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1 .State of the Art Natural
Language Processing at Scale
Alex Thomas David Talby
Data Scientist @ Indeed CTO @ Pacific AI
#DD4SAIS
2 . CONTENTS
ü NLU REAL-WORLD EXAMPLES
ü DOCUMENT CLASSIFICATION - WALKTHROUGH
ü STATE OF THE ART NLU IN HEALTHCARE
ü TRAIN YOUR OWN DEEP LEARNING NLU MODELS
3 . INTRODUCING SPARK NLP
• Industrial Grade NLP for the Spark ecosystem
• Design Goals:
1. Performance & Scale
2. Frictionless Reuse
3. Enterprise Grade
• Built on top of the Spark ML API’s
• Apache 2.0 licensed, with active development & support
4 .NATIVE SPARK EXTENSION
High Performance Natural Language Understanding at Scale
Part of Speech Tagger Topic Modeling
Named Entity Recognition Word2Vec
Sentiment Analysis TF-IDF
Spell Checker String distance calculation
Tokenizer N-grams calculation
Stemmer Stop word removal
Lemmatizer Train/Test & Cross-Validate
Entity Extraction Ensembles
Spark ML API (Pipeline, Transformer, Estimator)
Spark SQL API (DataFrame, Catalyst Optimizer)
Spark Core API (RDD’s, Project Tungsten)
Data Sources API
5 . FRICTIONLESS REUSE
pipeline = pyspark.ml.Pipeline(stages=[
document_assembler,
tokenizer,
stemmer, Spark NLP annotators
normalizer,
stopword_remover,
tf, Spark ML featurizers
idf,
lda]) Spark ML LDA implementation
Single execution plan for
topic_model = pipeline.fit(df)
the given data frame
6 .Case study: Demand Forecasting of Admissions from ED
Features from Structured Data Reason for visit Current wait time
Age Number of orders
• How many patients will be admitted today? Gender Admit in past 30 days
• Data Source: EPIC Clarity data Vital signs Type of insurance
7 .Case study: Demand Forecasting of Admission from ED
Features from Natural Language Text
• A majority of the rich relevant content lies in unstructured notes that
are contributed by doctors and nurses from patient interactions.
• Data Source: Emergency Department Triage notes and other ED notes
Type of Pain Symptoms
Intensity of Pain Onset of symptoms
Body part of region Attempted home
remedy
ML with NLP
ML with structured data
Accuracy Baseline: Human manual prediction
8 . Risk prediction Case Study: Detecting Sepsis
“Compared to previous work that only
used structured data such as vital signs
and demographic information, utilizing free
text drastically improves the discriminatory
ability (increase in AUC from 0.67 to 0.86)
of identifying infection.”
9 .Cohort selection Case Study: Oncology
“Using the combination of structured
and unstructured data, 8324 patients
were identified as having advanced
NSCLC.
Of these patients, only 2472 were also in
the cohort generated using structured
data only.
Further, 1090 patients who should have
been excluded based on additional data,
would be included in the structured data
only cohort.”
10 . CODE WALKTHROUGH: DOCUMENT CLASSIFICATION
• A combined NLP & ML Pipeline
• Word embeddings as features
• Training your own custom NLP models
github.com/melcutz/nlu_tutorial
11 . Tokenizer Normalizer
Different Vocabulary Lemmatizer Fact Extraction
Part of Speech Tagger Spell Checker
Different Grammar Coreference Resolution Dependency Parser
Sentence Splitting Negation Detection
Named Entity Recognition Sentiment Analysis
Different Context Intent Classification Summarization
Word Embeddings Emotion Detection
Question Answering Relevance Ranking
Different Meaning Best Next Action Translation
Different Language Models
12 .Healthcare Extensions
High Performance Natural Language Understanding at Scale
Part of Speech Tagger Topic Modeling
Named Entity Recognition Word2Vec
Sentiment Analysis TF-IDF
Spell Checker String distance calculation
Tokenizer N-grams calculation
com.johnsnowlabs.nlp.clinica Stemmer Stop word removal data.johnsnowlabs.com/healt
l.* h
Lemmatizer Train/Test & Cross-Validate
Healthcare specific Entity Extraction Ensembles 1,800+ Expert curated,
NLP annotators for clean, linked, enriched
Spark in Scala, Java & always up to date
or Python: Spark ML API (Pipeline, Transformer, Estimator) data:
• Entity Recognition Spark SQL API (DataFrame, Catalyst Optimizer) • Terminology
• Value Extraction • Providers
• Word Embeddings Spark Core API (RDD’s, Project Tungsten) • Demographics
• Assertion Status • Clinical Guidelines
• Sentiment Analysis Data Sources API • Genes
• Spell Checking, … • Measures, …
13 .Named Entity Recognition
14 . Deep Learning for NER
F-Score Dataset Task
85.81% 2010 i2b2 Medical concept extraction
92.29% 2012 i2b2 Clinical event detection
94.37% 2014 i2b2 De-identification
“Entity Recognition from Clinical Texts via Recurrent Neural Network”.
Liu et al., BMC Medical Informatics & Decision Making, July 2017.
16 . Deep Learning for Entity Resolution
F-Score Dataset Task
ShARe /
90.30% CLEF Disease & problem norm.
92.29% NCBI Disease norm. in literature
“CNN-based ranking for biomedical entity normalization”.
Li et al., BMC Bioinformatics, October 2017.
17 . Assertion Status Detection
Prescribing sick days due to diagnosis of influenza. Positive
Jane complains about flu-like symptoms. Speculative
Jane’s RIDT came back clean. Negative
Jane is at risk for flu if she’s not vaccinated. Conditional
Jane’s older brother had the flu last month. Family history
Jane had a severe case of flu last year. Patient history
18 .Deep Learning for Assertion Status Detection
Dataset Metric
94.17% Mirco-averaged F1
4th i2b2/VA
79.76% Marco-averaged F1
“Improving Classification of Medical Assertions in Clinical Notes“
Kim et al., In Proceedings of the 49th Annual Meeting of the Association for
Computational Linguistics: Human Language Technologies, 2011.
19 . USING SPARK NLP
• Homepage: https://nlp.johnsnowlabs.com
– Getting Started, Documentation, Examples, Videos, Blogs
– Join the Slack Community
• GitHub: https://github.com/johnsnowlabs/spark-nlp
– Open Issues & Feature Requests
– Contribute!
• The library has Scala and Python 2 & 3 API’s
• Get directly from maven-central or spark-packages
• Tested on all Spark 2.x versions
20 . THANK YOU!
althomas@indeed.com david@pacific.ai
in/alnith/ in/davidtalby
@davidtalby
#DD4SAIS 20
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