Programme booklet (pdf)

62
CLIN 21 – CONFERENCE PROGRAMME
Subtrees as a new type of context in Word Space Models
Abstract
Smets, Margaux and Speelman, Dirk and Geeraerts, Dirk
QLVL, K.U.Leuven
In Word Space Models (WSMs) there are traditionally two types of contexts that can be
used: (i) lexical co-occurrences (`bag-of-words models') and (ii) syntactic dependencies.
In general, models with the second type of contexts seem to perform better. However,
there are some problems with these models. In the first place, a choice has to be made
which contexts to include: only subject/verb and verb/object-relations, or also other
dependencies . Second, in contrast with bag-of-words models, the syntactic models are
supervised: they require quite large resources (a dependency parser, a manually
annotated corpus, . . .), which might not be available for each language .
The contexts we propose for use in WSMs are subtrees as defined in the framework of
Data-Oriented-Parsing. Subtrees can capture both bag-of-words (co- occurrence)
information, and syntactic information. Moreover, they are not limited to specific types
of dependencies, but rather take entire structures into account.
At first sight, it might seem that the problem of resources for dependency-WSMs
remains in this framework. After all, we first need the `correct' tree for a sentence,
before we can extract subtrees from it. However, in our experiments we show how the
entire algorithm can be made unsupervised by using an unsupervised parser as a
preprocessing step.
In the presentation, I will first discuss in detail the workings of this new type of WSM.
Next, I will present some initial results from experiments with parameters such as the
accuracy of the parser in the preprocessing step, the maximum subtree depth, the
minimum subtree frequency, and considering only subtrees with the highest variance.
Corresponding author: margauxsmets@gmail.com