Question Classification using Naive Bayes Classifier and Creating Missing Classes using Semantic Similarity in Question Answering System

2015 ◽  
Vol 23 (4) ◽  
pp. 155-160 ◽  
Author(s):  
Jeena Mathew ◽  
◽  
Shine N Das
Keyword(s):  
Semantic Similarity ◽  
Question Answering ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Question Answering System ◽  
Question Classification

scholarly journals Web image re-ranking using query specific in cloud computing

2018 ◽  
Vol 7 (2.21) ◽  
pp. 423
Author(s):  
U V. Anbazhagu ◽  
R Balakrishna ◽  
A Sajeev Ram ◽  
M Latha
Keyword(s):  
Question Answering ◽  
Naive Bayes ◽  
Optimal Solution ◽  
Naïve Bayes ◽  
Video Data ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Page Ranking ◽  
The Given

Question answering (QA) allows all users to get information in enhanced technique. In this project we suggest a system for inspiring textual answer with appropriate media data. Our system consists of three components Interpretation median picking, Inquiry propagation, Data pick and Launching. Interpretation median picking is used to select various types of answers. Inquiry propagation is used for extracting the root words from the given query. Data pick and Launching is used for selecting the appropriate answer and producing the result. We use Stemming algorithm, Naïve Bayes classifier algorithm and page ranking algorithms. Stemming algorithm is used to extract the root word from the given searched query. Naïve Bayes classifier algorithm is used for selecting the type of medium. By using the page ranking algorithm the optimal solution is got. Our approach automatically determines which media will be a best solution for the given query. It automatically harvests the data from website for getting the answer. Our approach can enable a novel multimedia question answering (MMQA) approach as users can find multimedia answers by matching their questions with those in the pool. We are enhancing community contributed answers. Any user who is unaware of data can get the information promptly. Our approach is to deal with the complex questions in an effective way. Based on the generated queries, we vertically collect image and video data with multimedia search engines. 

scholarly journals Determination of near-fault impulsive signals with multivariate naïve Bayes method

2021 ◽  
Author(s):  
Deniz Ertuncay ◽  
Giovanni Costa
Keyword(s):  
Naive Bayes ◽  
Strong Motion ◽  
Naïve Bayes ◽  
Strike Slip ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Earthquake Physics ◽  
Near Fault ◽  
A Site

AbstractNear-fault ground motions may contain impulse behavior on velocity records. To calculate the probability of occurrence of the impulsive signals, a large dataset is collected from various national data providers and strong motion databases. The dataset has a large number of parameters which carry information on the earthquake physics, ruptured faults, ground motion parameters, distance between the station and several parts of the ruptured fault. Relation between the parameters and impulsive signals is calculated. It is found that fault type, moment magnitude, distance and azimuth between a site of interest and the surface projection of the ruptured fault are correlated with the impulsiveness of the signals. Separate models are created for strike-slip faults and non-strike-slip faults by using multivariate naïve Bayes classifier method. Naïve Bayes classifier allows us to have the probability of observing impulsive signals. The models have comparable accuracy rates, and they are more consistent on different fault types with respect to previous studies.

Sign in / Sign up

Export Citation Format

Share Document