Statistical Models for Unsupervised Prepositional Phrase 
Attachment 
Adwait Ratnaparkhi 
Dept. of Computer and Information Science 
University of Pennsylvania 
200 South 33rd Street 
Philadelphia, PA 19104-6389 
adwait~unagi, cis. upenn, edu 
Abstract 
We present several unsupervised statistical 
models for the prepositional phrase attachment 
task that approach the accuracy of the best su- 
pervised methods for this task. Our unsuper- 
vised approach uses a heuristic based on at- 
tachment proximity and trains from raw text 
that is annotated with only part-of-speech tags 
and morphological base forms, as opposed to 
attachment information. It is therefore less 
resource-intensive and more portable than pre- 
vious corpus-based algorithm proposed for this 
task. We present results for prepositional 
phrase attachment in both English and Span- 
ish. 
1 Introduction 
Prepositional phrase attachment is the task of 
deciding, for a given preposition in a sentence, 
the attachment site that corresponds to the 
interpretation of the sentence. For example, 
the task in the following examples is to de- 
cide whether the preposition with modifies the 
preceding noun phrase (with head word shirt) 
or the preceding verb phrase (with head word 
bought or washed). 
1. I bought the shirt with pockets. 
2. I washed the shirt with soap. 
In sentence 1, with modifies the noun shirt, since 
with pockets describes the shirt. However in sen- 
tence 2, with modifies the verb washed since with 
soap describes how the shirt is washed. While 
this form of attachment ambiguity is usually 
easy for people to resolve, a computer requires 
detailed knowledge about words (e.g., washed 
vs. bought) in order to successfully resolve such 
ambiguities and predict the correct interpreta- 
tion. 
1079 
2 Previous Work 
Most of the previous successful approaches to 
this problem have been statistical or corpus- 
based, and they consider only prepositions 
whose attachment is ambiguous between a pre- 
ceding noun phrase and verb phrase. Previous 
work has framed the problem as a classification 
task, in which the goal is to predict N or V, cor- 
responding to noun or verb attachment, given 
the head verb v, the head noun n, the preposi- 
tion p, and optionally, the object of the prepo- 
sition n2. For example, the (v, n,p, n2) tuples 
corresponding to the example sentences are 
1. bought shirt with pockets 
2. washed shirt with soap 
The correct classifications of tuples 1 and 2 are 
N and V, respectively. 
(Hindle and Rooth, 1993) describes a par- 
tially supervised approach in which the FID- 
DITCH partial parser was used to extract 
(v,n,p) tuples from raw text, where p is a 
preposition whose attachment is ambiguous be- 
tween the head verb v and the head noun n. 
The extracted tuples are then used to con- 
struct a classifier, which resolves unseen ambi- 
guities at around 80% accuracy. Later work, 
such as (Ratnaparkhi et al., 1994; Brill and 
Resnik, 1994; Collins and Brooks, 1995; Merlo 
et al., 1997; Zavrel and Daelemans, 1997; Franz, 
1997), trains and tests on quintuples of the 
form (v,n,p, n2,a) extracted from the Penn 
treebank(Marcus et al., 1994), and has gradu- 
ally improved on this accuracy with other kinds 
of statistical learning methods, yielding up to 
84.5% accuracy(Collins and Brooks, 1995). Re- 
cently, (Stetina and Nagao, 1997) have reported 
88% accuracy by using a corpus-based model in 
conjunction with a semantic dictionary. 
While previous corpus-based methods are 
highly accurate for this task, they are difficult 
to port to other languages because they re- 
quire resources that are expensive to construct 
or simply nonexistent in other languages. We 
present an unsupervised algorithm for prepo- 
sitional phrase attachment in English that re- 
quires only an part-of-speech tagger and a mor- 
phology database, and is therefore less resource- 
intensive and more portable than previous ap- 
proaches, which have all required either tree- 
banks or partial parsers. 
3 Unsupervised Prepositional 
Phrase Attachment 
The exact task of our algorithm will be to con- 
struct a classifier cl which maps an instance of 
an ambiguous prepositional phrase (v, n, p, n2) 
to either N or V, corresponding to noun at- 
tachment or verb attachment, respectively. In 
the full natural language parsing task, there are 
more than just two potential attachment sites, 
but we limit our task to choosing between a verb 
v and a noun n so that we may compare with 
previous supervised attempts on this problem. 
