Word Sense Disambiguation 
using Conceptual Density 
Eneko Agirre* 
Lengoaia eta Sistema Informatikoak saila. Euskal Herriko Universitatea. 
p.k. 649, 200800 Donostia. Spain. jibagbee@si.heu.es 
German Rigau** 
Departament de Llenguatges i Sistemes Informhtics. Universitat Polit~cnica de Catalunya. 
Pau Gargallo 5, 08028 Barcelona. Spain. g.rigau@lsi.upc.es 
Abstract. 
This paper presents a method for the resolution 
of lexical ambiguity of nouns and its automatic 
evaluation over the Brown Corpus. The method 
relies on the use oil' the wide-coverage noun 
taxonomy of WordNet and the notion of 
conceptual distance among concepts, captured by 
a Conceptual Density formula developed for this 
purpose. This fully automatic method requires 
no hand coding of lexical entries, hand tagging 
of text nor any kind of training process. The 
results of the experiments have been 
automatically evaluated against SemCor, the 
sense-tagged version of the Brown Corpus. 
1 Introduction 
Much of recent work in lexical ambiguity 
resolution offers the prospect that a disambiguation 
system might be able to receive as input unrestricted 
text and tag each word with the most likely sense 
with fairly reasonable accuracy and efficiency. The 
most extended approach use the context of the word to 
be disambiguatcd together with inl'ormation about 
each of its word senses to solve this problem. 
Interesting experiments have been performed in 
recent years using preexisting lexical knowledge 
resources: \[Cowie el al. 92\], \[Wilks et al. 93\] with 
LDOCE, \[Yarowsky 92\] with Roget's International 
Thesaurus, and \[Sussna 93\], \[Voorhees 9311, 
\[Richardson et al. 94\], \[Resnik 95\] with WordNet. 
Although each of these techniques looks promising 
for disambiguation, either they have been only 
applied to a small number of words, a few sentences 
or not in a public domain corpus. For this reason we 
have tried to disambiguate all the nouns from real 
*Eneko Agirre was supported by a grant from the Basque 
Goverment. Part of this work is included in projects 
141226-TA248/95 of the Basque Country University and 
PI95-054 of the Basque Government. 
**German Rigau was supported by a grant from the 
Ministerio de Educaci6n y Ciencia. 
texts in the public domain sense tagged version of the 
Brown corpus \[Francis & Kucera 67\], \[Miller et al. 
93\], also called Semantic Concordance or SemCor for 
short 1, The words in SemCor are tagged with word 
senses from WordNet, a broad semantic taxonomy for 
English \[Miller 90\] 2. Thus, SemCor provides an 
appropriate environment for testing our procedures 
and comparing among alternatives in a fully 
automatic way. 
The automatic decision procedure for lexical 
ambiguity resolution presented in this paper is based 
on an elaboration of the conceptual distance among 
concepts: Conceptual Density \[Agirre & Rigau 95\]. 
Thc system needs to know how words are clustered in 
semantic classes, and how semantic classes are 
hierarchically organised. For this purpose, we have 
used WordNet. Our system tries to resolve the lexical 
ambiguity ot' nouns by finding the combination of 
senses from a set of contiguous nouns that 
maximises the Conceptual Density among senses. 
The perlbrmance of the procedure was tested on four 
SemCor texts chosen at random. For comparison 
purposes two other approaches, \[Sussna 93\] and 
\[Yarowsky 92\], were also tried. The results show that 
our algorithm performs better on the test set. 
Following this short introduction the Conceptual 
Dcnsity formula is presented. The main procedure to 
resolve lexical ambiguity of nouns using Conceptual 
Density is sketched on section 3. Section 4 descri'bes 
extensively the experiments and its results. Finally, 
sections 5 and 6 deal with further work and 
conclusions. 
1Semcor comprises approximately 250,000 words. Tile 
tagging was done manually, and the error rate measured 
by the authors is around 10% for polysemous words. 
