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Indexing with WordNet synsets can improve text retrieval 
Julio Gonzalo and Felisa Verdejo and Irina Chugur and Juan Cigarr~in 
UNED 
Ciudad Universitaria, s.n. 
28040 Madrid - Spain 
{julio, felisa, irina, juanci}@ieec, uned. es 
Abstract 
The classical, vector space model for text retrieval 
is shown to give better results (up to 29% better in 
our experiments) if WordNet synsets are chosen as 
the indexing space, instead of word forms. This re- 
sult is obtained for a manually disambiguated test 
collection (of queries and documents) derived from 
the SEMCOR semantic concordance. The sensitiv- 
ity of retrieval performance to (automatic) disam- 
biguation errors when indexing documents is also 
measured. Finally, it is observed that if queries are 
not disambiguated, indexing by synsets performs (at 
best) only as good as standard word indexing. 
1 Introduction 
Text retrieval deals with the problem of finding all 
the relevant documents in a text collection for a 
given user's query. A large-scale semantic database 
such as WordNet (Miller, 1990) seems to have a great 
potential for this task. There are, at least, two ob- 
vious reasons: 
• It offers the possibility to discriminate word 
senses in documents and queries. This would 
prevent matching spring in its "metal device" 
sense with documents mentioning spring in the 
sense of springtime. And then retrieval accu- 
racy could be improved. 
• WordNet provides the chance of matching se- 
mantically related words. For instance, spring, 
fountain, outflow, outpouring, in the appropri- 
ate senses, can be identified as occurrences of 
the same concept, 'natural flow of ground wa- 
ter'. And beyond synonymy, WordNet can be 
used to measure semantic distance between oc- 
curring terms to get more sophisticated ways of 
comparing documents and queries. 
However, the general feeling within the informa- 
tion retrieval community is that dealing explicitly 
with semantic information does not improve signif- 
icantly the performance of text retrieval systems. 
This impression is founded on the results of some 
experiments measuring the role of Word Sense Dis- 
ambiguation (WSD) for text retrieval, on one hand, 
and some attempts to exploit the features of Word- 
Net and other lexical databases, on the other hand. 
In (Sanderson, 1994), word sense ambiguity is 
shown to produce only minor effects on retrieval ac- 
curacy, apparently confirming that query/document 
matching strategies already perform an implicit dis- 
ambiguation. Sanderson also estimates that if ex- 
plicit WSD is performed with less than 90% accu- 
racy, the results are worse than non disambiguating 
at all. In his experimental setup, ambiguity is in- 
troduced artificially in the documents, substituting 
randomly chosen pairs of words (for instance, ba- 
nana and kalashnikov) with artificially ambiguous 
terms (banana/kalashnikov). While his results are 
very interesting, it remains unclear, in our opinion, 
whether they would be corroborated with real oc- 
currences of ambiguous words. There is also other 
minor weakness in Sanderson's experiments. When 
he ~disambiguates" a term such as spring/bank to 
get, for instance, bank, he has done only a partial 
disambiguation, as bank can be used in more than 
one sense in the text collection. 
Besides disambiguation, many attempts have been 
done to exploit WordNet for text retrieval purposes. 
Mainly two aspects have been addressed: the enrich- 
ment of queries with semantically-related terms, on 
one hand, and the comparison of queries and doc- 
uments via conceptual distance measures, on the 
other. 
Query expansion with WordNet has shown to be 
potentially relevant to enhance recall, as it permits 
matching relevant documents that could not contain 
any of the query terms (Smeaton et al., 1995). How- 
ever, it has produced few successful experiments. 
For instance, (Voorhees, 1994) manually expanded 
50 queries over a TREC-1 collection (Harman, 1993) 
using synonymy and other semantic relations from 
WordNet 1.3. Voorhees found that the expansion 
was useful with short, incomplete queries, and rather 
useless for complete topic statements -where other 
expansion techniques worked better-. For short 
queries, it remained the problem of selecting the ex- 
pansions automatically; doing it badly could degrade 
retrieval performance rather than enhancing it. In 
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(Richardson and Smeaton, 1995), a combination of 
rather sophisticated techniques based on WordNet, 
including automatic disambiguation and measures of 
semantic relatedness between query/document con- 
cepts resulted in a drop of effectiveness. Unfortu- 
nately, the effects of WSD errors could not be dis- 
cerned from the accuracy of the retrieval strategy. 
