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

3.2. Lexical-Semantic Information Extraction 49
give the following sentences, taken from the British National Corpus 23 (BNC), to
illustrate their assumptions:
1. This is not the case with sugar, honey, grape must, cloves and other spices
which increase its merit.
⇒ {clove hyponym of spice}
2. Ships laden with nutmeg or cinnamon, cloves or coriander once battled the
Seven Seas to bring home their precious cargo.
⇒ {nutmeg hyponym of spice}
⇒ {cinnamon hyponym of spice}
⇒ {coriander hyponym of spice}
Using the correct relations extracted without the LSA filter, for each hyponym,
the top ten most similar words were collected and tested for having the same hypernym.
This resulted in a slight improvement of precision, while the number of
relations obtained was ten times higher.
Berland and Charniak (1999) present work on the extraction of part-of relations
from a corpus, using handcrafted patterns. In a similar fashion to Hearst (1992),
seed instances are used to infer linguistic patterns, then used to acquire new relation
instances. In the end, the extracted instances are ranked according to their loglikelihood
(Dunning, 1993).
Girju and Moldovan (2002) followed Hearst’s method to discover lexical-syntactic
patterns expressing causation. Given that only some categories of nouns (e.g.
states of affairs) can be associated with causation, extracted relations were later
validated regarding semantic constraints on the relation arguments.
Cimiano and Wenderoth (2007) present an approach for the automatic acquisition
of qualia structures (Pustejovsky, 1991), which aim to describe the meaning
of lexical elements (earlier presented in section 2.2.5 of this thesis). Willing to
decrease the problem of data sparseness, they propose looking for discriminating
patterns in the Web. For each qualia term, a set of search engine queries for each
qualia role is generated, based on known lexical-syntactic patterns. The first 50
snippets returned are downloaded and POS-tagged. Then, patterns, defined over
POS-tags, conveying the qualia role of interest, are matched to obtain candidate
qualia elements. In the end, the candidates are weighted and ranked according to
well-known similarity measures (e.g. Jaccard coefficient, PMI).
The main problem of the aforementioned approaches is that they rely on a finite
set of handcrafted rules, though some discovered with the help of automatic procedures,
and are therefore vulnerable to data sparseness. Even though Hearst (1992)
says that the six proposed patterns occur frequently, they are unlikely to capture
all the occurrences of the target relation(s).
About the manual identification of semantic patterns, Snow et al. (2005) add
that it is not very interesting and can be biased by the designer. They propose
a supervised approach, trained with WordNet, to discover hyponymy patterns,
and an automatic classifier that decides if a hypernymy relation holds between two
nouns. Their procedure works as follows:
23 See http://www.natcorp.ox.ac.uk/ (August 2012)