42,789 research outputs found
Neural Vector Spaces for Unsupervised Information Retrieval
We propose the Neural Vector Space Model (NVSM), a method that learns
representations of documents in an unsupervised manner for news article
retrieval. In the NVSM paradigm, we learn low-dimensional representations of
words and documents from scratch using gradient descent and rank documents
according to their similarity with query representations that are composed from
word representations. We show that NVSM performs better at document ranking
than existing latent semantic vector space methods. The addition of NVSM to a
mixture of lexical language models and a state-of-the-art baseline vector space
model yields a statistically significant increase in retrieval effectiveness.
Consequently, NVSM adds a complementary relevance signal. Next to semantic
matching, we find that NVSM performs well in cases where lexical matching is
needed.
NVSM learns a notion of term specificity directly from the document
collection without feature engineering. We also show that NVSM learns
regularities related to Luhn significance. Finally, we give advice on how to
deploy NVSM in situations where model selection (e.g., cross-validation) is
infeasible. We find that an unsupervised ensemble of multiple models trained
with different hyperparameter values performs better than a single
cross-validated model. Therefore, NVSM can safely be used for ranking documents
without supervised relevance judgments.Comment: TOIS 201
Variational Deep Semantic Hashing for Text Documents
As the amount of textual data has been rapidly increasing over the past
decade, efficient similarity search methods have become a crucial component of
large-scale information retrieval systems. A popular strategy is to represent
original data samples by compact binary codes through hashing. A spectrum of
machine learning methods have been utilized, but they often lack expressiveness
and flexibility in modeling to learn effective representations. The recent
advances of deep learning in a wide range of applications has demonstrated its
capability to learn robust and powerful feature representations for complex
data. Especially, deep generative models naturally combine the expressiveness
of probabilistic generative models with the high capacity of deep neural
networks, which is very suitable for text modeling. However, little work has
leveraged the recent progress in deep learning for text hashing.
In this paper, we propose a series of novel deep document generative models
for text hashing. The first proposed model is unsupervised while the second one
is supervised by utilizing document labels/tags for hashing. The third model
further considers document-specific factors that affect the generation of
words. The probabilistic generative formulation of the proposed models provides
a principled framework for model extension, uncertainty estimation, simulation,
and interpretability. Based on variational inference and reparameterization,
the proposed models can be interpreted as encoder-decoder deep neural networks
and thus they are capable of learning complex nonlinear distributed
representations of the original documents. We conduct a comprehensive set of
experiments on four public testbeds. The experimental results have demonstrated
the effectiveness of the proposed supervised learning models for text hashing.Comment: 11 pages, 4 figure
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Hierarchical classification for multiple, distributed web databases
The proliferation of online information resources increases the importance of effective and efficient distributed searching. Our research aims to provide an alternative hierarchical categorization and search capability based on a Bayesian network learning algorithm. Our proposed approach, which is grounded on automatic textual analysis of subject content of online web databases, attempts to address the database selection problem by first classifying web databases into a hierarchy of topic categories. The experimental results reported demonstrate that such a classification approach not only effectively reduces the class search space, but also helps to significantly improve the accuracy of classification performance
TopSig: Topology Preserving Document Signatures
Performance comparisons between File Signatures and Inverted Files for text
retrieval have previously shown several significant shortcomings of file
signatures relative to inverted files. The inverted file approach underpins
most state-of-the-art search engine algorithms, such as Language and
Probabilistic models. It has been widely accepted that traditional file
signatures are inferior alternatives to inverted files. This paper describes
TopSig, a new approach to the construction of file signatures. Many advances in
semantic hashing and dimensionality reduction have been made in recent times,
but these were not so far linked to general purpose, signature file based,
search engines. This paper introduces a different signature file approach that
builds upon and extends these recent advances. We are able to demonstrate
significant improvements in the performance of signature file based indexing
and retrieval, performance that is comparable to that of state of the art
inverted file based systems, including Language models and BM25. These findings
suggest that file signatures offer a viable alternative to inverted files in
suitable settings and from the theoretical perspective it positions the file
signatures model in the class of Vector Space retrieval models.Comment: 12 pages, 8 figures, CIKM 201
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