However, in (Smeaton and Quigley, 1996), retrieval 
on a small collection of image captions - that is, on 
very short documents - is reasonably improved us- 
ing measures of conceptual distance between words 
based on WordNet 1.4. Previously, captions and 
queries had been manually disambiguated against 
WordNet. The reason for such success is that with 
very short documents (e.g. boys playing in the sand) 
the chance of finding the original terms of the query 
(e.g. of children running on a beach) are much lower 
than for average-size documents (that typically in- 
elude many phrasings for the same concepts). These 
results are in agreement with (Voorhees, 1994), but 
it remains the question of whether the conceptual 
distance matching would scale up to longer docu- 
ments and queries. In addition, the experiments in 
. (Smeaton and Quigley, 1996) only consider nouns, 
while WordNet offers the chance to use all open-class 
words (nouns, verbs, adjectives and adverbs). 
Our essential retrieval strategy in the experiments 
reported here is to adapt a classical vector model 
based system, using WordNet synsets as indexing 
space instead of word forms. This approach com- 
bines two benefits for retrieval: one, that terms axe 
fully disambiguated (this should improve precision); 
and two, that equivalent terms can be identified (this 
should improve recall). Note that query expansion 
does not satisfy the first condition, as the terms used 
to expand are words and, therefore, are in turn am- 
biguous. On the other hand, plain word sense dis- 
ambiguation does not satisfy the second condition. 
as equivalent senses of two different words are not 
matched. Thus, indexing by synsets gets maximum 
matching and minimum spurious matching, seeming 
a good starting point to study text retrieval with 
WordNet. 
Given this approach, our goal is to test two 
main issues which are not clearly answered -to our 
knowledge- by the experiments mentioned above: 
• Abstracting from the problem of sense disam- 
biguation, what potential does WordNet offer 
for text retrieval? In particular, we would like 
to extend experiments with manually disam- 
biguated queries and documents to average-size 
texts. 
• Once the potential of WordNet is known for a 
manually disambiguated collection, we want to 
test the sensitivity of retrieval performance to 
disambiguation errors introduced by automatic 
WSD. 
This paper reports on our first results answering 
these questions. The next section describes the test 
collection that we have produced. The experiments 
are described in Section 3, and the last Section dis- 
cusses the results obtained. 
2 The test collection 
The best-known publicly available corpus hand- 
tagged with WordNet senses is SEMCOR (Miller et 
al., 1993), a subset of the Brown Corpus of about 
100 documents that occupies about 11 Mb. (in- 
cluding tags) The collection is rather heterogeneous, 
covering politics, sports, music, cinema, philosophy, 
excerpts from fiction novels, scientific texts... A 
new, bigger version has been made available recently 
(Landes et al., 1998), but we have not still adapted 
it for our collection. 
We have adapted SEMCOR in order to build a test 
collection -that we call IR-SEMCOR- in four manual 
steps: 
• We have split the documents to get coherent 
chunks of text for retrieval. We have obtained 
171 fragments that constitute our text collec- 
tion, with an averagv length of 1331 words per 
fragment. 
• We have extended the original TOPIC tags of 
the Brown Corpus with a hierarchy of subtags, 
assigning a set of tags to each text in our col- 
lection. This is not used in the experiments 
reported here. 
• We have written a summary for each of the frag- 
ments, with lengths varying between 4 and 50 
words and an average of 22 words per summary. 
Each summary is a human explanation of the 
text contents, not a mere bag of related key- 
words. These summaries serve as queries on 
the text collection, and then there is exactly 
one relevant document per query. 
• Finally, we have hand-tagged each of the 
summaries with WordNet 1.5 senses. When 
a word or term was not present in the 
database, it was left unchanged. In general, 
such terms correspond to groups (vg. Ful- 
ton_County_Grand-Jury), persons (Cervantes) 
or locations (Fulton). 
We also generated a list Of "stop-senses" and a list 
of "stop-synsets', automatically translating a stan- 
dard list of stop words for English. 
Such a test collection offers the chance to measure 
the adequacy of WordNet-based approaches to IR in- 
dependently from the disambiguator being used, but 
also offers the chance to measure the role of auto- 
matic disambiguation by introducing different rates 
39 
! 
! 
