Automatic Extraction of Examples for Word Sense Disambiguation

CHAPTER 2. BASIC APPROACHES TO WORD SENSE DISAMBIGUATION 24
that represent the relevant information about the example they refer to. Those attributes are
predefined and picked up uniformly for each instance so that each vector has the same number
and type of features. There are three types of features from which the selection can be made
- local features, global features and syntactic dependencies. Local features describe the narrow
context around the target word - bigrams, trigrams (other n-grams) that are generally consti-
tuted by lemmas, POS tags, word-forms together with their positions with respect to the target
word. Global features on the other hand consider not only the local context around the word
that is being disambiguated, but the whole context (whole sentences, paragraphs, documents)
aiming to describe best the semantic domain in which the target word is found. The third type
of features that can be extracted are the syntactic dependencies. They are normally extracted
at a sentence level using heuristic patterns and regular expressions in order to gain a better
representation for the syntactic cues and argument-head relations such as subject, object, noun-
modifier, preposition etc.
Commonly, features are selected out of a predefined pool of features (PF), which consists out
of the most relevant features for the approached task. We compiled such PF (see Appendix B)
being mostly influenced for our choice by (Dinu and Kübler, 2007) and (Mihalcea, 2002).
Let us consider a simple example. Given the sentences in (2) we will attempt to create FVs
that represent the occurrences of the noun bank in those sentences.
(2)
1. ”He sat on the bank of the river and watched the currents.”
2. ”He operated a bank of switches.”
For the sake of simplicity at this point we chose a minimum number of features from the pre-
defined pool of features. The corresponding set consists only of local features (cw−2, cw−1, cw0,
cw+1, cw+2, cp−2, cp−1, cp0, cp+1, cp+2). The resulting vectors thus can be seen in Table 2.4
below. Since we do not explain in detail exactly how those vectors are put together, please refer
to Chapter 6 where we follow stepwise the process of FV construction.
# cw−2 cw−1 cw0 cw+1 cw+2 cp−2 cp−1 cp0 cp+1 cp+2 class
1. on the bank of the P DET N P DET bank%1:17:01::
2. operated a bank of switches V DET N P N bank%1:14:01::
Table 2.4: Feature vectors for the sentences in (2).
As we have already noted, supervised learning is based on the training data that has been
manually annotated beforehand while unsupervised learning does not need such predefined
sense inventory. Thus, a very important part of the FV is the class label that represents the
classification of this vector (this is the value of the output space that is associated with a train-
ing example) which in our case is to be found in the class column. As its value we have selected
the correct sense of the noun bank in the sentence represented by the sense key that this sense