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

4.1. Semantic relations from definitions 59
where the grammars are created, and two automatic steps. Semantic relations, held
between words in the definitions and the definiendum, are extracted after processing
dictionary entries. Extracted relation instances are represented as term-based
relational triples (hereafter, tb-triples) with the following structure:
arg1 RELATION NAME arg2
A tb-triple indicates that one sense of the lexical item in the first argument (arg1)
is related to one sense of the lexical item in the second argument (arg2) by means
of a relation identified by RELATION NAME. For instance:
animal HIPERONIMO DE c~ao (animal HYPERNYM OF dog)
Each step of the extraction procedure is illustrated in figure 4.2, and encompasses
the following steps:
1. Creation of the extraction grammars: After a careful analysis of the
structure of the dictionary definitions, patterns that denote semantic relations
are manually compiled into grammars. The rules of the grammars are made
specifically for the extraction of relations between words in dictionary definitions
and their definiendum.
2. Extraction of semantic relations: The grammars are used together with
a parser 1 that processes the dictionary definitions. Only definitions of open
category words (nouns, verbs, adjectives and adverbs) are processed. In the
end, if definitions match the patterns, instances of semantic relations are extracted
and represented as tb-triples t = {w1 R w2} where w1 is a word in the
definition, w2 is the definiendum, and R is the name of a relation established
by one sense of w1 and one sense of w2.
3. Cleaning and lemmatisation: After extraction, some relations have invalid
arguments, including punctuation marks or prepositions. Definitions are thus
POS-tagged with the tagger provided by the OpenNLP toolkit 2 , using the
models for Portuguese 3 . Triples with invalid arguments are discarded 4 . Moreover,
if the arguments of the triples are inflected and thus not defined in the
dictionary, lemmatisation rules are applied 5 .
This procedure results in a set of tb-triples of different predefined types. The
resulting set may be formally seen as a term-based directed lexical network (see
section 2.2.3). To this end, each tb-triple t = {w1 R w2} will denote an edge with
label R, connecting words w1 and w2, which will be the nodes.
1We used the chart parser PEN, available from https://code.google.com/p/pen/ (September
2012)
2Available from http://incubator.apache.org/opennlp/ (September 2012)
3See http://opennlp.sourceforge.net/models-1.5/ (September 2012)
4Definitions are not tagged before extraction because the tagger models were trained in corpora
text and do not work as well as they should for dictionary definitions. Furthermore, the grammars
of PAPEL do not consider tags. Tagging at this stage should only be seen as a complement to the
information provided by the dictionary.
5The lemmatisation rules were compiled by our colleague Ricardo Rodrigues, and take advantage
of the annotation provided by the OpenNLP POS tagger.