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Recent technological advances have radically changed the way we communicate. Today’s
communication has become ubiquitous and it has fostered the need for information that is
easier to create, spread and consume. As a consequence, we have experienced the short-
ening of text messages in mediums ranging from electronic mailing, instant messaging to
microblogging. Moreover, the ubiquity and fast-paced nature of these mediums have pro-
moted their use for previously unimaginable tasks. For instance, reporting real-world events
was classically carried out by news reporters, but, nowadays, most interesting events are
first disclosed on social networks like Twitter by eyewitness through short text messages.
As a result, the exploitation of the thematic content in short text has captured the interest
of both research and industry.
Topic models are a type of probability models that have traditionally been used to
explore this thematic content, a.k.a. topics, in regular text. Most popular topic models fall
into the sub-class of LVMs (Latent Variable Models), which include several latent variables
at the corpus, document and word levels to summarise the topics at each level. However,
classical LVM-based topic models struggle to learn semantically meaningful topics in short
text because the lack of co-occurring words within a document hampers the estimation of
the local latent variables at the document level. To overcome this limitation, pooling and
hierarchical Bayesian strategies that leverage on contextual information have been essential
to improve the quality of topics in short text.
In this thesis, we study the problem of learning semantically meaningful and predictive
representations of text in two distinct phases:
• In the first phase, Part I, we investigate the use of LVM-based topic models for the
specific task of event detection in Twitter. In this situation, the use of contextual
information to pool tweets together comes naturally. Thus, we first extend an existing
clustering algorithm for event detection to use the topics learned from pooled tweets.
Then, we propose a probability model that integrates topic modelling and clustering
to enable the flow of information between both components.
• In the second phase, Part II and Part III, we challenge the use of local latent variables
in LVMs, specifically when the context of short messages is not available. First of all,
we study the evaluation of the generalization capabilities of LVMs like PFA (Poisson
Factor Analysis) and propose unbiased estimation methods to approximate it. With
the most accurate method, we compare the generalization of chordal models without
latent variables to that of PFA topic models in short and regular text collections.
In summary, we demonstrate that by integrating clustering and topic modelling, the perfor-
mance of event detection techniques in Twitter is improved due to the interaction between
both components. Moreover, we develop several unbiased likelihood estimation methods for
assessing the generalization of PFA and we empirically validate their accuracy in different
document collections. Finally, we show that we can learn chordal models without latent
variables in text through Chordalysis, and that they can be a competitive alternative to
classical topic models, particularly in short text.