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DTSTART:20210314T030000
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DTSTAMP:20210311T025150Z
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DTSTART;TZID=Canada/Pacific:20210226T113000
DTEND;TZID=Canada/Pacific:20210226T133000
DESCRIPTION:One of the main problems that text classification encounters is
  the cost of creating high quality labels. There are several different pro
 blems that cause labelling to be so expensive. These include needing a lar
 ge number of labelled data\, requiring more than one person to label each 
 data point\, and requiring the people that are doing the labelling to be e
 xperts in their respective fields. All of these contribute to the cost of 
 creating the labels. The most effective way to lower the cost of labelling
  while preserving the quality of the labels is to reduce the number of lab
 eled data points that are needed to create an effective classifier. One of
  the techniques to do this is known as active learning. Active learning is
  the process of purposefully selecting a subset of the available data to b
 e labeled based on the information that the classifier already has. This l
 eads to a process of repeatedly suppling the experts with small batches of
  data to be labeled. After a hopefully small number of batches the classif
 ier will have achieved the desired performance while requiring fewer label
 ed examples than a classifier created from a random selection of the data.
  I will present a current research project where we are working to create 
 an active learning process for minimizing the number of label data points 
 for the creation of a neural network classifier for emotion detection in T
 weets.\n\nSpeaker(s): Colton Aarts\, \n\nVirtual: https://events.vtools.ie
 ee.org/m/263419
LOCATION:Virtual: https://events.vtools.ieee.org/m/263419
ORGANIZER:Fan.Jiang@unbc.ca
SEQUENCE:4
SUMMARY:An Active Learning Approach for Emotion Detection
URL;VALUE=URI:https://events.vtools.ieee.org/m/263419
X-ALT-DESC:Description: <br /><p>One of the main problems that text classif
 ication encounters is the cost of creating high quality labels. There are 
 several different problems that cause labelling to be so expensive. These 
 include needing a large number of labelled data\, requiring more than one 
 person to label each data point\, and requiring the people that are doing 
 the labelling to be experts in their respective fields. All of these contr
 ibute to the cost of creating the labels. The most effective way to lower 
 the cost of labelling while preserving the quality of the labels is to red
 uce the number of labeled data points that are needed to create an effecti
 ve classifier. One of the techniques to do this is known as active learnin
 g. Active learning is the process of purposefully selecting a subset of th
 e available data to be labeled based on the information that the classifie
 r already has. This leads to a process of repeatedly suppling the experts 
 with small batches of data to be labeled. After a hopefully small number o
 f batches the classifier will have achieved the desired performance while 
 requiring fewer labeled examples than a classifier created from a random s
 election of the data. I will present a current research project where we a
 re working to create an active learning process for minimizing the number 
 of label data points for the creation of a neural network classifier for e
 motion detection in Tweets.</p>
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