Proceedings of the 43rd Annual Meeting of the ACL, pages 34–41,
Ann Arbor, June 2005. c©2005 Association for Computational Linguistics
Learning Semantic Classes for Word Sense Disambiguation
Upali S. Kohomban Wee Sun Lee
Department of Computer Science
National University of Singapore
Singapore, 117584
{upalisat,leews}@comp.nus.edu.sg
Abstract
Word Sense Disambiguation suffers from
a long-standing problem of knowledge ac-
quisition bottleneck. Although state of the
art supervised systems report good accu-
racies for selected words, they have not
been shown to be promising in terms of
scalability. In this paper, we present an ap-
proach for learning coarser and more gen-
eral set of concepts from a sense tagged
corpus, in order to alleviate the knowl-
edge acquisition bottleneck. We show that
these general concepts can be transformed
to fine grained word senses using simple
heuristics, and applying the technique for
recent SENSEVAL data sets shows that our
approach can yield state of the art perfor-
mance.
1 Introduction
Word Sense Disambiguation (WSD) is the task of
determining the meaning of a word in a given con-
text. This task has a long history in natural language
processing, and is considered to be an intermediate
task, success of which is considered to be important
for other tasks such as Machine Translation, Lan-
guage Understanding, and Information Retrieval.
Despite a long history of attempts to solve WSD
problem by empirical means, there is not any clear
consensus on what it takes to build a high perfor-
mance implementation of WSD. Algorithms based
on Supervised Learning, in general, show better per-
formance compared to unsupervised systems. But
they suffer from a serious drawback: the difficulty
of acquiring considerable amounts of training data,
also known as knowledge acquisition bottleneck. In
the typical setting, supervised learning needs train-
ing data created for each and every polysemous
word; Ng (1997) estimates an effort of 16 person-
years for acquiring training data for 3,200 significant
words in English. Mihalcea and Chklovski (2003)
provide a similar estimate of an 80 person-year ef-
fort for creating manually labelled training data for
about 20,000 words in a common English dictionary.
Two basic approaches have been tried as solu-
tions to the lack of training data, namely unsu-
pervised systems and semi-supervised bootstrapping
techniques. Unsupervised systems mostly work
on knowledge-based techniques, exploiting sense
knowledge encoded in machine-readable dictionary
entries, taxonomical hierarchies such as WORD-
NET (Fellbaum, 1998), and so on. Most of the
bootstrapping techniques start from a few ‘seed’ la-
belled examples, classify some unlabelled instances
using this knowledge, and iteratively expand their
knowledge using information available within newly
labelled data. Some others employ hierarchical rel-
atives such as hypernyms and hyponyms.
In this work, we present another practical alterna-
tive: we reduce the WSD problem to a one of finding
generic semantic class of a given word instance. We
show that learning such classes can help relieve the
problem of knowledge acquisition bottleneck.
1.1 Learning senses as concepts
As the semantic classes we propose learning, we
use WORDNET lexicographer file identifiers corre-
34
sponding to each fine-grained sense. By learning
these generic classes, we show that we can reuse
training data, without having to rely on specific
training data for each word. This can be done be-
cause the semantic classes are common to words
unlike senses; for learning the properties of a given
class, we can use the data from various words. For
instance, the noun crane falls into two semantic
classes ANIMAL and ARTEFACT. We can expect the
words such as pelican and eagle (in the bird sense)
to have similar usage patterns to those of ANIMAL
sense of crane, and to provide common training ex-
amples for that particular class.
For learning these classes, we can make use of any
training example labelled with WORDNET senses
for supervised WSD, as we describe in section 3.1.
Once the classification is done for an instance, the
resulting semantic classes can be transformed into
finer grained senses using some heuristical mapping,
as we show in the next sub section. This would not
guarantee a perfect conversion because such a map-
ping can miss some finer senses, but as we show in
what follows, this problem in itself does not prevent
us from attaining good performance in a practical
WSD setting.
