Automatic Extraction of Examples for Word Sense Disambiguation

CHAPTER 2. BASIC APPROACHES TO WORD SENSE DISAMBIGUATION 14
(1)
pine 1 * seven kinds of evergreen tree with needle-shaped leaves
2 pine
3 waste away through sorrow or illness
4 pine for something; pine to do something
cone 1 solid body which narrows to a point
2 something of this shape whether solid or hollow
3 * fruit of certain evergreen trees (fir, pine)
As just mentioned, each of the separate senses has its own entry and is compared to the
entries for the neighboring words of the target word. The aim of the comparison is to find over-
lapping words in the targeted entries. Thus, each of the entries of pine is compared to each of the
entries of cone. As a result the largest overlap appears to be between the first sense of pine and
the third sense of cone, both marked with * in example (1), hence those are the senses selected
from the Lesk algorithm for both words. In such a way the latter algorithm disambiguates both
words pine and cone if they appear together in the same context. In cases where each of those
words is seen in different surroundings, the process of comparison of the dictionary entries for
those surrounding words and the word pine or respectively cone will be pursuit.
2.1.2 Alternative Methods
As Mihalcea (2007) notes, another type of knowledge-based method uses semantic similarity
measures that are calculated from already existing semantic networks (e.g. WordNet 1 , Lexi-
pedia 2 etc.). The nature of this approach relies mostly on the fact that the smallest semantic
distance between two words can be used as a trustworthy metric to show how closely related
those words are. There are two different ways in the attempt to acquire the semantic similarity
- local and global. The only difference between those two approaches is that in order to build
threads of meaning the method either looks only in a very small window size of the context of
the target word and discards the rest (local) or the entire text/document is considered (global).
Selectional preferences can be used both in knowledge-based as well as in corpus-based ap-
proaches for word sense disambiguation. This method tries to account for the fact that linguistic
elements favor arguments of a specific semantic class, e.g. a verb like ’drink’ selects as object
drinkable things while a verb like ’eat’ prefers as object edible things, and both prefer as subject
animate entities, as for example: ”He ate some chocolate.”. Selectional preferences however could
get a lot more complex than it might seem - consider for example sentences as: ”The acid ate the
1 http://wordnet.princeton.edu/
2 http://www.lexipedia.com/