Automatic Extraction of Examples for Word Sense Disambiguation

CHAPTER 2. BASIC APPROACHES TO WORD SENSE DISAMBIGUATION 21
automatic mapping to the most recent version is usually also provided. Other such resources are:
Hector (Atkins, 1993), Longman Dictionary of Contemporary English (Procter, 1978), BalkaNet
(Stamou et al., 2002), etc.
Theoretically, each dictionary could be used as a sense inventory for WSD. However, there are
several problems coming along. First, dictionaries are not always freely available for research,
which was the reason why WordNet became in fact the standard sense inventory for the last
decade. However, it is still being argued if it is good as such. Since WordNet distinguishes
between the senses of each word in an extremely fine-grained manner, it is often hard to use it
for WSD, hence there are cases where a coarser distinction is desirable. Calzolari et al. (2002)
even argue that the use of WordNet as a sense inventory in WSD yields worse results than using
traditional dictionaries. However, it is not WordNet itself but the predefined sense inventory as
such that appears to hinder supervised word sense disambiguation. There is a large number
of attempts to solve the latter problem and although none of them completely succeed WordNet
will continue to be the standard sense inventory for WSD.
Another problem in respect to sense inventories is their compatibility. Each dictionary has
its own granularity and representation of senses, which are normally extremely hard if not even
impossible to map against each other. Thus systems that use different sense inventories are
impossible to compare, since their performance is bound to the inventory they use. Of course,
since this issue is a well known problem already, evaluation exercises (see Chapter 4) use a
single sense inventory for all the participating systems or it is required that those inventories
that are different from the standard one provide mapping to it.
2.3.2 Source Corpora
One of the biggest problems for supervised word sense disambiguation (the knowledge acquisi-
tion bottleneck problem) is the fact that there are very few annotated corpora that can be used
in order good and reliable broad-coverage systems to be trained. This is due to the fact that the
creation of such corpora requires a highly laborious human effort. The huge dependency of the
method on the provided corpora is the reason why for languages other than English, supervised
WSD is extremely difficult if not even impossible. Below follows a brief description of the main
data sources for supervised WSD.
Senseval provides several corpora not only for English but as well for languages as Ital-
ian, Basque, Catalan, Chinese, Romanian and Spanish. The most recent edition of Senseval
(Senseval-3) resulted in the following annotated corpora:
- English all words - 5000 words were tagged from Penn Treebank text (Marcus et al., 1993)
with WordNet as senses.
- English lexical sample - 57 words collected via The Open Mind Word Expert interface (Mi-
halcea and Chklovski, 2003) with WordNet sense inventory.