2The senses of a word are represented by synonym sets 
(or synscts), one for each word sense. The nominal part 
of WordNct can be viewed as a tangled hierarchy of 
hypo/hypernymy relations among synsets. Nominal 
relations include also three kinds of meronymic 
relations, which can be paraphrased as member-of, made- 
of and component-part-of. The version used in this work 
is WordNet 1.4, The coverage in WordNet of senses lot 
open-class words in SemCor reaches 96% according to 
the authors. 
3_(5 
2 Conceptual Density and Word 
Sense Disambiguation 
Conceptual distance tries to provide a basis for 
measuring closeness in meaning among words, taking 
as reference a structured hierarchical net. Conceptual 
distance between two concepts is defined in IRada et 
al. 89\] as the length of the shortest path that connects 
the concepts in a hierarchical semantic net. In a 
similar approach, \[Sussna 931 employs the notion of 
conceptual distance between network nodes in order to 
improve precision during document indexing. \[Resnik 
95\] captures semantic similarfly (closely related to 
conceptual distance) by means of the information 
content of the concepts in a hierarchical net. In 
general these alw;oaches focus on nouns. 
The measure ()1' conceptual distance among concepts 
we are looking for should be scnsflive Io: 
• the length of the shortest palh that connects lhe 
concepts involved. 
• the depth in the hierarchy: concepts in a deeper 
part of the hierarchy should be ranked closer. 
• the density of concepts in the hierarchy: concepts 
in a dense part of the hierarchy are relatively closer 
than those in a more sparse region. 
- tile measure should be independent of the lltllllber 
o1' concepts we are measuring. 
We have experimented willl several fornmlas that 
follow the four criteria presented above. The 
experiments reported here were pcrformcd using the 
Conceptual Density formuhl \[Agirre & Rigau 95\], 
which compares areas of subhierarchies. 
To illustrate how Conceptual 1)ensity can help to 
disambiguate a word, in figure I lhe word W has four 
senses and several context words. Each sense of the 
words belongs to a subhierarchy of WordNct. Tile dots 
in the subhierarchies represent the senses of eilhcr the 
word to be disambiguated (W) or the words in the 
context. Conceptual Density will yield the highest 
density for lhe subhierarchy containing more senses of 
lhose, rehttive to the total amount of senses in the 
subhierarchy. Tim sense o1' W contained in the 
subhierarchy with highest Conceptual l)ensity will be 
chosen as the sense disambiguating W in the given 
context. In figure 1, sense2 would be chosen. 
W 
W0~d to be disarlJ0iguated: W 
Context words: wl w2 w3 w4 ... 
Figure 1: senses of a word in WordNet 
Given a concept c, at the top of a sulfifierarchy, and 
given nhyp (mean number of hyponyms per node), 
the Conceptual Density for c when its subhierarchy 
contains a number m (nmrks) of senses of the words 
to disambiguate is given by the \[ormula below: 
m- I Z .0 20 
nh37~ 
CI)(c, m)- ,::0 descendants,, (1) 
l;ornlula I shows a lmralneter that was COlnputed 
experimentally. The 0.20 tries to smooth the 
exponential i, as m ranges between I and tim total 
number of senses in WordNet. Several values were 
Ified for the parameter, and it was found that the best 
lmrl'ormanee was attained consistently when the 
parameter was near 0.20. 
3 The Disambiguation Algorithm 
Using Conceptual Density 
Given a window size, the program moves the 
window one noun at a time from the beginning of the 
document towards its end, disambiguating in each 
step the noun in the middle of the window and 
considering the other nouns in the window as contexl. 
Non-noun words are ,lot taken into account. 
The algorilhm Io disambiguate a given noun w in 
tile middle of a window o1' nouns W (c.f. figure 2) 
roughly proceeds its folk)ws: 
Step \].) 