While we will be given the candidate attach- 
ment sites during testing, the training proce- 
dure assumes no a priori information about po- 
tential attachment sites. 
3.1 Generating Training Data From 
Raw Text 
We generate training data from raw text by 
using a part-of-speech tagger, a simple chun- 
ker, an extraction heuristic, and a morphology 
database. The order in which these tools are 
applied to raw text is shown in Table 1. The 
tagger from (Ratnaparkhi, 1996) first annotates 
sentences of raw text with a sequence of part- 
of-speech tags. The chunker, implemented with 
two small regular expressions, then replaces 
simple noun phrases and quantifier phrases with 
their head words. The extraction heuristic then 
finds head word tuples and their likely attach- 
ments from the tagged and chunked text. The 
heuristic relies on the observed fact that in En- 
glish and in languages with similar word order, 
the attachment site of a preposition is usually 
located only a few words to the left of the prepo- 
sition. Finally, numbers are replaced by a single 
token, the text is converted to lower case, and 
the morphology database is used to find the base 
forms of the verbs and nouns. 
The extracted head word tuples differ from 
the training data used in previous supervised at- 
tempts in an important way. In the supervised 
case, both of the potential sites, namely the verb 
v and the noun n are known before the attach- 
ment is resolved. In the unsupervised case dis- 
cussed here, the extraction heuristic only finds 
what it thinks are unambiguous cases of prepo- 
sitional phrase attachment. Therefore, there is 
only one possible attachment site for the prepo- 
sition, and either the verb v or the noun n does 
not exist, in the case of noun-attached prepo- 
sition or a verb-attached preposition, respec- 
tively. This extraction heuristic loosely resem- 
bles a step in the bootstrapping procedure used 
to get training data for the classifier of (Hindle 
and Rooth, 1993). In that step, unambiguous 
attachments from the FIDDITCH parser's out- 
put are initially used to resolve some of the am- 
biguous attachments, and the resolved cases are 
iteratively used to disambiguate the remaining 
unresolved cases. Our procedure differs criti- 
cally from (Hindle and Rooth, 1993) in that we 
do not iterate, we extract unambiguous attach- 
ments from unparsed input sentences, and we 
totally ignore the ambiguous cases. It is the hy- 
pothesis of this approach that the information 
in just the unambiguous attachment events can 
resolve the ambiguous attachment events of the 
test data. 
3.1.1 Heuristic Extraction of 
Unambiguous Cases 
Given a tagged and chunked sentence, the ex- 
traction heuristic returns head word tuples of 
the form (v,p, n2) or (n,p, n2), where v is the 
verb, n is the noun, p is the preposition, n2 is 
the object of the preposition. The main idea 
of the extraction heuristic is that an attach- 
ment site of a preposition is usually within a 
few words to the left of the preposition. We 
extract : 
(v,p, n2) if 
• p is a preposition (p ~ of) 
• v is the first verb that occurs within K 
words to the left of p 
• v is not a form of the verb to be 
• No noun occurs between v and p 
1080 
Tool Output 
Raw Text 
POS Tagger 
Chunker 
Extraction Heuristic 
Morphology 
The professional conduct of lawyers in other jurisdictions is guided by Amer- 
ican Bar Association rules or by state bar ethics codes, none of which permit 
non-lawyers to be partners in law firms. 
The/DT professional/JJ conduct/NN of/IN lawyers/NNS in/IN other/JJ 
jurisdictions/NNS is/VBZ guided/VBN by/IN American/NNP Bar/NNP 
Association/NNP rules/NNS or/CC by/IN state/NN bar/NN ethics/NNS 
codes/NNS ,/, none/NN of/IN which/WDT permit/VBP non-lawyers/NNS 
to/TO be/VB partners/NNS in/IN law/NN firms/NNS ./. 
conduct/NN of/IN lawyers/NNS in/IN jurisdictions/NNS is/VBZ 
guided/VBN by/IN rules/NNS or/CC by/IN codes/NNS ,/, none/NN 
of/IN which/WDW permit/VBP non-lawyers/NNS to/TO be/VS part- 
ners/NNS in/IN firms/NNS ./. 