1.2 Information loss in coarse grained senses
As an empirical verification of the hypothesis that
we can still build effective fine-grained sense dis-
ambiguators despite the loss of information, we an-
alyzed the performance of a hypothetical coarse
grained classifier that can perform at 100% accu-
racy. As the general set of classes, we used WORD-
NET unique beginners, of which there are 25 for
nouns, and 15 for verbs.
To simulate this classifier on SENSEVAL English
all-words tasks’ data (Edmonds and Cotton, 2001;
Snyder and Palmer, 2004), we mapped the fine-
grained senses from official answer keys to their
respective beginners. There is an information loss
in this mapping, because each unique beginner can
typically include more than one sense. To see how
this ‘classifier’ fares in a fine-grained task, we can
map the ‘answers’ back to WORDNET fine-grained
senses by picking up the sense with the lowest sense
number that falls within each unique beginner. In
principal, this is the most likely sense within the
class, because WORDNET senses are said to be
12
312
412
512
612
712
812
912
1012
1112
3112
124131415161417124131819202117125131415161417125131819202117
22232423
2425
Figure 1: Performance of a hypothetical coarse-
grained classifier, output mapped to fine-grained
senses, on SENSEVAL English all-words tasks.
ordered in descending order of frequency. Since
this sense is not necessarily the same as the origi-
nal sense of the instance, the accuracy of the fine-
grained answers will be below 100%.
Figure 1 shows the performance of this trans-
formed fine-grained classifier (CG) for nouns and
verbs with SENSEVAL-2 and 3 English all words
task data (marked as S2 and S3 respectively),
along with the baseline WORDNET first sense (BL),
and the best-performer classifiers at each SENSE-
VAL excercise (CL), SMUaw (Mihalcea, 2002) and
GAMBL-AW (Decadt et al., 2004) respectively.
There is a considerable difference in terms of im-
provement over baseline, between the state-of-the-
art systems and the hypothetical optimal coarse-
grained system. This shows us that there is an im-
provement in performance that we can attain over
the state-of-the-art, if we can create a classifier for
even a very coarse level of senses, with sufficiently
high accuracy. We believe that the chances for such
a high accuracy in a coarse-grained sense classifier
is better, for several reasons:
• previously reported good performance for
coarse grained systems (Yarowsky, 1992)
• better availability of data, due to the possibil-
ity of reusing data created for different words.
For instance, labelled data for the noun ‘crane’
is not found in SEMCOR corpus at all, but
there are more than 1000 sample instances for
the concept ANIMAL, and more than 9000 for
ARTEFACT.
35
• higher inter-annotator agreement levels and
lower corpus/genre dependencies in train-
ing/testing data due to coarser senses.
1.3 Overall approach
Basically, we assume that we can learn the ‘con-
cepts’, in terms of WORDNET unique beginners, us-
ing a set of data labelled with these concepts, re-
gardless of the actual word that is labelled. Hence,
we can use a generic data set that is large enough,
where various words provide training examples for
these concepts, instead of relying upon data from the
examples of the same word that is being classified.
Unfortunately, simply labelling each instance
with its semantic class and then using standard su-
pervised learning algorithms did not work well. This
is probably because the effectiveness of the feature
patterns often depend on the actual word being dis-
ambiguated and not just its semantic class. For ex-
ample, the phrase ‘run the newspaper’ effectively
indicates that ‘newspaper’ belongs to the seman-
tic class GROUP. But ‘run the tape’ indicates that
‘tape’ belongs to the semantic class ARTEFACT. The
collocation ‘run the’ is effective for indicating the
GROUP sense only for ‘newspaper’ and closely re-
lated words such as ‘department’ or ‘school’.
In this experiment, we use a k-nearest neighbor
classifier. In order to allow training examples of
different words from the same semantic class to
effectively provide information for each other, we
modify the distance between instances in a way
that makes the distance between instances of simi-
lar words smaller. This is described in Section 3.
The rest of the paper is organized as follows: In
section 2, we discuss several related work. We pro-
ceed on to a detailed description of our system in
section 3, and discuss the empirical results in section
4, showing that our representation can yield state of
the art performance.
2 Related Work
Using generic classes as word senses has been
done several times in WSD, in various contexts.
