We introduce in this paper a method to anonymize document vector spaces. These vector spaces can be used to analyze confidential documents without disclosing private information. The method is inspired in microaggregation, a popular technique used in statistical disclosure control. URL http://doi.acm.org/10.1145/2480362.2480543 [9] Source URL: https://www.iiia.csic.es/en/node/54488 Links [1] ht...

In this study, we propose to use an Adaptive Genetic Algorithm (AGA) aimed to enhance the performance of information retrieval under Vector Space Model (VSM) in both (Cosine and Dice similarity). Using the algorithm is aimed to improve the quality of the results of user's query and generate improved queries that fit searcher’s needs. Furthermore, we investigate and evaluate different fitness fu...

subvector weights Article title subvector weights

This paper addresses the classification of linked entities. We introduce a relational vectnr-space (VS) model (in analogy to the VS model used in information retrieval) that abstracts the linked structure, representing entities by vectors of weights. Given labeled data as background knowledgdtraining data, classification procedures can be defined for this model, including a straightforward, "di...

OBJECTIVE The relationship between diseases and their causative genes can be complex, especially in the case of polygenic diseases. Further exacerbating the challenges in their study is that many genes may be causally related to multiple diseases. This study explored the relationship between diseases through the adaptation of an approach pioneered in the context of information retrieval: vector...

A new method for content-based image retrieval is being presented. This method uses a vector-space model to represent images in a multi-dimensional space. This model allows the use of multiple attributes in the retrieval process and also identiies the most selective values for each attribute. Therefore by ignoring the less signiicant values the user can reduce the dimensionality of the feature ...

In the Vector Space Model (VSM) of Information Retrieval (IR), the documents and queries are conceived as vectors of weights belonging to the same linear space, and retrieval is based on similarity measures. This view yields paradoxes and problems as regards concepts of fundamental importance to IR (similarity, term independence, preference, linearity). In the present paper, a different view is...

A Hille-Yosida Theorem is proved on convenient vector spaces, a class, which contains all sequentially complete locally convex spaces. The approach is governed by convenient analysis and the credo that many reasonable questions concerning strongly continuous semigroups can be proved on the subspace of smooth vectors. Examples from literature are reconsidered by these simpler methods and some ap...

Due to different aspects that emotion-oriented research looks to capture, the emotion taxonomy used often differs among research efforts. Therefore, it is hard to coordinate the research efforts using different emotion taxonomies. On the other hand, due to the multiplicity of “emotion”, emotion annotations more naturally fit the paradigm of multi-label classification since one instance (such as...

We present a systematic study of parameters used in the construction of semantic vector space models. Evaluation is carried out on a variety of similarity tasks, including a compositionality dataset, using several source corpora. In addition to recommendations for optimal parameters, we present some novel findings, including a similarity metric that outperforms the alternatives on all tasks con...