Experiment 07o correct document retrieved in first place 
62.0 Indexing by synsets 
Indexing by word senses 53.2 
Indexing by words (basic SMART) 48.0 
Indexing by synsets with a 5% errors ratio 62.0 
Id. with 10% errors ratio 60.8 
Id. with 20% errors ratio 56.1 
Id. with 30% errors ratio 54.4 
Indexing with all possible synsets (no disambiguation) 52.6 
Id. with 60% errors ratio 49.1 
Synset indexing with non-disambiguated queries 48.5 
Word-Sense indexing with non-disambiguated queries 40.9 
Table 1: Percentage of correct documents retrieved in first place 
of "disambignation errors" in the collection. The 
only disadvantage is the small size of the collection, 
which does not allow fine-grained distinctions in the 
results. However, it has proved large enough to give 
meaningful statistics for the experiments reported 
here. 
Although designed for our concrete text retrieval 
testing purposes, the resulting database could also 
be useful for many other tasks. For instance, it could 
be used to evaluate automatic summarization sys- 
tems (measuring the semantic relation between the 
manually written and hand-tagged summaries of IR- 
SEMCOR and the output of text summarization sys- 
tems) and other related tasks. 
3 The experiments 
We have performed a number of experiments using a 
standard vector-model based text retrieval system, 
SMART (Salton, 1971), and three different indexing 
spaces: the original terms in the documents (for 
standard SMART runs), the word-senses correspond- 
ing to the document terms (in other words, a man- 
ually disambiguated version of the documents) and 
the WordNet synsets corresponding to the document 
terms (roughly equivalent to concepts occurring in 
the documents). 
These are all the experiments considered here: 
1. The original texts as documents and the sum- 
maries as queries. This is a classic SMART run, 
with the peculiarity that there is only one rele- 
vant document per query. 
2. Both documents (texts) and queries (sum- 
maries) are indexed in terms of word-senses. 
That means that we disambiguate manually all 
terms. For instance "debate" might be substi- 
tuted with "debate~l:10:01:?'. The three num- 
bers denote the part of speech, the WordNet 
lexicographer's file and the sense number within 
the file. In this case, it is a noun belonging to 
the noun.communication file. 
With this collection we can see if plain disam- 
biguation is helpful for retrieval, because word 
senses are distinguished but synonymous word 
senses are not identified. 
3. In the previous collection, we substitute each 
word sense for a unique identifier of its associ- 
ated synset. For instance, "debate~l:lO:01:." 
is substituted with "n04616654", which is an 
identifier for 
"{argument, debate1}" (a discussion in which 
reasons are advanced for and against some 
proposition or proposal; "the argument over 
foreign aid goes on and on') 
This collection represents conceptual indexing, 
as equivalent word senses are represented with 
a unique identifier. 
4. We produced different versions of the synset 
indexed collection, introducing fixed percent- 
ages of erroneous synsets. Thus we simulated 
a word-sense disambiguation process with 5%, 
10%, 20%, 30% and 60% error rates. The er- 
rors were introduced randomly in the ambigu- 
ous words of each document. With this set of 
experiments we can measure the sensitivity of 
the retrieval process to disambiguation errors. 
5. To complement the previous experiment, we 
also prepared collections indexed with all pos- 
sible meanings (in their word sense and synset 
versions) for each term. This represents a lower 
bound for automatic disambiguation: we should 
not disambiguate if performance is worse than 
considering all possible senses for every word 
form. 
6. We produced also a non-disambiguated version 
of the queries (again, both in its word sense and 
40 
Figure 1: Different indexing approaches 
c 0 
u .= 
o. 
0.8 
0.6 
0.4 
0.2 
0 1 
0.3 0.4 
1. Indexing by synsets o 
2. Indexing by word senses -+--- 
3. Indexing by words (SMART) -o-- 
1 
2 ~ 
~-.... 
I I I I ! 
0.5 0.6 0.7 0.8 0.9 
Recall 
synset variants). This set of queries was run 
against the manually disambiguated collection. 
In all cases, we compared arc and ann standard 
weighting schemes, and they produced very similar 
results. Thus we only report here on the results for 
nnn weighting scheme. 
