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

Chapter 5
Synset Discovery
As referred in the previous chapter, a LKB structured in words, instead of concepts,
does not handle lexical ambiguity and might lead to serious inconsistencies. To
deal with that issue, wordnets are structured in synsets, which are groups of words
sharing a common meaning and thus representing a concept. This chapter is about
the discovery of synsets from a term-based LKB, which is the first step for moving
towards a sense-aware resource.
Since a synset groups words according to their synonymy, in this step, we only
use the network established by the synonymy triples extracted from dictionaries.
On the one hand, co-occurrence graphs extracted from corpora have shown to be
useful for identifying not only synonymous words, but also word senses (Dorow,
2006). It should be mentioned that, in opposition to other kinds of relation, synonymous
words share similar neighbourhoods, but may not co-occur frequently
in corpora text (Dorow, 2006), which leads to few textual patterns connecting
this kind of words. So, as referred in section 3.2.2, most of the works on synonymy
(or near-synonymy) extraction from corpora rely on the application of mathematical
models (e.g. Turney (2001)), including graphs, clustering algorithms, or
both (e.g. Dorow (2006)). On the other hand, in synonymy networks extracted
from dictionaries, clusters tend to express concepts (Gfeller et al., 2005) and can
therefore be exploited for the establishement of synsets. Methods for improving the
organisation of synonymy graphs, extracted from different resources, are presented
by Navarro et al. (2009).
As other authors noticed for PAPEL (Prestes et al., 2011), we confirmed that
synonymy networks extracted from dictionaries connect more than half of the words
by, at least, one path. Therefore, as others did for discovering new concepts from
text (e.g. Lin and Pantel (2002)), we used a (graph) clustering algorithm on our
synonymy networks. This kind of work is related to WSD. More specifically, it
can be seen as word sense induction (WSI, Navigli (2012)) as it discovers possible
concepts of a word, without exploiting an existing sense inventory.
As discussed in section 4.3, from a linguistic point of view, word senses are
not discrete, so their representation as crisp objects does not reflect the human
language. A more realistic approach for coping with this fact is to represent synsets
as models of uncertainty, such as fuzzy sets, to handle word senses and natural
language concepts. Our clustering algorithm can be used for the discovery of fuzzy
synsets. The fuzzy membership of a word in a synset can be interpreted as the
confidence level about using this word to indicate the meaning of the synset.