Resnik (1997) described a method to acquire a set
of conceptual classes for word senses, employing
selectional preferences, based on the idea that cer-
tain linguistic predicates constraint the semantic in-
terpretation of underlying words into certain classes.
The method he proposed could acquire these con-
straints from a raw corpus automatically.
Classification proposed by Levin (1993) for Eng-
lish verbs remains a matter of interest. Although
these classes are based on syntactic properties unlike
those in WORDNET, it has been shown that they can
be used in automatic classifications (Stevenson and
Merlo, 2000). Korhonen (2002) proposed a method
for mapping WORDNET entries into Levin classes.
WSD System presented by Crestan et al. (2001)
in SENSEVAL-2 classified words into WORD-
NET unique beginners. However, their approach
did not use the fact that the primes are common for
words, and training data can hence be reused.
Yarowsky (1992) used Roget’s Thesaurus cate-
gories as classes for word senses. These classes dif-
fer from those mentioned above, by the fact that they
are based on topical context rather than syntax or
grammar.
3 Basic Design of the System
The system consists of three classifiers, built using
local context, part of speech and syntax-based rela-
tionships respectively, and combined with the most-
frequent sense classifier by using weighted major-
ity voting. Our experiments (section 4.3) show that
building separate classifiers from different subsets
of features and combining them works better than
building one classifier by concatenating the features
together.
For training and testing, we used publicly avail-
able data sets, namely SEMCOR corpus (Miller et
al., 1993) and SENSEVAL English all-words task
data. In order to evaluate the systems performance
in vivo, we mapped the outputs of our classifier to
the answers given in the key. Although we face a
penalty here due to the loss of granularity, this ap-
proach allows a direct comparison of actual usability
of our system.
3.1 Data
As training corpus, we used Brown-1 and Brown-
2 parts of SEMCOR corpus; these parts have all of
their open-class words tagged with corresponding
WORDNET senses. A part of the training corpus was
set aside as the development corpus. This part was
selected by randomly selecting a portion of multi-
36
class words (600 instances for each part of speech)
from the training data set. As labels, the seman-
tic class (lexicographic file number) was extracted
from the sense key of each instance. Testing data
sets from SENSEVAL-2 and SENSEVAL-3 English
all-words tasks were used as testing corpora.
3.2 Features
The feature set we selected was fairly simple; As
we understood from our initial experiments, wide-
window context features and topical context were
not of much use for learning semantic classes from
a multi-word training data set. Instead of general-
izing, wider context windows add to noise, as seen
from validation experiments with held-out data.
Following are the features we used:
3.2.1 Local context
This is a window of n words to the left, and n
words to the right, where n ∈ {1,2,3} is a parame-
ter we selected via cross validation.1
Punctuation marks were removed and all words
were converted into lower case. The feature vec-
tor was calculated the same way for both nouns and
verbs. The window did not exceed the boundaries
of a sentence; when there were not enough words to
either side of the word within the window, the value
NULL was used to fill the remaining positions.
For instance, for the noun ‘companion’ in sen-
tence (given with POS tags)
‘Henry/NNP peered/VBD doubtfully/RB
at/IN his/PRP$ drinking/NN compan-
ion/NN through/IN bleary/JJ ,/, tear-
filled/JJ eyes/NNS ./.’
the local context feature vector is [at,
his, drinking, through, bleary,
tear-filled], for window size n = 3. Notice
that we did not consider the hyphenated words as
two words, when the data files had them annotated
as a single token.
3.2.2 Part of speech
This consists of parts of speech for a window of
n words to both sides of word (excluding the word
1Validation results showed that a window of two words to
both sides yields the best performance for both local context and
POS features. n = 2 is the size we used in actual evaluation.
Feature Example Value
nouns
Subject - verb [art] represents a culture represent
Verb - object He sells his [art] sell
Adjectival modifiers the ancient [art] of runes ancient
Prepositional connectors academy of folk [art] academy of
Post-nominal modifiers the [art] of fishing of fishing
verbs
Subject - verb He [sells] his art he
Verb - object He [sells] his art art
Infinitive connector He will [sell] his art he
Adverbial modifier He can [paint] well well
Words in split infinitives to boldly [go] boldly
Table 1: Syntactic relations used as features. The
target word is shown inside [brackets]
itself), with quotation signs and punctuation marks
ignored. For SEMCOR files, existing parts of speech
were used; for SENSEVAL data files, parts of speech
from the accompanying Penn-Treebank parsed data
files were aligned with the XML data files. The
value vector is calculated the same way as the lo-
cal context, with the same constraint on sentence
boundaries, replacing vacancies with NULL.