4 Discussion of results 
4.1 Indexing approach 
In Figure 1 we compare different indexing ap- 
proaches: indexing by synsets, indexing by words 
(basic SMART) and indexing by word senses (ex- 
periments 1, 2 and 3). The leftmost point in each 
curve represents the percentage of documents that 
were successfully ranked as the most relevant for its 
summary/query. The next point represents the doc- 
uments retrieved as the first or the second most rel- 
evant to its summary/query, and so on. Note that, 
as there is only one relevant document per query, 
the leftmost point is the most representative of each 
curve. Therefore, we have included this results sep- 
arately in Table 1. 
The results are encouraging: 
• Indexing by WordNet synsets produces a 
remarkable improvement on our test collection. 
A 62% of the documents are retrieved in first 
place by its summary, against 48% of the ba- 
sic SMART run. This represents 14% more 
documents, a 29% improvement with respect 
to SMART. This is an excellent result, al- 
though we should keep in mind that is obtained 
with manually disambiguated queries and doc- 
uments. Nevertheless, it shows that WordNet 
can greatly enhance text retrieval: the problem 
resides in achieving accurate automatic Word 
Sense Disambiguation. 
• Indexing by word senses improves perfor- 
mance when considering up to four documents 
retrieved for each query/summary, although it 
is worse than indexing by synsets. This con- 
firms our intuition that synset indexing has ad- 
vantages over plain word sense disambiguation, 
because it permits matching semantically simi- 
lar terms. 
Taking only the first document retrieved for 
each summary, the disambiguated collection 
gives a 53.2% success against a 48% of the 
plain SMART query, which represents a 11% im- 
provement. For recall levels higher than 0.85, 
however, the disambiguated collection performs 
slightly worse. This may seem surprising, as 
word sense disambiguation should only increase 
our knowledge about queries and documents. 
But we should bear in mind that WordNet 1.5 is 
not the perfect database for text retrieval, and 
indexing by word senses prevents some match° 
ings that can be useful for retrieval. For in- 
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t- O 
o 
tl. 
0.8 
0.6 
0.4 
0.2 
0 I 
0.3 0.4 
Figure 2: sensitivity to disambiguation errors 
! ! I ! 
1. Manual disambiguation x 
2. 5% error -~--- 
3. 10% error -E3-- 
4. 20% error .-~ ..... 
5. 30% error --~--- 
6. All possible synsets per word (without disambigua~on) -~.- 
7. 60% error -<,--- 
8. SMART -~'--- 
21 3~ 
.... ::.-....-...:.. "-....-..>. 
• .--..::....... ..... ~. .~ 
°e. ~-- 
! I I I I 
0.5 0.6 0.7 0.8 0.9 
Recall 
stance, design is used as a noun repeatedly in 
one of the documents, while its summary uses 
design as a verb. WordNet 1.5 does not include 
cross-part-of-speech semantic relations, so this 
relation cannot be used with word senses, while 
term indexing simply (and successfully!) does 
not distinguish them. Other problems of Word- 
Net for text retrieval include too much fine- 
grained sense-distinctions and lack of domain 
information; see (Gonzalo et al., In press) for 
a more detailed discussion on the adequacy of 
WordNet structure for text retrieval. 
4.2 Sensitivity to disambiguation errors 
Figure 2 shows the sensitivity of the synset indexing 
system to degradation of disambiguation accuracy 
(corresponding to the experiments 4 and 5 described 
above). Prom the plot, it can be seen that: 
• Less than 10% disambiguating errors does 
not substantially affect performance. This is 
roughly in agreement with (Sanderson, 1994). 
• For error ratios over 10%, the performance de- 
grades quickly. This is also in agreement with 
(Sanderson, 1994). 
• However, indexing by synsets remains better 
than the basic SMART run up to 30% disam- 
biguation errors. From 30% to 60%, the data 
does not show significant differences with stan- 
dard SMART word indexing. This prediction 
differs from (Sanderson, 1994) result (namely, 
that it is better not to disambiguate below a 
90% accuracy). The main difference is that 
we are using concepts rather than word senses. 
But, in addition, it must be noted that Sander- 
son's setup used artificially created ambiguous 
pseudo words (such as 'bank/spring ~ which are 
not guaranteed to behave as real ambiguous 
words. Moreover, what he understands as dis- 
ambiguating is selecting -in the example- bank 
or spring which remain to be ambiguous words 
themselves. 
