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

86 Chapter 5. Synset Discovery
cota and planta. Besides an italian dish (not included), the word pasta might have
the popular meaning of money, the figurative meaning of a mixture of things, or
it might denote a file or a briefcase. As for the word cota, besides height (not
included), it can be a quota or portion, or refer to an old and respectable person,
and informally denote a father or a mother. The word planta might either denote
a plan or some guidelines, or it might denote a vegetable. Besides some synonyms
of plant/vegetable (e.g. planta, vegetal), the synset with the vegetable meaning
contains many actual plants or vegetables (e.g. maruge, camélia). After analysing
this problem, we noticed that the dictionary DA contains several definitions of plants
where the first sentence is just planta, without any differentia. Therefore, even
though the correct relation to extract would be hypernymy, our grammars see those
definitions as denoting synonymy. Another limitation shown by these examples is
that, sometimes, the fuzzy synsets contain words which are not synonyms, but have
similar neighbourhoods.
5.3.3 Thesaurus data for different cut points
After analysing the fuzzy synsets, we inspected the impact of applying different
cut-points (θ) in the transformation of the fuzzy thesaurus into a simple thesaurus.
Tables 5.3 and 5.4 present the properties of the different thesauri obtained with
different values for θ. Considering just the words of the thesauri, table 5.3 includes
the number of words, how many of those are ambiguous, the average number of
senses per word, and the number of senses of the most ambiguous word. As for
synsets, table 5.4 shows the total number of synsets, the average synset size in
terms of words, synsets of size 2 and size larger than 25, which are less likely to be
useful (Borin and Forsberg, 2010), as well as the largest synset. Both tables do not
consider synsets of size 1.
Before collecting the data in Tables 5.3 and 5.4, we followed one of the clustering
methods for word senses proposed for EuroWordNet, which suggests that synsets
with three members in common can be merged (Peters et al., 1998). However, the
design of our clustering algorithm and the configuration of our synonymy networks
are prone to create synsets sharing more than one word. So, to minimise the possibility
of merging synsets denoting different concepts, we made sure that merged
synsets had at least 75% overlap, computed as follows, where |Synset| denotes the
number of words of a synset:
Overlap(Synseta, Synsetb) =
Synseta ∩ Synsetb
min(|Synseta|, |Synsetb|)
(5.9)
As expected, as θ grows, ambiguity drops. This is observed not only from the
number of ambiguous words, but also from the average number of word senses and
the number of synsets. For instance, with θ = 0.5, despite the establishment of
8,000 clusters, each word has only one sense, which means there is no ambiguity.
Out of curiosity, the largest synset in CLIP, with θ = 0.075, denotes the concept of
money. It contains the following 58 words:
• jimbo, pastel, bagarote, guines, baguines, parrolo, marcaureles, ouro, grana, arame,
massaroca, tutu, metal, bagalho, níquel, bilhestres, milho, jan-da-cruz, china, cumquibus,
mussuruco, cobre, numerário, pilim, bagaço, pasta, zerzulho, painço, finanças,
chelpa, calique, posses, bagalhoça, pecuniária, boro, dieiro, pila, gaita,