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

24 Chapter 2. Background Knowledge
IE. The input of a OIE system is a corpus and the output is a set of facts, represented
as relational triples t = {e1, relation phrase, e2}. There is no need for annotated
data nor need for specifying the relations to extract.
For learning a classifier, an OIE system starts by identifying the noun phrases
of several thousands of sentences in the input corpus. The parsing structure of the
words connecting noun phrases is also analysed. This sequence is labelled as positive
or negative examples of trustworthy relations, according to predefined heuristics.
Positive and negative tuples are finally used to establish triples, where a pair of
noun phrases is connected by a relation phrase. Triples are mapped into feature
vectors, used as the input of a classifier. For the extraction, only a single pass is
needed over the input corpus. Each pair of noun phrases is used as the arguments
of a triple, and the text connecting them is used as the relation phrase. Triples
classified as trustworthy are extracted.
2.4 Remarks on this section
In this section, we bridge the theoretical work described in the previous sections with
the work developed in the scope of this thesis. The first part targets the knowledge
representation in our work and the second is about the information extraction techniques
applied.
2.4.1 Knowledge representation in our work
In our work, instances of semantic relations are first extracted as relational triples
t = {w1, R, w2}, which can both be seen as logical predicates or as the edges of a
lexical network. The types of semantic relations are typical relations between word
senses, including synonymy, hypernymy, several types of meronymy and most of the
relations introduced in section 2.1.2. The arguments of these relations are lexical
items, described by their orthographical form. Word senses are not handled.
On the other hand, the final resource of this work, Onto.PT, can be seen as lexical
ontology, as we have adopted a model inspired by Princeton WordNet (see more
about this resource in section 3.1.1). In order to represent natural language concepts,
Onto.PT groups synonymous words in synsets, which are groups of synonymous
words. This part of the resource can thus be seen as a thesaurus. As for other
semantic relations, Onto.PT includes several predefined types established between
synsets. Given that the presence of a lexical item in a synset defines a new possible
sense of this item, different senses of the same word are recognised.
2.4.2 Information Extraction techniques in our work
We have only exploited dictionaries for the extraction of semantic relations. For this
purpose, we used symbolic techniques over the dictionary definitions (see section 4).
We recall that dictionaries provide a wide coverage of the lexicon and they are
structured in words and meanings. Moreover, definitions tend to use simple vocabulary
and follow regularities, which makes most of them easily predictable. Therefore,
after careful observation, we manually encoded a set of semantic patterns, organised
in grammars, for processing them. Despite the manual labour involved in the
manual creation of the grammars, we could take advantage of one of the pros of