Step 2) 
Step 3) 
Step 4) 
Step 5) 
t:r:ee :-: compute tree(words in window) 
loop 
tree ::: compute conc(~ptua\] distanco(tree) 
concept -= se\].occt concept with llighest-._weigth(tree) 
J.f concept :: null. t:hen exJ_tloop 
tree := inark d:\[sambigui.tted senses (tree,concept) 
end\] oop 
output disambJguatJ.on ~esu\].t (tree) 
Figure 2: algori(hm for each window 
17 
First, the algorithm represents in a lattice the nouns 
present in the window, their senses and hypernyms 
(step 1). Then, the program computes the Conceptual 
Density of each concept in WordNet according to the 
senses it contains in its subhierarchy (step 2). It 
selects the concept c with highest Conceptual Density 
(step 3) and selects the senses below it as the correct 
senses for the respective words (step 4). 
The algorithm proceeds then to compute the density 
for the remaining senses in the lattice, and continues 
to disambiguate the nouns left in W (back to steps 2, 
3 and 4). When no further disambiguation is possible, 
the senses left for w are processed and the result is 
presented (step 5). 
Besides completely disambiguating a word or 
failing to do so, in some cases the disambiguation 
algorithm returns several possible senses for a word. 
In the experiments we considered these partial 
outcomes as failure to disambiguate. 
4 The Experiments 
4.1 The texts 
We selected four texts from SemCor at random: br- 
a01 (where a stands for gender "Press: Reportage"), 
br-b20 (b for "Press: Editorial"), br-j09 (j means 
"Learned: Science") and br-r05 (r for "Humour"). 
Table 1 shows some statistics for each text. 
text words nouns nouns monosemous 
in WN 
br-a01 2079 564 464 149 (32%) 
br-ab20 2153 453 377 128 (34%) 
br-.i09 2495 620 586 205 (34%) 
br-r05 2407 457 431 120 (27%) 
total 9134 2094 1858 602 (32%) 
Table 1 : data for each text 
An average of 11% of all nouns in these four texts 
were not found in WordNet. According to this data, 
the amount of monosemous nouns in these texts is 
bigger (32% average) than the one calculated for the 
open-class words fi'om the whole SemCor (27.2% 
according to \[Miller et al. 94\]). 
For our experiments, these texts play both the rol'e 
of input files (without semantic tags) and (tagged) test 
files. When they are treated as input files, we throw 
away all non-noun words, only leaving the lemmas of 
the nouns present in WordNet. 
4.2 Results and evaluation 
One of the goals of the experiments was to decide 
among different variants of the Conceptual Density 
formula. Results are given averaging the results of the 
four files. Partial disambiguation is treated as failure 
to disambiguate. Precision (that is, the percentage of 
actual answers which were correct) and recall (that is, 
the percentage of possible answers which were correct) 
are given in terms of polysemous nouns only. Graphs 
are drawn against the size of the context 3 . 
• meronymy does not improve 
performance as expected. A priori, the more 
relations are taken in account (e.i. meronymic 
relations, in addition to the hypo/hypernymy relation) 
the better density would capture semantic relatedness, 
and therefore better results can be expected. 
44 
~I~A 43 
v 42 
O -4 41 
40 
39 % meron 
---o--- hyper 
38 I I i I 
Window Size 
Figure 3: meronymy and hyperonymy 
The experiments (see figure 3) showed that there is 
not much difference; adding meronymic information 
does not improve precision, and raises coverage only 
3% (approximately). Nevertheless, in the rest of the 
results reported below, meronymy and hypernymy 
were used. 
• global nhyp is as good as local nhyp. 
The average number of hypouyms or nhyp (c.f. 
formula 1) can be approximated in two ways. If an 
independent nhyp is computed for every concept in 
WordNet we call it local nhyp. If instead, a unique 
nhyp is computed using the whole hierarchy, we have 
global nhyp. 
44 
43 
A 
42 
.o 41 - 
¢~ 40- 
39 
38 
local 
I I i I o ~, o ,~ ,9, 
Window Size 
Figure 4: local nhyp vs. global nhyp 
3context size is given in terms of nouns. 
18 
While local nhyp is the actual average for a given 
concept, global nhyp gives only an estimation. The 
results (c.f. figure 4) show that local nhyp performs 
only slightly better. Therefore global nhyp is 
favoured and was used in subsequent experiments. 
