Onto.PT: Towards the Automatic Construction of a Lexical Ontology ...

5.4. Discussion 93
discovered synsets result in CLIP, a Portuguese thesaurus, larger than public domain
Portuguese handcrafted thesauri. CLIP was compared with those thesauri, which
lead us to the conclusion that we can obtain an even larger thesaurus if we integrate
all thesauri. Given that OT.PT, the smaller thesaurus used in our experimentation,
is currently used for suggesting synonyms in OpenOffice 3 writer, CLIP can be seen as
a larger alternative for the same purpose. Still, since size is not the only important
property of a thesaurus, it is always possible to create a smaller thesauri, after
filtering less common words.
The proposed algorithm may be used for the creation of a fuzzy thesaurus, where
words have membership degrees to each synset. Having in mind that word senses
are not discrete, representing natural language concepts as fuzzy synsets is closer to
reality than using simple synsets. Moreover, a fuzzy thesaurus is a useful resource
for NLP. For instance, in WSD, choosing the synset where the target word has higher
membership might be used as a baseline. As far as we know, the fuzzy version of
our thesaurus is the first ever Portuguese thesaurus with fuzzy memberships.
The presented approach has however shown some limitations. For instance, a
fixed cut-point is probably not the best option while moving from a fuzzy thesaurus
to a thesaurus without fuzzy memberships. Therefore, we have recently added the
possibility of having a variable cut-point, relative to the highest membership in the
set. Possibly the main limitation of our approach is that synsets are not created
when word senses do not have dictionary entries with synonyms. This is something
we will have to deal in the future. Finally, the manual evaluation showed interesting
but not optimal results (75% accuracy), which indicates that there is still room for
improvement.
In any case, as we will discuss in the following chapters, CLIP could be used
as the synset-base for a future wordnet-like resource. But TeP is a similar public
alternative. And as it is created manually by experts, we have high confidence on its
contents, so, we decided to use it as the starting point of our synset-base. The next
chapter describes how TeP can be enriched with synonymy information extracted
from dictionaries, in order to have a broader thesaurus. In order to discover new
synsets, only the synpairs not added to TeP are the target of a clustering algorithm,
similar to the one presented here.
3 See http://www.openoffice.org/ (September 2012)