As the applications we build are increasingly driven by text, doing data ingestion, management, loading, and preprocessing in a robust, organized, parallel, and memory-safe way can get tricky. This talk walks through the highs (a custom billion-word corpus!), the lows (segfaults, 400 errors, pesky mp3s), and the new Python libraries we built to ingest and preprocess text for machine learning. While applications like Siri, Cortana, and Alexa may still seem like novelties, language-aware applications are rapidly becoming the new norm. Under the hood, these applications take in text data as input, parse it into composite parts, compute upon those composites, and then recombine them to deliver a meaningful and tailored end result. The best applications use language models trained on domain-specific corpora (collections of related documents containing natural language) that reduce ambiguity and prediction space to make results more intelligible. Here's the catch: these corpora are huge, generally consisting of at least hundreds of gigabytes of data inside of thousands of documents, and often more! In this talk, we'll see how working with text data is substantially different from working with numeric data, and show that ingesting a raw text corpus in a form that will support the construction of a data product is no trivial task. For instance, when dealing with a text corpus, you have to consider not only how the data comes in (e.g. respecting rate limits, terms of use, etc.), but also where to store the data and how to keep it organized. Because the data comes from the web, it's often unpredictable, containing not only text but audio files, ads, videos, and other kinds of web detritus. Since the datasets are large, you need to anticipate potential performance problems and ensure memory safety through streaming data loading and multiprocessing. Finally, in anticipation of the machine learning components, you have to establish a standardized method of transforming your raw ingested text into a corpus that's ready for computation and modeling. In this talk, we'll explore many of the challenges we experienced along the way and introduce two Python packages that make this work a bit easier: Baleen and Minke. Baleen is a package for ingesting formal natural language data from the discourse of professional and amateur writers, like bloggers and news outlets, in a categorized fashion. Minke extends Baleen with a library that performs parallel data loading, preprocessing, normalization, and keyphrase extraction to support machine learning on a large-scale custom corpus.

1. Building a Gigaword Corpus
Data Ingestion, Management, and Processing for NLP
Rebecca Bilbro
PyCon 2017

2. ● Me and my motivation
● Why make a custom corpus?
● Things likely to go wrong
○ Ingestion
○ Management
○ Loading
○ Preprocessing
○ Analysis
● Lessons we learned
● Open source tools we made

3. Rebecca Bilbro
Data Scientist

4. Yellowbrick

6. Natural language
processing

7. Everyone’s doing it
NLTK
So many great tools

8. The Natural Language Toolkit
import nltk
moby = nltk.text.Text(nltk.corpus.gutenberg.words('melville-moby_dick.txt'))
print(moby.similar("ahab"))
print(moby.common_contexts(["ahab", "starbuck"]))
print(moby.concordance("monstrous", 55, lines=10))

9. Gensim + Wikipedia
import bz2
import gensim
# Load id to word dictionary
id2word = gensim.corpora.Dictionary.load_from_text('wikipedia_wordids.txt')
# Instantiate iterator for corpus (which is ~24.14 GB on disk after compression!)
mm = gensim.corpora.MmCorpus(bz2.BZ2File('wikipedia_tfidf.mm.bz2'))
# Do latent Semantic Analysis and find 10 prominent topics
lsa = gensim.models.lsimodel.LsiModel(corpus=mm, id2word=id2word, num_topics=400)
lsa.print_topics(10)

10. A custom corpus

11. RSS
import os
import requests
import feedparser
feed = "http://feeds.washingtonpost.com/rss/national"
for entry in feedparser.parse(feed)['entries']:
r = requests.get(entry['link'])
path = entry['title'].lower().replace(" ", "-") + ".html"
with open(path, 'wb') as f:
f.write(r.content)

12. Ingestion
● Scheduling
● Adding new feeds
● Synchronizing feeds, finding duplicates
● Parsing different feeds/entries into a
standard form
● Monitoring
Storage
● Database choice
● Data representation, indexing, fetching
● Connection and configuration
● Error tracking and handling
● Exporting

13. And as the corpus began
to grow …
… new questions arose about costs
(storage, time) and surprising results
(videos?).

