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Encoding Sequential Information in Vector Space Models of Semantics: Comparing Holographic Reduced Representation and Random Permutation
Encoding information about the order in which words typically appear has been shown to improve the performance of high-dimensional semantic space models. This requires an encoding operation capable of binding together vectors in an order-sensitive way, and efficient enough to scale to large text corpora. Although both circular convolution and random permutations have been enlisted for this purpose in semantic models, these operations have never been systematically compared. In Experiment 1 we compare their storage capacity and probability of correct retrieval; in Experiments 2 and 3 we compare their performance on semantic tasks when integrated into existing models. We conclude that random permutations are a scalable alternative to circular convolution with several desirable properties
Matching Natural Language Sentences with Hierarchical Sentence Factorization
Semantic matching of natural language sentences or identifying the
relationship between two sentences is a core research problem underlying many
natural language tasks. Depending on whether training data is available, prior
research has proposed both unsupervised distance-based schemes and supervised
deep learning schemes for sentence matching. However, previous approaches
either omit or fail to fully utilize the ordered, hierarchical, and flexible
structures of language objects, as well as the interactions between them. In
this paper, we propose Hierarchical Sentence Factorization---a technique to
factorize a sentence into a hierarchical representation, with the components at
each different scale reordered into a "predicate-argument" form. The proposed
sentence factorization technique leads to the invention of: 1) a new
unsupervised distance metric which calculates the semantic distance between a
pair of text snippets by solving a penalized optimal transport problem while
preserving the logical relationship of words in the reordered sentences, and 2)
new multi-scale deep learning models for supervised semantic training, based on
factorized sentence hierarchies. We apply our techniques to text-pair
similarity estimation and text-pair relationship classification tasks, based on
multiple datasets such as STSbenchmark, the Microsoft Research paraphrase
identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments
show that the proposed hierarchical sentence factorization can be used to
significantly improve the performance of existing unsupervised distance-based
metrics as well as multiple supervised deep learning models based on the
convolutional neural network (CNN) and long short-term memory (LSTM).Comment: Accepted by WWW 2018, 10 page
Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy
Non-negative matrix factorization (NMF) has proved effective in many
clustering and classification tasks. The classic ways to measure the errors
between the original and the reconstructed matrix are distance or
Kullback-Leibler (KL) divergence. However, nonlinear cases are not properly
handled when we use these error measures. As a consequence, alternative
measures based on nonlinear kernels, such as correntropy, are proposed.
However, the current correntropy-based NMF only targets on the low-level
features without considering the intrinsic geometrical distribution of data. In
this paper, we propose a new NMF algorithm that preserves local invariance by
adding graph regularization into the process of max-correntropy-based matrix
factorization. Meanwhile, each feature can learn corresponding kernel from the
data. The experiment results of Caltech101 and Caltech256 show the benefits of
such combination against other NMF algorithms for the unsupervised image
clustering
Latent Semantic Indexing (LSI) Based Distributed System and Search On Encrypted Data
Latent semantic indexing (LSI) was initially introduced to overcome the issues of synonymy and polysemy of the traditional vector space model (VSM). LSI, however, has challenges of its own, mainly scalability. Despite being introduced in 1990, there are few attempts that provide an efficient solution for LSI, most of the literature is focuses on LSI’s applications rather than improving the original algorithm. In this work we analyze the first framework to provide scalable implementation of LSI and report its performance on the distributed environment of RAAD.
The possibility of adopting LSI in the field of searching over encrypted data is also investigated. The importance of that field is stemmed from the need for cloud computing as an effective computing paradigm that provides an affordable access to high computational power. Encryption is usually applied to prevent unauthorized access to the data (the host is assumed to be curious), however this limits accessibility to the data given that search over encryption is yet to catch with the latest techniques adopted by the Information Retrieval (IR) community. In this work we propose a system that uses LSI for indexing and free-query text for retrieving.
The results show that the available LSI framework does scale on large datasets, however it had some limitations with respect to factors like dictionary size and memory limit. When replicating the exact settings of the baseline on RAAD, it performed relatively slower. This could be resulted by the fact that RAAD uses a distributed file system or because of network latency. The results also show that the proposed system for applying LSI on encrypted data retrieved documents in the same order as the baseline (unencrypted data)
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