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

148 Chapter 8. Onto.PT: a lexical ontology for Portuguese
Given a sentence to disambiguate, both of the algorithms take advantage of a
given context W = {w1, w2, ..., wn}, which includes all the (content) words of the
sentence (nouns, verbs and, eventually, adjectives and adverbs). Before applying
the algorithms, the sentence is POS-tagged and lemmatised. Then, for each word
wi ∈ W to be disambiguated, the set of candidate synsets, Ci = {Si1, Si2, ..., Sim},
is retrieved from Onto.PT. Each candidate synset must contain the word to be
disambiguated, Sj ∈ C → wi ∈ Sj. The goal of each algorithm is to select a suitable
synset Sk ∈ C, for the occurrence of the word wi in the context W . The selected
synset should transmit the meaning of the word, when in the given context. The
selection of the best candidate depends on the used algorithm:
Bag-of-Words: For each candidate Sj, a set Rj = {qj1, qj2, ..., qjp} is established
with all the words in Sj and in synsets directly related with Sj, in
Onto.PT. The selected synset is the one maximising the similarity with the context
Sk : sim(Rk, W ) = max(sim(Ri, W )). Similarities may be computed by measures
typically used for comparing the similarity of sets, such as the Jaccard or the Overlap
coefficient (both referred in section 6.1.2 and other sections of this thesis).
This algorithm is actually an adaptation of the Lesk algorithm (Lesk, 1986;
Banerjee and Pedersen, 2002), with two main differences. First, in the Lesk algorithm
adapted for WordNet, the “context” of a sense consists not only of the words
in the synset, but also of words in its gloss and in example sentences. As Onto.PT
does not contain synset glosses, we use all the words in related synsets. Second, in
the Lesk algorithm, the similarity of contexts is given by the number of common
terms, while we use a more complex similarity measure. This way, the selection of
the most suitable synset is not biased towards synsets with larger “contexts”.
Personalized PageRank: As referred in section 7.1, the PageRank algorithm
(Brin and Page, 1998) ranks the nodes of a graph according to their structural
importance. However, it has been used to solve more specific problems, including
WSD with a wordnet (Agirre and Soroa, 2009). Our implementation is based on the
later work, and uses all Onto.PT. For such, we consider that Onto.PT is a graph
G = (V, E), with |V | nodes, representing the synsets, and |E| undirected edges, for
each relation between synsets. For a given context W , only the synsets with words
in the context have initial weights, which are uniformly distributed. The rest of the
synsets do not have an initial weight. After several iterations, it is expected that
more relevant synsets for the given context are ranked higher. Therefore, for each
word wi, this algorithm selects the highest ranked candidate synset.
WSD using Onto.PT is exemplified in the following real sentences, obtained from
AC/DC (Santos and Bick, 2000). For each sentence, we used all nouns and verbs
as context, and applied the Personalized PageRank algorithm to assign a suitable
synset for the occurrence of each noun. Each sentence is presented with the nouns
underlined. Then, for each noun, we show, in parenthesis, the number of senses they
have in Onto.PT, which is the number of alternative synsets including them, and,
of course, we show the selected synset.
(1) Vai estar, seguramente, colocado num local envergonhado e inacessível que
obrigará o pobre cidadão que pretenda reclamar a sujeitar-se à censura de