• context size: different behavionr for 
each text. One could assume that the more context 
lhere is, the better the disambiguation results would 
be. Our experiments show that each file from 
SemCor has a different behaviour (c.f. figure 5) while 
br-b20 shows clear improvement for bigger window 
sizes, br-r05 gets a local maximum at a 10 size 
window, etc. 
50 
45 
v 
.o 4o 
ID 
I:1, 
35 
30 
--t3--- br-a01 + br-b20 
+ br-r05 ----o---- br-j09 
I I o ~, g 
-- average 
I I 
Window Size 
Figure 5: context size and different filcs 
As each text is structured a list of sentences, 
lacking any indication of headings, sections, 
paragraph endings, text changes, etc. the program 
gathers the context without knowing whether the 
nouns actually occur in coherent pieces of text. This 
could account for the fact that in br-r05, composed 
mainly by short pieces of dialogues, the best results 
are for window size 10, the average size of this 
dialogue pieces. Likewise, the results for br-a01, 
which contains short journalistic texts, are hest for 
window sizes from 15 to 25, decreasing significatly 
for size 30. 
Ill addition, the actual nature of each text is for sure 
an impommt factor, difficult to measure, which could 
account for the different behawfiur on its own. In 
order to give an overall view of the performance, we 
consider the average hehaviour. 
• file vs. sense. WordNct groups noun senses 
in 24 lexicographer's files. The algorithm assigns a 
noun both an specific sense and a file label. Both file 
matches and sense matches are interesting to count. 
Whilc the sense level gives a fine graded measure of 
the algorithm, the file level gives an indication of the 
perl'ormance if we were interested in a less sharp level 
of disambiguation. The granularity of the sense 
distinctions made in \[Hearst, 91\], \[Yarowsky 92\] and 
\[Gale et al. 93\] also called homographs in \[Guthrie et 
al. 931\], can be compared to that of the file level in 
WordNct. 
For instance, in \[Yarowsky 92\] two homographs of 
tile noun }liNg are considered, one characterised as 
MUSIC and the other as ANIMAL, INSECT. In 
WordNet, the 6 senses of I~t~s related to music appear 
in the following files: ARTIFACT, ATTRIBUTE, 
COMMUNICATION and PERSON. The 3 senses 
related to animals appear in the files ANIMAL and 
FOOD. This mcans that while the homograph level 
in \[Yarowsky 92\] distinguishes two sets of senses, 
the file level in WordNet distinguishes six sets of 
senses, still finer in granularity. 
Figure 6 shows that, as expected, file-level matches 
attain better performance (71.2% overall and 53.9% 
for polysemic nouns) than sense-level matches. 
55 
-£ 
.o 45 
ID 
40 
35 
---0-- Sense 
I I" - I 'I 
Window Size 
Figure 6: sense level vs. file level 
• evaluation of the results Figure 7 shows 
that, overall, coverage over polyscmous nonns 
increases significantly with the window size, without 
losing precision. Coverage tends to get stabilised near 
80%, getting little improvement for window sizes 
bigger than 20. 
The figure also shows the guessing baseline, 
given hy selecting senses at random. This baseline 
was first calculated analytically and later checked 
experimentally. We also compare the performance of 
our algorithm with that of the "most frequent" 
heuristic. The frequency counts for each sense were 
collected using the rest of SemCor, and then applied 
to the \['our texts. While the precision is similar to 
that of our algorithm, the coverage is 8% worse. 
3_9 
80- 
70 
6O - 
50- 
40- 
Coverage: ~ semantic density 
..... most frequent 
Precision: ----0--- semantic density 
..... most frequent 
guessing 
3o --T \[ T 1 
Window Size 
Figure 7: precision and coverage 
All the data for the best window size can be seen in 
table 2. The precision and coverage shown in all the 
preceding graphs were relative to the polysemous 
nouns only. Including monosemic nouns precision 
raises, as shown in table 2, from 43% to 64.5%, and 
the coverage increases from 79.6% to 86.2%. 