(n =lawyers, p =in, n2 =jurisdictions) 
(v =guided, p =by, n2 =rules) 
(n =lawyer, p =in, n2 =jurisdiction) 
(v =guide, p =by, n2 =rule) 
Table 1: How to obtain training data from raw text 
• n2 is the first noun that occurs within 
K words to the right of p 
• No verb occurs between p and n2 
(n,p, n2) if 
• p is a preposition (p ~ of) 
• n is the first noun that occurs within 
K words to the left of p 
• No verb occurs within K words to the 
left of p 
• n2 is the first noun that occurs within 
K words to the right of p 
• No verb occurs between p and n2 
Table 1 also shows the result of the applying the 
extraction heuristic to a sample sentence. 
The heuristic ignores cases where p = of, 
since such cases are rarely ambiguous, and we 
opt to model them deterministically as noun at- 
tachments. We will report accuracies (in Sec- 
tion 5) on both cases where p = of and where 
p ~ of. Also, the heuristic excludes examples 
with the verb to be from the training set (but 
not the test set) since we found them to be un- 
reliable sources of evidence. 
1081 
3.2 Accuracy of Extraction Heuristic 
Applying the extraction heuristic to 970K unan- 
notated sentences from the 1988 Wall St. Jour- 
nal 1 data yields approximately 910K unique 
head word tuples of the form (v,p, n2) or 
(n,p, n2). The extraction heuristic is far from 
perfect; when applied to and compared with the 
annotated Wall St. Journal data of the Penn 
treebank, only 69% of the extracted head word 
tuples represent correct attachments. 2 The ex- 
tracted tuples are meant to be a noisy but abun- 
dant substitute for the information that one 
might get from a treebank. Tables 2 and 3 
list the most frequent extracted head word tu- 
ples for unambiguous verb and noun attach- 
ments, respectively. Many of the frequent noun- 
attached (n,p, n2) tuples, such as hum to num, 3 
are incorrect. The prepositional phrase to hum 
is usually attached to a verb such as rise or fall 
in the Wall St. Journal domain, e.g., Profits 
rose ,{6 ~ to 52 million. 
1This data is available from the Linguistic Data Con- 
sortium, http ://www. Idc. apenn, edu 
2This accuracy also excludes cases where p -- of. 
3Recall the hum is the token for quantifier phrases 
identified by the chunker, like 5 million, or 6 ~. 
Frequency Verb 
8110 close 
1926 reach 
1539 rise 
1438 compare 
1072 fall 
970 account 
887 value 
839 say 
680 compare 
673 price 
\] Prep \] Noun2 
at num 
for comment 
to num 
with num 
to hum 
for hum 
at million 
in interview 
with million 
at num 
Table 2: Most frequent (v,p, n2) tuples 
Frequency Noun 
1983 num 
923 num 
853 share 
723 trading 
721 num 
560 num 
519 share 
461 hum 
417 trading 
376 share 
I Prep \[ Noun2 
to num 
from num 
from million 
on exchange 
in num 
to month 
on revenue 
to day 
on yesterday 
on sale 
Table 3: Most frequent (n,p, n2) tuples 
4 Statistical Models 
While the extracted tuples of the form (n, p, n2) 
and (v, p, n2) represent unambiguous noun and 
verb attachments in which either the verb or 
noun is known, our eventual goal is to resolve 
ambiguous attachments in the test data of the 
form (v, n,p, n2), in which both the noun n and 
verb v are always known. We therefore must 
use any information in the unambiguous cases 
to resolve the ambiguous cases. A natural way is 
to use a classifier that compares the probability 
of each outcome: 
cl(v,n,p, n2) = 
N if p = of 
arg maxae{N,V} Pr(v,n,p, a) otherwise 
(1) 
We do not currently use n2 in the probability 
model, and we omit it from further discussion. 
We can factor Pr(v,n,p, a) as follows: 
Pr(v,n,p,a) = Pr(v)Pr(n) 
Pr(a\[v,n) 
Pr(p\[a, v, n) 
The terms Pr(n) and Pr(v) are independent of 
the attachment a and need not be computed 
in d (1), but the estimation of Pr(a\[v,n) and 
Pr(pla, v,n ) is problematic since our training 
data, i.e., the head words extracted from raw 
text, occur with either n or v, but never both 
n, v. This leads to make some heuristically mo- 
tivated approximations. Let the random vari- 
able ¢ range over {true, false}, and let it de- 
note the presence or absence of any preposition 
that is unambiguously attached to the noun or 
verb in question. Then p(¢ = true\]n) is the 
conditional probability that a particular noun 
n in free text has an unambiguous prepositional 
phrase attachment. (¢ = true will be written 
simply as true.) We approximate Pr(alv , n) as 
follows: 
Pr(true\[n) Pr(a -- N\]v, n) 
Z(v,n) 
Pr(truelv) Pr(a = VIv ,n) 
Z(v,n) 
Z(v,n) = Pr(true\[n) + Pr(trueIv ) 
The rationale behind this approximation is that 
the tendency of a v,n pair towards a noun 
(verb) attachment is related to the tendency of 
the noun (verb) alone to occur with an unam- 
biguous prepositional phrase. The Z(v, n) term 
exists only to make the approximation a well 
formed probability over a E {N, V}. 
