9,284 research outputs found
Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models
Topic models, and more specifically the class of Latent Dirichlet Allocation
(LDA), are widely used for probabilistic modeling of text. MCMC sampling from
the posterior distribution is typically performed using a collapsed Gibbs
sampler. We propose a parallel sparse partially collapsed Gibbs sampler and
compare its speed and efficiency to state-of-the-art samplers for topic models
on five well-known text corpora of differing sizes and properties. In
particular, we propose and compare two different strategies for sampling the
parameter block with latent topic indicators. The experiments show that the
increase in statistical inefficiency from only partial collapsing is smaller
than commonly assumed, and can be more than compensated by the speedup from
parallelization and sparsity on larger corpora. We also prove that the
partially collapsed samplers scale well with the size of the corpus. The
proposed algorithm is fast, efficient, exact, and can be used in more modeling
situations than the ordinary collapsed sampler.Comment: Accepted for publication in Journal of Computational and Graphical
Statistic
Cross-Lingual Adaptation using Structural Correspondence Learning
Cross-lingual adaptation, a special case of domain adaptation, refers to the
transfer of classification knowledge between two languages. In this article we
describe an extension of Structural Correspondence Learning (SCL), a recently
proposed algorithm for domain adaptation, for cross-lingual adaptation. The
proposed method uses unlabeled documents from both languages, along with a word
translation oracle, to induce cross-lingual feature correspondences. From these
correspondences a cross-lingual representation is created that enables the
transfer of classification knowledge from the source to the target language.
The main advantages of this approach over other approaches are its resource
efficiency and task specificity.
We conduct experiments in the area of cross-language topic and sentiment
classification involving English as source language and German, French, and
Japanese as target languages. The results show a significant improvement of the
proposed method over a machine translation baseline, reducing the relative
error due to cross-lingual adaptation by an average of 30% (topic
classification) and 59% (sentiment classification). We further report on
empirical analyses that reveal insights into the use of unlabeled data, the
sensitivity with respect to important hyperparameters, and the nature of the
induced cross-lingual correspondences
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings
We consider the task of inferring is-a relationships from large text corpora.
For this purpose, we propose a new method combining hyperbolic embeddings and
Hearst patterns. This approach allows us to set appropriate constraints for
inferring concept hierarchies from distributional contexts while also being
able to predict missing is-a relationships and to correct wrong extractions.
Moreover -- and in contrast with other methods -- the hierarchical nature of
hyperbolic space allows us to learn highly efficient representations and to
improve the taxonomic consistency of the inferred hierarchies. Experimentally,
we show that our approach achieves state-of-the-art performance on several
commonly-used benchmarks
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