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Machine Learning Techniques for Text

You're reading from   Machine Learning Techniques for Text Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803242385
Length 448 pages
Edition 1st Edition
Languages
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Author (1):
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Nikos Tsourakis Nikos Tsourakis
Author Profile Icon Nikos Tsourakis
Nikos Tsourakis
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Toc

Table of Contents (13) Chapters Close

Preface 1. Chapter 1: Introducing Machine Learning for Text 2. Chapter 2: Detecting Spam Emails FREE CHAPTER 3. Chapter 3: Classifying Topics of Newsgroup Posts 4. Chapter 4: Extracting Sentiments from Product Reviews 5. Chapter 5: Recommending Music Titles 6. Chapter 6: Teaching Machines to Translate 7. Chapter 7: Summarizing Wikipedia Articles 8. Chapter 8: Detecting Hateful and Offensive Language 9. Chapter 9: Generating Text in Chatbots 10. Chapter 10: Clustering Speech-to-Text Transcriptions 11. Index 12. Other Books You May Enjoy

Preprocessing the data

The first task is to read the file with the meta-information about the corpus. The metadata.csv file includes one column with the audio filename and one with its transcription, separated by the | symbol. The relevant code is included in the text-clustering.ipynb notebook:

import pandas as pd
# Read the data from the reduced csv file.
data = pd.read_csv('./data/metadata.csv', usecols=range(2), names=['audiofile', 'transcription'], sep="|")
data.head()
>>  audiofile  transcription
0  LJ001-0001  Printing, in the only sense with which ...
1  LJ001-0002  in being comparatively modern.
2  LJ001-0003  For although the Chinese took impressio...
3  LJ001-0004  produced the block books, which were th...
4  LJ001-0005  the invention of movable metal letters ...

Unfortunately, the dataset lacks any information...

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