14. Post
+ title
+ url
+ content
+ hash()
+ htmlize()
Feed
+ title
+ link
+ active
OPML Reader
+ categories()
+ counts()
+ __iter__()
+ __len__()
ingest()
Configuration
+ logging
+ database
+ flags
Exporter
+ export()
+ readme()
Admin
+ ingest_feeds()
+ ingest_opml()
+ summary()
+ run()
+ export()
Utilities
+ timez
Logging
+ logger
+ mongolog
Ingest
+ feeds()
+ started()
+ finished()
+ process()
+ ingest()
Feed Sync
+ parse()
+ sync()
+ entries()
Post Wrangler
+ wrangle()
+ fetch()
connect()
<OPML />
Production-grade
ingestion:
Baleen

15. Raw corpus != Usable data

16. From each doc, extract html, identify paras/sents/words, tag with part-of-speech
Raw Corpus
HTML
corpus = [(‘How’, ’WRB’),
(‘long’, ‘RB’),
(‘will’, ‘MD’),
(‘this’, ‘DT’),
(‘go’, ‘VB’),
(‘on’, ‘IN’),
(‘?’, ‘.’),
...
]
Paras
Sents
Tokens
Tags

17. Streaming
Corpus Preprocessing
Tokenized
Corpus
CorpusReader for streaming access, preprocessing, and saving the tokenized version
HTML
Paras
Sents
Tokens
Tags
Raw
Corpus

18. Vectorization
...so many features

19. Visualize top tokens,
document distribution
& part-of-speech
tagging
Yellowbrick

20. Data Loader
Text
Normalization
Text
Vectorization
Feature
Transformation
Estimator
Data Loader
Feature Union Pipeline
Estimator
Text
Normalization
Document
Features
Text Extraction
Summary
Vectorization
Article
Vectorization
Concept Features
Metadata Features
Dict Vectorizer
Minke

21. Dynamic graph
analysis

22. 1.5 M documents, 7,500 jobs, 524 GB (uncompressed)
Keyphrase Graph:
- 2.7 M nodes
- 47 M edges
- Average
degree of 35

23. Lessons learned

24. Meaningfully literate data products rely
on…
...a custom, domain-specific corpus.

25. Meaningfully literate data products rely
on…
...a data management layer for flexibility
and iteration during
modeling.
Feature
Analysis
Algorithm
Selection
Hyperparameter
Tuning

26. corpus
├── citation.bib
├── feeds.json
├── LICENSE.md
├── manifest.json
├── README.md
└── books
├── 56d629e7c1808113ffb87eaf.html
├── 56d629e7c1808113ffb87eb3.html
└── 56d629ebc1808113ffb87ed0.html
└── business
├── 56d625d5c1808113ffb87730.html
├── 56d625d6c1808113ffb87736.html
└── 56d625ddc1808113ffb87752.html
└── cinema
├── 56d629b5c1808113ffb87d8f.html
├── 56d629b5c1808113ffb87d93.html
└── 56d629b6c1808113ffb87d9a.html
└── cooking
├── 56d62af2c1808113ffb880ec.html
├── 56d62af2c1808113ffb880ee.html
└── 56d62af2c1808113ffb880fa.html
Preprocessing
Transformer
Raw
CorpusReader
Tokenized
Corpus
Post-processed
CorpusReader
Meaningfully literate data products rely
on…
...a custom CorpusReader for streaming,
and also intermediate storage.

27. Meaningfully literate data products rely
on…
...visual steering and
graph analysis for
interpretation.

28. Corpus
Processing
Extract noun
keyphrases
weighted by
TF-IDF.
Corpus
Ingestion
Routine
Document
Collection
Every Hour

29. Baleen & Minke

30. Yellowbrick

31. Getting data
● (Tutorial) “Fantastic Data and Where to Find Them” by Nicole
Donnelly
● (Poster) “On the Hour Data Ingestion” by Benjamin Bengfort and
Will Voorhees
Speed to insight
● (Talk) “Human-Machine Collaboration” by Tony Ojeda
● (Poster) “A Framework for Exploratory Data Analysis” by Tony
Ojeda and Sasan Bahadaran
Machine learning
● (Poster) “Model Management Systems” by Benjamin Bengfort
and Laura Lorenz
● (Poster) “Yellowbrick” by Benjamin Bengfort and Rebecca Bilbro
Also, sprints!
pycon.districtdatalabs.com

32. Thank you!
Rebecca Bilbro
Twitter: twitter.com/rebeccabilbro
Github: github.com/rebeccabilbro
Email: rebecca.bilbro@bytecubed.com