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

50 Chapter 3. Related Work
1. Extract all hypernym-hyponym pairs from WordNet.
2. For each pair, find sentences in which both words occur.
3. Parse the sentences, and automatically extract patterns from the obtained trees,
which are good cues for hypernymy.
4. Train a hypernymy classifier based on the previous features.
Besides rediscovering the six Hearst patterns, which gives a quantitative justification
to Hearst’s intuition, Snow et al. (2005) were able to discover the following
additional patterns:
• like
• called
• is a
• , a
Girju et al. (2006) used a heavily supervised approach as well, based on WordNet,
this time for discovering part-of relations. The same authors presented a similar
approach for the extraction of manner-of relations (Girju et al., 2003). However,
as WordNet does not contain this kind of relation, the classifier was trained with a
corpus where these relations were manually annotated.
Despite quite successful works on supervised LSIE, when there is not an available
set of reliable relations of a certain type with a considerable size, a fully supervised
approach is not suitable, unless one is willing to create such a set. An alternative is
to use a bootstrapping approach, as in the Espresso algorithm (Pantel and Pennacchiotti,
2006), that acquires semantic relations with minimal supervision. Pantel
and Pennacchiotti (2006)’s main contribution is the exploitation of broad coverage
noisy patterns (generic patterns), which increase recall, but have typically low precision
(e.g. X of Y for part-of). Espresso starts with a small set of seed instances,
I, and iterates through three main phases: (i) pattern induction, (ii) pattern ranking/selection,
and (iii) instance extraction, briefly described below:
1. Infer a set of surface patterns, P , which are strings that, in the corpus, connect
the arguments of the seed instances.
2. Rank each inferred pattern, p ∈ P , according to its reliability, rπ(p), given by
its average strength of association across each instance i ∈ I:
rπ(p) =
i∈I
pmi(i,p)
maxpmi
|I|
∗ rl(i)
Here, maxpmi is the maximum PMI between all patterns and all instances, given
by the ratio between the frequency of p connecting terms x and y, |x, p, y|, and
all the co-occurrences of x and y times the number of occurrences of p, pmi(i, p):