As an example, for the sentence we used in the
previous example, the part-of-speech vector with
context size n = 3 for the verb peered is [NULL,
NULL, NNP, RB, IN, PRP$].
3.2.3 Syntactic relations with the word
The words that hold several kinds of syntactic re-
lations with the word under consideration were se-
lected. We used Link Grammar parser due to Sleator
and Temperley (1991) because of the information-
rich parse results produced by it.
Sentences in SEMCOR corpus files and the SEN-
SEVAL files were parsed with Link parser, and words
were aligned with links. A given instance of a word
can have more than one syntactic features present.
Each of these features was considered as a binary
feature, and a vector of binary values was con-
structed, of which each element denoted a unique
feature found in the test set of the word.
Each syntactic pattern feature falls into either of
two types collocation or relation:
Collocation features Collocation features are
such features that connect the word under consid-
eration to another word, with a preposition or an in-
finitive in between — for instance, the phrase ‘art
of change-ringing’ for the word art. For these fea-
tures, the feature value consists of two words, which
are connected to the given word either from left or
37
from right, in a given order. For the above example,
the feature value is [∼.of.change-ringing],
where ∼ denotes the placeholder for word under
consideration.
Relational features Relational features represent
more direct grammatical relationships, such as
subject-verb or noun-adjective, the word under con-
sideration has with surrounding words. When
encoding the feature value, we specified the re-
lation type and the value of the feature in the
given instance. For instance, in the phrase ‘Henry
peered doubtfully’, the adverbial modifier feature
for the verb ‘peered’ is encoded as [adverb-mod
doubtfully].
A description of the relations for each part of
speech is given in the table 1.
3.3 Classifier and instance weighting
The classifier we used was TiMBL, a memory based
learner due to Daelemans et al. (2003). One reason
for this choice was that memory based learning has
shown to perform well in previous word sense dis-
ambiguation tasks, including some best performers
in SENSEVAL, such as (Hoste et al., 2001; Decadt
et al., 2004; Mihalcea and Faruque, 2004). Another
reason is that TiMBL supported exemplar weights, a
necessary feature for our system for the reasons we
describe in the next section.
One of the salient features of our system is that it
does not consider every example to be equally im-
portant. Due to the fact that training instances from
different instances can provide confusing examples,
as shown in section 1.3, such an approach cannot be
trusted to give good performance; we verified this
by our own findings through empirical evaluations
as shown in section 4.2.
3.3.1 Weighting instances with similarity
We use a similarity based measure to assign
weights to training examples. In the method we use,
these weights are used to adjust the distances be-
tween the test instance and the example instances.
The distances are adjusted according to the formula
∆E(X,Y) = ∆(X,Y)ew
X +epsilon1
,
where ∆E(X,Y) is the adjusted distance between
instance Y and example X, ∆(X,Y) is the original
distance, ewX is the exemplar weight of instance X.
The small constant epsilon1 is added to avoid division by
zero.
There are various schemes used to measure inter-
sense similarity. Our experiments showed that the
measure defined by Jiang and Conrath (1997) (JCn)
yields best results. Results for various weighting
schemes are discussed in section 4.2.
3.3.2 Instance weighting explained
The exemplar weights were derived from the fol-
lowing method:
1. pick a labelled example e, and extract its sense
se and semantic class ce.
2. if the class ce is a candidate for the current test
word w, i.e. w has any senses that fall into
ce, find out the most frequent sense of w, scew ,
within ce. We define the most frequent sense
within a class as the sense that has the lowest
WORDNET sense number within that class. If
none of the senses of w fall into ce, we ignore
that example.
3. calculate the relatedness measure between se
and scew , using whatever the similarity metric
being considered. This is the exemplar weight
for example e.