% w:30 II Cove,-. I Prec \[Recall 
overall File 86.2 71.2 61.4 
Sense 64.5 55.5 
polysemic File 79.6 53.9 42.8 
Sense 43 34.2 
Table 2: overall data for the best window size 
4.3 Comparison with other works 
The raw results presented here seem to be poor 
when compared to those shown in \[Hearst 91\], \[Gale 
et al. 93\] and \[Yarowsky 9211. We think that several 
factors make the comparison difficult. Most of those 
works focus in a selected set of a few words, generally 
with a couple of senses of very different meaning 
(coarse-grained distinctions), and for which their 
algorithm could gather enough evidence. On the 
contrary, we tested our method with all the nouns in 
a subset of an unfestricted public domain corpus 
(more than 9.000 words), making fine-grained 
distinctions among all the senses in WordNct. 
An approach that uses hierarchical knowledge is 
that of \[Resnik 9511, which additionally uses the 
information content of each concept gathered from 
corpora. Unfortunately he applies his method on a 
different task, that of disambiguating sets of related 
nouns. The evaluation is done on a set of related 
nouns from Roger's Thesaurus tagged by hand. The 
fact that some senses were discarded because the 
human judged them not reliable makes comparison 
even more difficult. 
In order to compare our approach we decided to 
implement \[Yarowsky 92\] and \[Sussna 93\], and test 
them on our texts. For \[Yarowsky 92\] we had to 
adapt it to work with WordNet. His method relies on 
cooccurrence data gathered on Roget's Thesaurus 
semantic categories. Instead, on our experiment we 
use saliency values 4 based on the lexicographic file 
tags in SemCor. The results for a window size of 50 
nouns are those shown in table 35. Tile precision 
attained by our algorithm is higher. To compare 
figures better consider the results in table 4, were the 
coverage of our algorithm was easily extended using 
the version presented below, increasing recall to 
70.1%. \[+ 
ii+°v+ i+c ,=11 I C.Density 86.2 71.2 J 61.4 
Yarowsky 100.0 64.0 1 64.0 
Table 3: comparison with \[Yarowsky 9211 
From the methods based on Conceptual Distance, 
\[Sussna 9311 is the most similar to ours. Sussna 
disambiguates several documents from a public 
corpus using WordNet. The test set was tagged by 
hand, allowing more than one correct senses for a 
single word. The method he uses has to overcome a 
combinatorial explosion 6 controlling the size of the 
window and "freezing" the senses for all the nouns 
preceding the noun to be disambiguated. In order to 
fi'eeze the winning sense Sussna's algorithm is forced 
to make a unique choice. When Conceptual Distance 
is not able to choose a single sense, the algorithm 
chooses one at random. 
Conceptual Density overcomes the combinatorial 
explosion extending the notion of conceptual distance 
from a pair of words to n words, and therefore can 
yield more than one correct sense for a word. For 
comparison, we altered our algorithm to also make 
random choices when unable to choose a single sense. 
We applied the algorithm Sussna considers best, 
4We tried both mutual information and association ratio, 
and the later performed better. 
5The results of our algorithm are those for window size 
30, file matches and overall. 
6In our replication of his experiment the mutual 
constraint for the first 10 nouns (tile optimal window 
size according to his experiments) of file br-r05 had to 
deal with more than 200,000 synset pairs. 
20 
discarding the factors that do not affect performance 
significantly 7, and obtain the results in table 4. 
% Cover. \[ Prec. 
C.l)ensity File I00.0 70.1 
Sense 6(1.1 
Sussna File 100.0 64.5 
Sense 52.3 
Table 4: comparison with \[St, ssna 931 
A more thorougla comparison with these methods 
could he desirable, hut not possible in this paper l'or 
the sake of conciseness. 
might be only one of a number of complementary 
evidences of the plausibility ol'a certain word sense. 
Furthermore, WordNet 1.4 is not a complete lexical 
database (current version is 1.5). 
