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Using PyStruct to Classify MOOC Discussion Forum Posts
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Marks
Author(s):
Kyle Shaffer
Location
Room CR6
Date
jul Sat 16
Days Raw Files
Start
14:15
First Raw Start
12:50
Duration
00:55:00
Offset
1:24:11
End
15:10
Last Raw End
14:20
Chapters
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0:06:01
0:06:07
0:28:50
0:29:59
0:35:15
0:35:18
0:35:21
0:35:23
0:59:59
1:25:02
Total cuts_time
89 min.
https://pygotham.org/2015/talks/282/using-pystruct-to-classify-mooc-discussion-forum-posts
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This talk will detail how I approached the task of classifying discussion forum posts in a massive open online course (MOOC). In particular, the talk will focus on using the PyStruct library as a tool to implement a conditional random field for this problem. Many linear classifiers have been shown to be effective at text classification, however discussion forums present a distinct sequential structure that can be leveraged. This makes this problem an excellent candidate for the application of structured prediction models. After presenting the study background, a brief overview of structured prediction will be presented focusing on the PyStruct library. In addition to presenting how PyStruct was used for this task, I will also present an overview with code examples for constructing features that were fed to the models and will detail any Python tools (such as scikit-learn and NLTK) used for these tasks. The results of the study show that framing this as a structured prediction task leads to substantial improvements in precision and F1 scores compared with a linear classifier.
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