Automated classification of texts into genres can benefit NLP applications, in that the
structure, location and even interpretation of information within a text are dictated
by its genre. Cross-lingual methods promise such benefits to languages which lack
genre-annotated training data. While there has been work on genre classification for
over two decades, none has considered cross-lingual methods before the start of this
project. My research aims to fill this gap. It follows previous approaches to monolingual
genre classification that exploit simple, low-level text features, many of which
can be extracted in different languages and have similar functions. This contrasts with
work on cross-lingual topic or sentiment classification of texts that typically use word
frequencies as features. These have been shown to have limited use when it comes
to genres. Many such methods also assume cross-lingual resources, such as machine
translation, which limits the range of their application. A selection of these approaches
are used as baselines in my experiments.
I report the results of two semi-supervised methods for exploiting genre-labelled
source language texts and unlabelled target language texts. The first is a relatively
simple algorithm that bridges the language gap by exploiting cross-lingual features and
then iteratively re-trains a classification model on previously predicted target texts. My
results show that this approach works well where only few cross-lingual resources are
available and texts are to be classified into broad genre categories. It is also shown that
further improvements can be achieved through multi-lingual training or cross-lingual
feature selection if genre-annotated texts are available in several source languages. The
second is a variant of the label propagation algorithm. This graph-based classifier learns
genre-specific feature set weights from both source and target language texts and uses
them to adjust the propagation channels for each text. This allows further feature sets
to be added as additional resources, such as Part of Speech taggers, become available.
While the method performs well even with basic text features, it is shown to benefit
from additional feature sets. Results also indicate that it handles fine-grained genre
classes better than the iterative re-labelling method