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

1.2. Approach 5
• They are not built for Portuguese from scratch, and thus have to deal with
translation issues, and include problems as lexical gaps;
• They do not handle word senses, which might lead to inconsistencies regarding
lexical ambiguity.
Looking at this scenario, we set our goal to the development of computational
tools for acquiring, structuring and integrating lexical-semantic
knowledge from text. Although some of these tools can be used independently,
their development had in mind the exploitation of Portuguese resources and the aim
of creating a new lexical ontology for Portuguese, where the aforementioned
limitations were minimised. Consequently, the resulting resource would be:
• Public domain and thus free for being used by anyone, both in a research
or in a commercial setting. We believe this is the best way for the resource to
play its role in helping to advance the state-of-the-art of Portuguese NLP. Furthermore,
a bigger community of users tends to provide important feedback,
useful for improving the resource.
• Created automatically, which would be done by exploiting textual resources
and other public LKBs, all created from scratch for one or more variants
of Portuguese. An automatic construction enables the creation of larger and
broader resources, in a trade-off for lower reliability, but still acceptable for
most tasks.
• Structured according to the wordnet model. This option relied on the great
acceptance of this model and on the wide range of algorithms that work over
this kind of structure to achieve various NLP tasks.
1.2 Approach
Our flexible approach for the acquisition, organisation and integration of
lexical-semantic knowledge involves three main automatic steps. Each step is
independent of each other and can be used for the achievement of simpler tasks. Alternatively,
their combination enables the integration of lexical-semantic knowledge
from different heterogeneous sources and results in a wordnet-like ontology. The
three steps are briefly described as follows:
1. Extraction: instances of semantic relations, held between lexical items, are
automatically extracted from text. As long as the extracted instances are
represented as triples (two items connected by a predicate), the extraction
techniques used in this step do not affect the following steps. In the specific
case of our work, we followed a pattern based extraction on dictionary
definitions.
2. Thesaurus enrichment and clustering: if there is a conceptual base with
synsets for the target language, its synsets are augmented with the extracted
synonymy relations. For this purpose, the network established by all extracted
synonymy instances (synpairs) is exploited for computing the similarities between
each synset and synpair. Both elements of a synpair are then added to
their most similar synset. As for synpairs with two lexical items not covered