1 .Asoka Diggs - Data Scientist June 2018 

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3 .About Me  Lots of database design and architecture experience  Text analytics class during my Master’s degree in Predictive Analytics  Preparing text for analysis has been a theme of just about every project I’ve been involved in for the last 4+ years Information Technology 

4 .Agenda  Overview of using part-of-speech tagging to extract tokens from text  Python notebooks showing some different extraction approaches – Comparison of standard pipeline tokens to noun phrase extraction  Pseudo code example of scaling the method to a Spark cluster  Q&A Information Technology 

5 .Begin With the End in Mind  A part-of-speech based token extraction pipeline for text: – Can be straightforward to implement – Generates tokens that are more human readable – Can find phrases naturally (replacement for n-grams generation) – Removes many stopwords “for free”  Lemmatizing or singularizing terms can collapse terms that are different because of plural / singular differences Information Technology 

6 .Typical pipeline  Tokenize the text  Convert all tokens to lower case  Apply a stemmer to reduce tokens to common roots – E.g. boil, boils, boiler, boiled, boiling  boil  Remove stop words – Common English words (the, of, to, in, and, or, which)  Might also include n-gram generation Information Technology 

7 .Challenge  Standard pipeline for preparing text for analysis yields tokens that can be of low value  These tokens can be difficult to explain to business users – what do the tokens mean?  Uses many rules for handling text, and text (nearly) always has exceptions to each rule. Information Technology 

8 .Solution  Replace the standard tokenize-stem-stop-n_gram pipeline with a part-of- speech based pipeline  Extract words and phrases with the desired parts-of-speech  Use singularization as a different approach to stemming to bring singular and plural forms of words together Result is not perfect, but better quality than the tokenization approach. Information Technology 

9 .Benefit The resulting tokens naturally:  Include phrases (n-gram replacement)  Remove the standard English stop words. They have a part-of-speech that isn’t typically used in analysis  Supports readability and don’t need stemming to collapse similar words to a common root Information Technology 

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11 .Resources and Links  Pattern – For Python* 3 compatibility, I had to install a development branch of the library. See (link) for details – Something like: git clone –b development https://github.com/clips/pattern cd pattern sudo python setup.py install  NLTK  Penn-Treebank (on Wikipedia) and Tags  Intel® Distribution for Python* (link)  Others: RDRPOSTagger, spaCy, rakutenma, TextBlob Information Technology 

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