• Tune the sense distinctions to the level 
best suited for the application. On the one 
hand the sense distinctions made by WordNet 1.4 arc 
not always satisl'actory. On tire other hand, our 
algorithm is not designed to work on the file level, 
e.g. il' the sense level is unable to distinguish among 
two senses, the file level also fails, even if both 
senses were fronl the same file. If the senses were 
collapsed at the file level, the coverage and precision 
of tile algorithm at the file level might be even better. 
5 Further Work 
We would like to have included in this paper a 
study on whether there is or not a correlation among 
correct and erroneous sense assignations and the 
degree of Conceptual Density, that is, the actual 
figure held by fommla I. If this was the case, the 
error rate could be furtber decreased setting a ccrtain 
lhreshold for Conceptual Density wdues of wilming 
senses. We would also like to evaluate the usel'ulness 
of partia~l disambiguation: decrease of ambiguity, 
number of times correct sense is among the chosen 
ones, etc. 
There are some factors that could raise the 
performmace of our algorithm: 
• Work on coherent chunks of text. 
Unfortunately any information about discourse 
structure is absent in SemCor, apart from sentence 
endings Thc performance would gain from the fact 
lhat sentences from unrelated topics wouht not be 
considered in the disamhiguation window. 
• Extend and improve the semantic data. 
WordNet provides sinonymy, hypernymy and 
meronyny relations for nouns, but other relations are 
missing. For instance, WordNet lacks eross-categorial 
semantic relations, which could he very useful to 
extend the notion of Conceptual Density of nouns to 
Conceptual Density of words. Apart from extending 
lhe disambiguation to verbs, adjectives and adverbs, 
cross-catcgorial relations would allow to capture better 
lhe relations alnong senses and provide firmer grounds 
for disambiguating. 
These other relations could be extracted from other 
knowledge sources, both corpus-based or MRD-based. 
If those relations could be given on WordNet senses, 
Conceptual Density could profit from them. It is ot, r 
belief, following the ideas of \[McRoy 92\] that full- 
fledged lexical ambiguity resolution should combine 
several information sources. Conceptual Density 
"/Initial mutual constraint size is 10 and window size'is 
41. Meronymic links are also considered. All the links 
have the same weigth. 
6 Conclusion 
The automatic method for the disambiguation of 
nouns presented in this papcr is ready-usable in any 
general domain and on free-running text, given part of 
speech tags. It does not need any training and uses 
word sense tags from WordNet, an extensively used 
Icxieal data base. 
Conceptual Density has been used for other tasks 
apart from the disambiguation of free-running test. Its 
application for automatic spelling correction is 
outlined in tAgirre ct al. 94\]. It was also used on 
Computational Lexicography, enriching dictionary 
senses with semantic tags extracted from WordNet 
\[Rigau 9411, or linking bilingual dictionaries to 
WordNet \[Rigau and Agirre 96\]. 
In the experiments, the algorithm disambiguated 
\['our texts (about 10,000 words long) of SemCor, a 
subset of the Brown corpus. The results were obtained 
automatically comparing the tags in SemCor with 
those computed by the algorithm, which would allow 
the comparison with other disambiguation methods. 
Two other methods, \[Sussna 93\] and \[Yarowsky 92\], 
were also tried on the same texts, showing that our 
algorithm performs better. 
Results are promising, considering the difficnlty of 
the task (free running text, large number of senses per 
word in WordNet), and the htck o1' any discourse 
structure of the texts. Two types el' results can be 
obtaincd: the specific scnse or a coarser, file level, 
tag. 
Acknowledgements 
This work, partially described ill \[Agirre &Rigau 9611, 
was started in the Computing Research Laboratory in 
New Mexico State University. We wish to thank all the 
staff of the CRL and specially Jim Cowie, Joe Guthtrie, 
Louise Guthrie and David l"arwell. We woukl also like to 
thank Xabier Arregi, Jose mari Arriola, Xabier Artola, 
Arantza Dfaz de llarraza, Kepa Sarasola and Aitor Soroa 
fiom the Computer Science Faculty of EHU and Franeesc 
Ribas, ltoracio Rodrfguez and Alicia Ageno from the 
Computer Science Department of UPC. 
22 
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