa.ralfle, if not bel;ter, espcc, ially when ihe }IlllOllll{; Of inq)rovenw, nl; ()vet th0, \])aseline is eonsi(lered. The Ino<lel 1) 3, ,SI;e,l;ina (1.998) was {,rai\]md tm SemCor that was merged with a flfll senl;ential parse tree, the determination of which is considered a difficult l)rolflem of its own (Collins, 1997). \Ve belie, re thai; by int:ori}oral;ing tim data from SemC(n (dis(:llSse(l ill I;he fllI;llre work sc, ct;ion)~ {;11o, \]}erforlll}lllCe (ff our sysi;em will surpass Stetina's.</Paragraph> </Section> </Section> class="xml-element"></Paper>