We approximate Pr(p\[a, v, n) as follows: 
Pr(p\[a = N, v, n) .~ Pr(p\[true, n) 
Pr(p\[a = V,v,n) ~ Pr(pItrue, v) 
The rationale behind these approximations is 
that when generating p given a noun (verb) at- 
tachment, only the counts involving the noun 
(verb) are relevant, assuming also that the noun 
(verb) has an attached prepositional phrase, i.e., 
d? = true. 
We use word statistics from both the tagged 
corpus and the set of extracted head word tuples 
to estimate the probability of generating ¢ = 
true, p, and n2. Counts from the extracted set 
of tuples assume that ¢ -- true, while counts 
from the corpus itself may correspond to either 
q5 = true or ¢ = false, depending on if the noun 
1082 
or verb in question is, or is not, respectively, 
unambiguously attached to a preposition. 
4.1 Generate ¢ 
The quantities Pr(trueln ) and Pr(truelv ) de- 
note the conditional probability that n or v 
will occur with some unambiguously attached 
preposition, and are estimated as follows: 
{ c(n) > 0 
Pr(trueln) = .5 otherwise 
> o 
Pr(truelv) = .5 otherwise 
where c(n) and c(v) are counts from the tagged 
corpus, and where c(n, true) and c(v, true) are 
counts from the extracted head word tuples. 
4.2 Generate p 
The terms Pr(p\[n, true) and Pr(plv, true) de- 
note the conditional probability that a particu- 
lar preposition p will occur as an unambiguous 
attachment to n or v. We present two tech- 
niques to estimate this probability, one based 
on bigram counts and another based on an in- 
terpolation method. 
4.2.1 Bigram Counts 
This technique uses the bigram counts of the 
extracted head word tuples, and backs off to 
the uniform distribution when the denominator 
is zero. 
c(n,p,true) Pr(pltrue, n) = ~(n,true) c(n, true) > 0 
otherwise 
c(v,p,true) Pr(pltrue ,v) = ~(v,tr~,) c(v, true) > 0 
otherwise 
where ~ is the set of possible prepositions, 
where all the counts c(...) are from the ex- 
tracted head word tuples. 
4.2.2 Interpolation 
This technique is similar to the one in (Hindle 
and Rooth, 1993), and interpolates between the 
tendencies of the (v,p) and (n,p) bigrams and 
the tendency of the type of attachment (e.g., N 
or V) towards a particular preposition p. First, 
define cN(p) = ~n c(n,p, true) as the number 
of noun attached tuples with the preposition 
p, and define C N = ~'~pCN(P) as the number 
of noun attached tuples. Analogously, define 
cy(p) = ~vc(v,p, true) and cy = ~pcv(p). 
The counts c(n,p, true) and c(v,p, true) are 
from the extracted head word tuples. Using the 
above notation, we can interpolate as follows: 
Pr(pltrue, n) 
Pr(pltrue , v) 
c(n,p, true) + c~(p) CN 
c(n, true) + 1 
c(v,p, true) + cv(P) cv 
c(v, true) + 1 
5 Evaluation in English 
Approximately 970K unannotated sentences 
from the 1988 Wall St. Journal were pro- 
cessed in a manner identical to the example sen- 
tence in Table 1. The result was approximately 
910,000 head word tuples of the form (v,p, n2) 
or (n,p, n2). Note that while the head word 
tuples represent correct attachments only 69% 
of the time, their quantity is about 45 times 
greater than the quantity of data used in previ- 
ous supervised approaches. The extracted data 
was used as training material for the three clas- 
sifters Clbase , Clinterp, and Clbigram. Each classi- 
fier is constructed as follows: 
Clbase This is the "baseline" classifier that pre- 
dicts N of p = of, and V otherwise. 
