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

3.2. Lexical-Semantic Information Extraction 43
points that should be considered in the automatic extraction of knowledge from a
dictionary, and its conversion into a computational format. They refer three approaches
for creating a knowledge base from a dictionary, which vary in the initial
required amount of knowledge, and in the quality of the extracted information:
• Co-ocurrences enable the establishment of associations between words, without
requiring initial linguistic information.
• A grammar with a collection of linguistic patterns enables, for instance, to
identify the genus (hypernym) and the differentia for each dictionary entry.
• Hand-coding the lexical entries of a controlled vocabulary (about 5% of the
knowledge base), and iterating through the remaining words, enables to derive
a network of semantic units.
While the third approach results in a rich semantic structure, it needs a substantial
amount of initial linguistic knowledge. The first approach produces a much simpler
resource, but does not require hand-coded knowledge.
Ide and Véronis (1995) are very critical of the research on information extraction
from dictionaries. They refer that dictionaries use inconsistent conventions to represent
knowledge and that the definitions are not as consistent as they should be.
Since they are the result of several lexicographers work for several years, dictionaries
have many variations to transmit the same thing. Reviews and updates increase the
probability of inconsistencies.
In order to assess the information extracted from dictionaries,
Ide and Véronis (1995) performed a quantitative evaluation of automatically
extracted hypernymy relations. As hypernymy is the least arguable semantic relation
and the easiest to extract, the authors believed that, if their results were poor,
they would be poorer for more complex domains and less clearly defined relations.
The evaluation consisted of comparing an “ideal” hierarchy, manually created, with
hierarchies extracted from five dictionaries. The extraction procedure was based on
the heuristics of Chodorow et al. (1985), which resulted in tangled hierarchies, later
disambiguated manually. After inspection, it was noticed that these hierarchies had
serious problems of incompleteness and there were difficulties at higher levels:
• Some words were (relatively randomly) attached too high in the hierarchy;
some heads of definitions were not the hypernym of the definiendum, but the
“whole” that contains it; overlaps that should occur between concepts are
sometimes missing.
• All the heads separated by the conjunction or are considered to be hypernyms,
but sometimes, when looking at the hierarchy, problems exist; circularity tends
to occur in the highest levels, possibly when lexicographers lack terms to
designate certain concepts.
The authors state that hierarchies with this kind of problems are likely to be unusable
in NLP systems and discuss means to refine them automatically. Merging the
hierarchies of the five dictionaries and introducing “covert categories” drastically
reduces the amount of problems from 55-70% to 6%. Other problems are minimised
by considering “empty heads” and patterns occurring in the beginning of the definition
that denote the part-of relation; or by using more complex grammars/broadcoverage
parsers instead of static string patterns for extraction.