In the implementation, we used freely available
WordNet::Similarity package (Pedersen et
al., 2004). 2
3.4 Classifier optimization
A part of SEMCOR corpus was used as a validation
set (see section 3.1). The rest was used as training
data in validation phase. In the preliminary experi-
ments, it was seen that the generally recommended
classifier options yield good enough performance,
although variations of switches could improve per-
formance slightly in certain cases. Classifier op-
tions were selected by a search over the available
option space for only three basic classifier parame-
ters, namely, number of nearest neighbors, distance
metric and feature weighting scheme.
2WordNet::Similarity is a perl package available
freely under GNU General Public Licence. http://wn-
similarity.sourceforge.net.
38
Classifier Senseval-2 Senseval-3
Baseline 0.617 0.627
POS 0.616 0.614
Local context 0.627 0.633
Synt. Pat 0.620 0.612
Concatenated 0.609 0.611
Combined 0.631 0.643
Table 2: Results of baseline, individual, and com-
bined classifiers: recall measures for nouns and
verbs combined.
4 Results
In what follows, we present the results of our ex-
periments in various test cases.3 We combined the
three classifiers and the WORDNET first-sense clas-
sifier through simple majority voting. For evaluating
the systems with SENSEVAL data sets, we mapped
the outputs of our classifiers to WORDNET senses
by picking the most-frequent sense (the one with the
lowest sense number) within each of the class. This
mapping was used in all tests. For all evaluations,
we used SENSEVAL official scorer.
We could use the setting only for nouns and verbs,
because the similarity measures we used were not
defined for adjectives or adverbs, due to the fact that
hypernyms are not defined for these two parts of
speech. So we list the initial results only for nouns
and verbs.
4.1 Individual classifiers vs. combination
We evaluated the results of the individual classifiers
before combination. Only local context classifier
could outperform the baseline in general, although
there is a slight improvement with the syntactic pat-
tern classifier on SENSEVAL-2 data.
The results are given in the table 2, together
with the results of voted combination, and baseline
WORDNET first sense. Classifier shown as ‘con-
catenated’ is a single classifier trained from all of
these feature vectors concatenated to make a sin-
gle vector. Concatenating features this way does not
seem to improve performance. Although exact rea-
sons for this are not clear, this is consistent with pre-
3Note that the experiments and results are reported for SEN-
SEVAL data for comparison purposes, and were not involved in
parameter optimization, which was done with the development
sample.
Senseval-2 Senseval-3
No similarity used 0.608 0.599
Resnik 0.540 0.522
JCn 0.631 0.643
Table 3: Effect of different similarity schemes on
recall, combined results for nouns and verbs
Senseval-2 Senseval-3
SM 0.631 0.643
GW 0.634 0.649
LW 0.641 0.650
Table 4: Improvement of performance with classifier
weighting. Combined results for nouns and verbs
with voting schemes Simple Majority (SM), Global
classifier weights (GW) and local weights (LW).
vious observations (Hoste et al., 2001; Decadt et al.,
2004) that combining classifiers, each using differ-
ent features, can yield good performance.
4.2 Effect of similarity measure
Table 3 shows the effect of JCn and Resnik simi-
larity measures, along with no similarity weighting,
for the combined classifier. It is clear that proper
similarity measure has a major impact on the perfor-
mance, with Resnik measure performing worse than
the baseline.
4.3 Optimizing the voting process
Several voting schemes were tried for combining
classifiers. Simple majority voting improves perfor-
mance over baseline. However, previously reported
results such as (Hoste et al., 2001) and (Decadt et al.,
2004) have shown that optimizing the voting process
helps improve the results. We used a variation of
Weighted Majority Algorithm (Littlestone and War-
muth, 1994). The original algorithm was formulated
for binary classification tasks; however, our use of it
for multi-class case proved to be successful.
We used the held-out development data set for ad-
justing classifier weights. Originally, all classifiers
have the same weight of 1. With each test instance,
the classifier builds the final output considering the
weights. If this output turns out to be wrong, the
classifiers that contributed to the wrong answer get
their weights reduced by some factor. We could ad-
39
Senseval-2 Senseval-3
System 0.777 0.806
Baseline 0.756 0.783
Table 5: Coarse grained results
just the weights locally or globally; In global setting,
the weights were adjusted using a random sample
of held-out data, which contained different words.