• If we do not disambiguate, the performance is 
slightly worse than disambiguating with 30% er- 
rors, but remains better than term indexing, al- 
though the results are not definitive. An inter- 
esting conclusion is that, if we can disambiguate 
reliably the queries, WordNet synset indexing 
could improve performance even without dis- 
ambiguating the documents. This could be con- 
firmed on much larger collections, as it does not 
involve manual disambiguation. 
It is too soon to say if state-of-the-art WSD tech- 
niques can perform with less than 30% errors, be- 
cause each technique is evaluated in fairly different 
settings. Some of the best results on a compara- 
ble setting (namely, disambiguating against Word- 
Net, evaluating on a subset of the Brown Corpus, 
and treating the 191 most frequently occurring and 
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c- 
o 
gJ 
a. 
0.8 
0.6 
0.4 
0.2 
0 
0.3 
Figure 3: Performance with non-disambiguated queries 
i ! ! ! 
Indexing by words (SMART) o 
Synset indexing with non-disambiguated queries -+--- 
Word-sense indexing with non-disambiguated queries -D-- 
12 
- o... o ~ "'lb. 
""-gl..° 
I I I l I I 
0.4 0.5 0.6 0.7 0.8 0.9 
Recall 
ambiguous words of English) are reported reported 
in (Ng, 1997). They reach a 58.7% accuracy on a 
Brown Corpus subset and a 75.2% on a subset of the 
Wall Street Journal Corpus. A more careful evalua- 
tion of the role of WSD is needed to know if this is 
good enough for our purposes. 
Anyway, we have only emulated a WSD algorithm 
that just picks up one sense and discards the rest. A 
more reasonable approach here could be giving dif- 
ferent probabilities for each sense of a word, and use 
them to weight synsets in the vectorial representa- 
tion of documents and queries. 
4.3 Performance for non-disambiguated 
queries 
In Figure 3 we have plot the results of runs with 
a non-disambiguated version of the queries, both for 
word sense indexing and synset indexing, against the 
manually disambiguated collection (experiment 6). 
The synset run performs approximately as the basic 
SMART run. It seems therefore useless to apply con- 
ceptual inde.,dng if no disambiguation of the query is 
feasible. This is not a major problem in an interac- 
tive system that may help the user to disambiguate 
his query, but it must be taken into account if the 
process is not interactive and the query is too short 
to do reliable disambiguation. 
5 Conclusions 
We have experimented with a retrieval approach 
based on indexing in terms of WordNet synsets in- 
stead of word forms, trying to address two questions: 
1) what potential does WordNet offer for text re- 
trieval, abstracting from the problem of sense disam- 
biguation, and 2) what is the sensitivity of retrieval 
performance to disambiguation errors. The answer 
to the first question is that indexing by synsets 
can be very helpful for text retrieval, our experi- 
ments give up to a 29% improvement over a standard 
SMART run indexing with words. We believe that 
these results have to be further contrasted, but they 
strongly suggest that WordNet can be more useful 
to Text Retrieval than it was previously thought. 
The second question needs further, more fine- 
grained, experiences to be clearly answered. How- 
ever, for our test collection, we find that error rates 
below 30% still produce better results than stan- 
dard word indexing, and that from 30% to 60% er- 
ror rates, it does not behave worse than the standard 
SMART run. We also find that the queries have to 
be disambiguated to take advantage of the approach; 
otherwise, the best possible results with synset in- 
dexing does not improve the performance of stan- 
dard word indexing. 
Our first goal now is to improve our retrieval 
system in many ways, studying how to enrich the 
query with semantically related synsets, how to corn- 
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pare documents and queries using semantic informa- 
tion beyond the cosine measure, and how to obtain 
weights for synsets according to their position in the 
WordNet hierarchy, among other issues. 
A second goal is to apply synset indexing in a 
Cross-Language environment, using the Euro Word- 
Net multilingual database (Gonzalo et al., In press). 
Indexing by synsets offers a neat way of performing 
language-independent retrieval, by mapping synsets 
into the EuroWordNet InterLingual Index that finks 
monolingual wordnets for all the languages covered 
by EuroWordNet. 
Acknowledgments 
This research is being supported by the European 
Community, project LE #4003 and also partially by 
the Spanish government, project TIC-96-1243-CO3-O1. 
We are indebted to Ren~e Pohlmann for giving us good 
pointers at an early stage of this work, and to Anselmo 
Pefias and David FernAndez for their help finishing up 
the test collection. 

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