Clinterp: This classifier has the form of equa- 
tion (1), uses the method in section 4.1 to 
generate ¢, and the method in section 4.2.2 
to generate p. 
clbigram: This classifier has the form of equa- 
tion (1), uses the method in section 4.1 to 
generate ¢, and the method in section 4.2.1 
to generate p. 
Table 4 shows accuracies of the classifiers on 
the test set of (Ratnaparkhi et al., 1994), which 
is derived from the manually annotated attach- 
ments in the Penn Treebank Wall St. Journal 
data. The Penn Treebank is drawn from the 
1989 Wall St. Journal data, so there is no pos- 
sibility of overlap with our training data. Fur- 
thermore, the extraction heuristic was devel- 
oped and tuned on a "development set", i.e., a 
set of annotated examples that did not overlap 
with either the test set or the training set. 
1083 
Subset 
p= of 
Number of Events 
925 
clbigrarn 
917 
Clinterp 
917 
Clbase 
917 
p ~ of 2172 1620 1618 1263 
Total 3097 2i80 2535 
81.85% 
2537 
81.91% Accuracy 70.39% 
Table 4: Accuracy of mostly unsupervised classifiers on English Wall St. Journal data 
Attachment Pr(alv ,n) Pr(p\[a,v,n) 
Noun(a = N) .02 .24 
Verb(a = V) .30 .44 
Table 5: The key probabilities for the ambigu- 
ous example rise hum to hum 
Table 5 shows the two probabilities Pr(a\[v, n) 
and Pr(p\[a, v, n), using the same approxima- 
tions as clbigram, for the ambiguous example rise 
num to num. (Recall that Pr(v) and Pr(n) are 
not needed.) While the tuple (num, to, num) is 
more frequent than (rise, to, num), the condi- 
tional probabilities prefer a = V, which is the 
choice that maximizes Pr(v, n,p, a). 
Both classifiers Clinter p and dbigram clearly 
outperform the. baseline, but the classifier 
dinterp does not outperform dbigram, even 
though it interpolates between the less specific 
evidence (the preposition counts) and more spe- 
cific evidence (the bigram counts). This may be 
due to the errors in our extracted training data; 
supervised classifiers that train from clean data 
typically benefit greatly by combining less spe- 
cific evidence with more specific evidence. 
Despite the errors in the training data, 
the performance of the unsupervised classifiers 
(81.9%) begins to approach the best perfor- 
mance of the comparable supervised classifiers 
(84.5%). (Our goal is to replicate the super- 
vision of a treebank, but not a semantic dictio- 
nary, so we do not compare against (Stetina and 
Nagao, 1997).) Furthermore, we do not use the 
second noun n2, whereas the best supervised 
methods use this information. Our result shows 
that the information in imperfect but abundant 
data from unambiguous attachments, as shown 
in Tables 2 and 3, is sufficient to resolve ambigu- 
ous prepositional phrase attachments at accu- 
racies just under the supervised state-of-the-art 
accuracy. 
6 Evaluation in Spanish 
We claim that our approach is portable to lan- 
guages with similar word order, and we support 
this claim by demonstrating our approach on 
the Spanish language. We used the Spanish 
tagger and morphological analyzer developed 
at the Xerox Research Centre Europe 4 and we 
modified the extraction heuristic to account for 
the new tagset, and to account for the Spanish 
equivalents of the words of (i.e., de or del) and 
to be (i.e., set). Chunking was not performed 
on the Spanish data. We used 450k sentences 
of raw text from the Linguistic Data Consor- 
tium's Spanish News Text Collection to extract 
a training set, and we used a non-overlapping 
set of 50k sentences from the collection to create 
test sets. Three native Spanish speakers were 
asked to extract and annotate ambiguous in- 
stances of Spanish prepositional phrase attach- 
ments. They annotated two sets (using the full 
sentence context); one set consisted of all am- 
biguous prepositional phrase attachments of the 
form (v,n,p, n2), and the other set consisted of 
cases where p = con. For testing our classifier, 
we used only those judgments on which all three 
annotators agreed. 
6.1 Performance 
The performance of the classifiers Clbigram, 
Clinterp, and Clbase , when trained and tested 
on Spanish language data, are shown in Ta- 
ble 6. The Spanish test set has fewer ambiguous 
prepositions than the English test set, as shown 
by the accuracy of Clbase. However, the accuracy 
improvements of Clbigra m over Clbase are statisti- 
cally significant for both test sets. 5 
4These were supplied by Dr. Lauri Kartunnen during 
his visit to Penn. 
