30,460 research outputs found
Nonparametric Bayesian Topic Modelling with Auxiliary Data
The intent of this dissertation in computer science is to study
topic models for text analytics. The first objective of this
dissertation is to incorporate auxiliary information present in
text corpora to improve topic modelling for natural language
processing (NLP) applications. The second objective of this
dissertation is to extend existing topic models to employ
state-of-the-art nonparametric Bayesian techniques for better
modelling of text data. In particular, this dissertation focusses
on:
- incorporating hashtags, mentions, emoticons, and target-opinion
dependency present in tweets, together with an external sentiment
lexicon, to perform opinion mining or sentiment analysis on
products and services;
- leveraging abstracts, titles, authors, keywords, categorical
labels, and the citation network to perform bibliographic
analysis on research publications, using a supervised or
semi-supervised topic model; and
- employing the hierarchical Pitman-Yor process (HPYP) and the
Gaussian process (GP) to jointly model text, hashtags, authors,
and the follower network in tweets for corpora exploration and
summarisation.
In addition, we provide a framework for implementing arbitrary
HPYP topic models to ease the development of our proposed topic
models, made possible by modularising the Pitman-Yor processes.
Through extensive experiments and qualitative assessment, we find
that topic models fit better to the data as we utilise more
auxiliary information and by employing the Bayesian nonparametric
method
Word Embeddings: A Survey
This work lists and describes the main recent strategies for building
fixed-length, dense and distributed representations for words, based on the
distributional hypothesis. These representations are now commonly called word
embeddings and, in addition to encoding surprisingly good syntactic and
semantic information, have been proven useful as extra features in many
downstream NLP tasks.Comment: 10 pages, 2 tables, 1 imag
Modelling trust in semantic web applications
This paper examines some of the barriers to the adoption of car-sharing, termed carpooling in the US, and develops a framework for trusted recommendations. The framework is established on a semantic modelling approach putting forward its suitability to resolving adoption barriers while also highlighting the characteristics of trust that can be exploited. Identification is made of potential vocabularies, ontologies and public social networks which can be used as the basis for deriving direct and indirect trust values in an implementation
MetaLDA: a Topic Model that Efficiently Incorporates Meta information
Besides the text content, documents and their associated words usually come
with rich sets of meta informa- tion, such as categories of documents and
semantic/syntactic features of words, like those encoded in word embeddings.
Incorporating such meta information directly into the generative process of
topic models can improve modelling accuracy and topic quality, especially in
the case where the word-occurrence information in the training data is
insufficient. In this paper, we present a topic model, called MetaLDA, which is
able to leverage either document or word meta information, or both of them
jointly. With two data argumentation techniques, we can derive an efficient
Gibbs sampling algorithm, which benefits from the fully local conjugacy of the
model. Moreover, the algorithm is favoured by the sparsity of the meta
information. Extensive experiments on several real world datasets demonstrate
that our model achieves comparable or improved performance in terms of both
perplexity and topic quality, particularly in handling sparse texts. In
addition, compared with other models using meta information, our model runs
significantly faster.Comment: To appear in ICDM 201
Dirichlet belief networks for topic structure learning
Recently, considerable research effort has been devoted to developing deep
architectures for topic models to learn topic structures. Although several deep
models have been proposed to learn better topic proportions of documents, how
to leverage the benefits of deep structures for learning word distributions of
topics has not yet been rigorously studied. Here we propose a new multi-layer
generative process on word distributions of topics, where each layer consists
of a set of topics and each topic is drawn from a mixture of the topics of the
layer above. As the topics in all layers can be directly interpreted by words,
the proposed model is able to discover interpretable topic hierarchies. As a
self-contained module, our model can be flexibly adapted to different kinds of
topic models to improve their modelling accuracy and interpretability.
Extensive experiments on text corpora demonstrate the advantages of the
proposed model.Comment: accepted in NIPS 201
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