1,311 research outputs found
Unsupervised Terminological Ontology Learning based on Hierarchical Topic Modeling
In this paper, we present hierarchical relationbased latent Dirichlet
allocation (hrLDA), a data-driven hierarchical topic model for extracting
terminological ontologies from a large number of heterogeneous documents. In
contrast to traditional topic models, hrLDA relies on noun phrases instead of
unigrams, considers syntax and document structures, and enriches topic
hierarchies with topic relations. Through a series of experiments, we
demonstrate the superiority of hrLDA over existing topic models, especially for
building hierarchies. Furthermore, we illustrate the robustness of hrLDA in the
settings of noisy data sets, which are likely to occur in many practical
scenarios. Our ontology evaluation results show that ontologies extracted from
hrLDA are very competitive with the ontologies created by domain experts
Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts
We present a Bayesian nonparametric framework for multilevel clustering which
utilizes group-level context information to simultaneously discover
low-dimensional structures of the group contents and partitions groups into
clusters. Using the Dirichlet process as the building block, our model
constructs a product base-measure with a nested structure to accommodate
content and context observations at multiple levels. The proposed model
possesses properties that link the nested Dirichlet processes (nDP) and the
Dirichlet process mixture models (DPM) in an interesting way: integrating out
all contents results in the DPM over contexts, whereas integrating out
group-specific contexts results in the nDP mixture over content variables. We
provide a Polya-urn view of the model and an efficient collapsed Gibbs
inference procedure. Extensive experiments on real-world datasets demonstrate
the advantage of utilizing context information via our model in both text and
image domains.Comment: Full version of ICML 201
Topic modeling-based domain adaptation for system combination
This paper gives the system description of the domain adaptation team of Dublin City University for our participation in the system combination task in the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT (ML4HMT-12). We used the results of unsupervised document classification as meta information to the system combination module. For the Spanish-English data, our strategy achieved 26.33 BLEU points, 0.33 BLEU points absolute improvement over the standard confusion-network-based system combination. This was the best score in terms of BLEU among six participants in ML4HMT-12
Hierarchically Clustered Representation Learning
The joint optimization of representation learning and clustering in the
embedding space has experienced a breakthrough in recent years. In spite of the
advance, clustering with representation learning has been limited to flat-level
categories, which often involves cohesive clustering with a focus on instance
relations. To overcome the limitations of flat clustering, we introduce
hierarchically-clustered representation learning (HCRL), which simultaneously
optimizes representation learning and hierarchical clustering in the embedding
space. Compared with a few prior works, HCRL firstly attempts to consider a
generation of deep embeddings from every component of the hierarchy, not just
leaf components. In addition to obtaining hierarchically clustered embeddings,
we can reconstruct data by the various abstraction levels, infer the intrinsic
hierarchical structure, and learn the level-proportion features. We conducted
evaluations with image and text domains, and our quantitative analyses showed
competent likelihoods and the best accuracies compared with the baselines.Comment: 10 pages, 7 figures, Under review as a conference pape
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