These weights were used for classifying all words
in the actual test set. In local setting, each classifier
weight setting was optimized for individual words
that were present in test sets, by picking up random
samples of the same word from SEMCOR .4 Table 4
shows the improvements with each setting.
Coarse grained (at semantic-class level) results
for the same system are shown in table 5. Baseline
figures reported are for the most-frequent class.
4.4 Final results on SENSEVAL data
Here, we list the performance of the system with ad-
jectives and adverbs added for the ease of compar-
ison. Due to the facts mentioned at the beginning
of this section, our system was not applicable for
these parts of speech, and we classified all instances
of these two POS types with their most frequent
sense. We also identified the multi-word phrases
from the test documents. These phrases generally
have a unique sense in WORDNET ; we marked
all of them with their first sense without classify-
ing them. All the multiple-class instances of nouns
and verbs were classified and converted to WORD-
NET senses by the method described above, with lo-
cally optimized classifier voting.
The results of the systems are shown in tables 7
and 8. Our system’s results in both cases are listed
as Simil-Prime, along with the baseline WORD-
NET first sense (including multi-word phrases and
‘U’ answers), and the two best performers’ results
reported.5 These results compare favorably with the
official results reported in both tasks.
4Words for which there were no samples in SEMCOR were
classified using a weight of 1 for all classifiers.
5The differences of the baseline figures from the previously
reported figures are clearly due to different handling of multi-
word phrases, hyphenated words, and unknown words in each
system. We observed by analyzing the answer keys that even
better baseline figures are technically possible, with better tech-
niques to identify these special cases.
Senseval-2 Senseval-3
Micro Average < 0.0001 < 0.0001
Macro Average 0.0073 0.0252
Table 6: One tailed paired t-test significance levels
of results: P(T lessorequalslant t)
System Recall
SMUaw (Mihalcea, 2002) 0.690
Simil-Prime 0.664
Baseline (WORDNET first sense) 0.648
CNTS-Antwerp (Hoste et al., 2001) 0.636
Table 7: Results for SENSEVAL-2 English all words
data for all parts of speech and fine grained scoring.
Significance of results To verify the significance
of these results, we used one-tailed paired t-test, us-
ing results of baseline WORDNET first sense and
our system as pairs. Tests were done both at micro-
average level and macro-average level, (considering
test data set as a whole and considering per-word av-
erage). Null hypothesis was that there is no signif-
icant improvement over the baseline. Both settings
yield good significance levels, as shown in table 6.
5 Conclusion and Future Work
We analyzed the problem of Knowledge Acquisition
Bottleneck in WSD, proposed using a general set of
semantic classes as a trade-off, and discussed why
such a system is promising. Our formulation al-
lowed us to use training examples from words dif-
ferent from the actual word being classified. This
makes the available labelled data reusable for differ-
ent words, relieving the above problem. In order to
facilitate learning, we introduced a technique based
on word sense similarity.
The generic classes we learned can be mapped to
System Recall
Simil-Prime 0.661
GAMBL-AW-S (Decadt et al., 2004) 0.652
SenseLearner (Mihalcea and Faruque, 2004) 0.646
Baseline (WORDNET first sense) 0.642
Table 8: Results for SENSEVAL-3 English all words
data for all parts of speech and fine grained scoring.
40
finer grained senses with simple heuristics. Through
empirical findings, we showed that our system can
attain state of the art performance, when applied to
standard fine-grained WSD evaluation tasks.
In the future, we hope to improve on these results:
Instead of using WORDNET unique beginners, using
more natural semantic classes based on word usage
would possibly improve the accuracy, and finding
such classes would be a worthwhile area of research.
As seen from our results, selecting correct similarity
measure has an impact on the final outcome. We
hope to work on similarity measures that are more
applicable in our task.
6 Acknowledgements
Authors wish to thank the three anonymous review-
ers for their helpful suggestions and comments.
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