5Using proportions of changed cases, P ---- 0.0258 for 
the first set, and P -- 0.0108 for the set where p = con 
1084 
Test Set 
All p 
Subset 
p = delldel 
p # delldel 
Accuracy 
Number of Events 
156 
116 
272 
Clbigrarn 
154 
103 
257 
94.5% 
p = con Total 192 166 
Accuracy - 86.4% 
clinterp dbase 
154 154 
97 91 
251 245 
92.3% 90.1% 
11601151 83.3% 78.6% 
Table 6: Accuracy of mostly unsupervised classifiers on Spanish News Data 
7 Conclusion 
The unsupervised algorithm for prepositional 
phrase attachment presented here is the only 
algorithm in the published literature that can 
significantly outperform the baseline without 
using data derived from a treebank or parser. 
The accuracy of our technique approaches the 
accuracy of the best supervised methods, and 
does so with only a tiny fraction of the supervi- 
sion. Since only a small part of the extraction 
heuristic is specific to English, and since part- 
of-speech taggers and morphology databases are 
widely available in other languages, our ap- 
proach is far more portable than previous ap- 
proaches for this problem. We successfully 
demonstrated the portability of our approach 
by applying it to the prepositional phrase at- 
tachment task in the Spanish language. 
8 Acknowledgments 
We thank Dr. Lauri Kartunnen for lending us 
the Spanish natural language tools, and Mike 
Collins for helpful discussions on this work. 

References 
ACL. 1997. Proceedings of the 35th Annual 
Meeting of the A CL, and 8th Conference of 
the EACL, Madrid, Spain, July. 
Eric Brill and Phil Resnik. 1994. A Rule Based 
Approach to Prepositional Phrase Attach- 
ment Disambiguation. In Proceedings of the 
Fifteenth International Conference on Com- 
putational Linguistics (COLING). 
Michael Collins and James Brooks. 1995. 
Prepositional Phrase Attachment through a 
Backed-off Model. In David Yarowsky and 
Kenneth Church, editors, Proceedings of the 
Third Workshop on Very Large Corpora, 
pages 27-38, Cambridge, Massachusetts, 
June. 
Alexander Franz. 1997. Independence Assump- 
tions Considered Harmful. In ACL (ACL, 
1997). 
Donald Hindle and Mats Rooth. 1993. Struc- 
tural Ambiguity and Lexical Relations. Com- 
putational Linguistics, 19(1):103-120. 
Mitchell P. Marcus, Beatrice Santorini, and 
Mary Ann Marcinkiewicz. 1994. Building 
a large annotated corpus of English: the 
Penn Treebank. Computational Linguistics, 
19(2):313-330. 
Paola Merlo, Matthew W. Crocker, and 
Cathy Berthouzoz. 1997. Attaching Multiple 
Prepositional Phrases: Generalized Backed- 
off Estimation. In Claire Cardie and Ralph 
Weischedel, editors, Second Conference on 
Empirical Methods in Natural Language Pro- 
cessing, pages 149-155, Providence, R.I., 
Aug. 1-2. 
Adwait Ratnaparkhi, Jeff Reynar, and Salim 
Roukos. 1994. A Maximum Entropy Model 
for Prepositional Phrase Attachment. In Pro- 
ceedings of the Human Language Technology 
Workshop, pages 250-255, Plalnsboro, N.J. 
ARPA. 
Adwait Ratnaparkhi. 1996. A Maximum En- 
tropy Part of Speech Tagger. In Eric Brill 
and Kenneth Church, editors, Conference on 
Empirical Methods in Natural Language Pro- 
cessing, University of Pennsylvania, May 17- 
18. 
Jiri Stetina and Makoto Nagao. 1997. Corpus 
Based PP Attachment Ambiguity Resolution 
with a Semantic Dictionary. In Jou Zhou and 
Kenneth Church, editors, Proceedings of the 
Fifth Workshop on Very Large Corpora, pages 
66-80, Beijing and Hong Kong, Aug. 18 - 20. 
Jakub Zavrel and Walter Daelemans. 1997. 
Memory-Based Learning: Using Similarity 
for Smoothing. In ACL (ACL, 1997). 
