Unifying Bayesian Inference and Vector Space Models for Improved Decipherment

2015 ◽  
Author(s):  
Qing Dou ◽  
Ashish Vaswani ◽  
Kevin Knight ◽  
Chris Dyer
Keyword(s):  
Bayesian Inference ◽  
Vector Space ◽  
Vector Space Models

scholarly journals From Frequency to Meaning: Vector Space Models of Semantics

2010 ◽  
Vol 37 ◽  
pp. 141-188 ◽  
Author(s):  
P. D. Turney ◽  
P. Pantel
Keyword(s):  
Vector Space ◽  
Open Source ◽  
Semantic Processing ◽  
Human Language ◽  
Open Source Project ◽  
Word Context ◽  
The Matrix ◽  
Vector Space Models ◽  
New Perspective

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field.

A Systematic Review on Author Identification Methods

2017 ◽  
Vol 4 (2) ◽  
pp. 81-91 ◽  
Author(s):  
Sunil Digamberrao Kale ◽  
Rajesh Shardanand Prasad
Keyword(s):  
Systematic Review ◽  
Vector Space ◽  
Probabilistic Models ◽  
Learning Models ◽  
Plagiarism Detection ◽  
Identification Methods ◽  
Hybrid Approaches ◽  
Author Identification ◽  
Meta Learning ◽  
Vector Space Models

Author Identification is a technique for identifying author of anonymous text. It has near about 130 year's long history, started with the work by Mendenhall 1987. Applications of Author identification include plagiarism detection, detecting anonymous author, in forensics and so on. In this paper the authors outline features used for Author identification like vocabulary, syntactic and others. Researchers worked on various methods for Author identification they also outline this paper on types of Author Identification methods that include 1. Profile-based Approaches which includes Probabilistic Models, Compression Models, Common n-Grams (CNG) approach, 2. Instance-based Approaches which includes Vector Space Models, Similarity-based Models, Meta-learning Models and 3. Hybrid Approaches. At the end the authors conclude this paper with observations and future scope.

Learned vector-space models for document retrieval

1995 ◽  
Vol 31 (3) ◽  
pp. 419-429 ◽  
Author(s):  
William R. Caid ◽  
Susan T. Dumais ◽  
Stephen I. Gallant
Keyword(s):  
Vector Space ◽  
Document Retrieval ◽  
Vector Space Models

scholarly journals Research on Multi-Attribute Decision-Making in Condition-Based Maintenance for Power Transformers Based on Cloud and Kernel Vector Space Models

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5948
Author(s):  
Renxi Gong ◽  
Siqiang Li ◽  
Weiyu Peng
Keyword(s):  
Decision Making ◽  
Vector Space ◽  
Assessment System ◽  
Condition Based Maintenance ◽  
Power Transformers ◽  
Maintenance Strategy ◽  
Cloud Models ◽  
Optimal Maintenance ◽  
Maintenance Decision ◽  
Vector Space Models

Decision-making for the condition-based maintenance (CBM) of power transformers is critical to their sustainable operation. Existing research exhibits significant shortcomings; neither group decision-making nor maintenance intention is considered, which does not satisfy the needs of smart grids. Thus, a multivariate assessment system, which includes the consideration of technology, cost-effectiveness, and security, should be created, taking into account current research findings. In order to address the uncertainty of maintenance strategy selection, this paper proposes a maintenance decision-making model composed of cloud and vector space models. The optimal maintenance strategy is selected in a multivariate assessment system. Cloud models allow for the expression of natural language evaluation information and are used to transform qualitative concepts into quantitative expressions. The subjective and objective weights of the evaluation index are derived from the analytic hierarchy process and the grey relational analysis method, respectively. The kernel vector space model is then used to select the best maintenance strategy through the close degree calculation. Finally, an optimal maintenance strategy is determined. A comparison and analysis of three different representative maintenance strategies resulted in the following findings: The proposed model is effective; it provides a new decision-making method for power transformer maintenance decision-making; it is simple, practical, and easy to combine with the traditional state assessment method, and thus should play a role in transformer fault diagnosis.

Sign in / Sign up

Export Citation Format

Share Document