1. Deriving Actionable Insights from
High Volume Media Streams
Jörn Kottmann
Peter Thygesen
Big Data Spain 2017,
Madrid

2. $WhoAreWe
Jörn Kottmann
● Senior Software Engineer, Sandstone SA, Luxembourg
● Member of Apache Software Foundation
● PMC Chair & Committer, Apache OpenNLP
● PMC and Committer, Apache UIMA
Peter Thygesen
● Senior Software Engineer & Partner, Paqle A/S, Denmark
● PMC and Committer, Apache OpenNLP

3. What is a Natural Language?

4. What is a Natural Language?
Is any language that has evolved naturally in humans through
use and repetition without conscious planning or
premeditation
(From Wikipedia)

5. What is NOT a Natural Language?

6. Characteristics of Natural Language
Unstructured
Ambiguous
Complex
Hidden semantic
Irony
Informal
Unpredictable
Rich
Most updated
Noise
Hard to search
Metaphors

7. and it holds most of human knowledge

8. and it holds most of human knowledge

9. and it holds most of human knowledge

10. As information overload grows
ever worse, computers may
become our only hope for
handling a growing deluge of
documents.
MIT Press - May 12, 2017

11. What is Natural Language Processing?
NLP is a field of computer science, artificial intelligence and
computational linguistics concerned with the interactions
between computers and human (natural) languages, and, in
particular, concerned with programming computers to fruitfully
process large natural language corpora.
(From Wikipedia)

12. ???

14. How?

15. Guten Morgen
早上好
おはようございます
Hyvää huomenta
Καλημέρα
доброе утро
शुभ भात dzień dobry
¡Buenos días!
By solving small problems each time
A pipeline where an ambiguity type is solved, incrementally.
Language Detector

16. By solving small problems each time
A pipeline where an ambiguity type is solved, incrementally.
Sentence Detector
Mr. Robert talk is today at room num. 7. Let's go?
| | | | ❌
| | ✅
Tokenizer
Mr. Robert talk is today at room num. 7. Let's go?
|| | | | | | | || || | ||| | | ❌
| | | | | | | | || | | | | | ✅

17. By solving small problems each time
Each step of a pipeline solves one ambiguity problem.
Name Finder
<Person>Washington</Person> was the first president of the USA.
<Place>Washington</Place> is a state in the Pacific Northwest region
of the USA.
POS Tagger
Laura Keene brushed by him with the glass of water .
| | | | | | | | | | |
NNP NNP VBD IN PRP IN DT NN IN NN .

18. By solving small problems each time
A pipeline can be long and resolve many ambiguities
Lemmatizer
He is better than many others
| | | | | |
He be good than many other

19. Language
Detector
Sentence
Detector
Tokenizer
POS
Tagger
Lemmatizer
Name
Finder
Chunker
Language 1
Language 2
Language N
Index
.
.
.

20. Apache OpenNLP

21. Apache OpenNLP
Mature project (> 10 years)
Actively developed
Machine learning
Java
Easy to train
Highly customizable
Fast
Language Detector
Sentence detector
Tokenizer
Part of Speech Tagger
Lemmatizer
Chunker
Parser
....

22. Language Detection
● Extract character n-grams as features, can be done for any string in any language
● Use the n-grams as features for classification via Maximum Entropy, Perceptron or
Naive Bayes
● Train on high quality data like Leipzig corpus to classify the text in more than 100
languages
Source: Language Detection Library for Java - Shuyo Nakatani

23. Training Language Detection Model
Corpus - Leipzig (https://catalog.ldc.upenn.edu/ldc2013t19)
svn co https://svn.apache.org/repos/bigdata/opennlp/trunk opennlp-corpus //(roughly 25GB)
bin/opennlp" LanguageDetectorConverter leipzig -sentencesDir data -sentencesPerSample 5
-samplesPerLanguage 2000 -encoding UTF-8 > ld-train.txt
bin/opennlp" LanguageDetectorConverter leipzig -sentencesDir data -sentencesPerSample 5
-samplesPerLanguage 2000 -samplesToSkip 2000 -encoding UTF-8 > ld-eval.txt
bin/opennlp" LanguageDetectorTrainer -model lang.bin -params MAXENT_45_PARAMS.txt -data
ld-train.txt -encoding UTF-8
bin/opennlp" LanguageDetectorEvaluator -model lang.bin -misclassified true
-reportOutputFile report.txt -data ld-eval.txt -encoding UTF-8

24. Training Models for English
Corpus - OntoNotes (https://catalog.ldc.upenn.edu/ldc2013t19)
bin/opennlp TokenNameFinderTrainer.ontonotes -lang eng -ontoNotesDir
~/opennlp-data-dir/ontonotes4/data/files/data/english/ -model en-ner-ontonotes.bin
bin/opennlp POSTaggerTrainer.ontonotes -lang eng -ontoNotesDir
~/opennlp-data-dir/ontonotes4/data/files/data/english/ -model en-pos-maxent.bin

25. Training Models for Portuguese
Corpus - Amazonia (http://www.linguateca.pt/floresta/corpus.html)
bin/opennlp TokenizerTrainer.ad -lang por -data amazonia.ad -model por-tokenizer.bin
-detokenizer lang/pt/tokenizer/pt-detokenizer.xml -encoding ISO-8859-1
bin/opennlp POSTaggerTrainer.ad -lang por -data amazonia.ad -model por-pos.bin
-encoding ISO-8859-1 -includeFeatures false
bin/opennlp ChunkerTrainerME.ad -lang por -data amazonia.ad -model por-chunk.bin
-encoding ISO-8859-1
bin/opennlp TokenNameFinderTrainer.ad -lang por -data amazonia.ad -model por-ner.bin
-encoding ISO-8859-1

26. Name Finder API - Detect Names
TokenNameFinderModel model = new TokenNameFinderModel(
OpenNLPMain.class.getResourceAsStream("/opennlp-models/por-ner.bin"));
NameFinderME nameFinder = new NameFinderME(model);
for (String document[][] : documents) {
for (String[] sentence : document) {
Span nameSpans[] = nameFinder.find(sentence);
// do something with the names
}
nameFinder.clearAdaptiveData();
}

27. Name Finder API - Train a model
ObjectStream<String> lineStream =
new PlainTextByLineStream(
new FileInputStream("en-ner-person.train"), StandardCharsets.UTF8);
TokenNameFinderModel model;
try (ObjectStream<NameSample> sampleStream = new NameSampleDataStream(lineStream))
{
model = NameFinderME.train("eng", "person", sampleStream,
TrainingParameters.defaultParams(),
newTokenNameFinderFactory());
}
model.serialize(modelFile);

28. Name Finder API - Evaluate a model
TokenNameFinderEvaluator evaluator =
new TokenNameFinderEvaluator(new NameFinderME(model));
evaluator.evaluate(sampleStream);
FMeasure result = evaluator.getFMeasure();
System.out.println(result.toString());

29. Name Finder API - Cross Evaluate a model
FileInputStream sampleDataIn = new FileInputStream("en-ner-person.train");
ObjectStream<NameSample> sampleStream = new
PlainTextByLineStream(sampleDataIn.getChannel(), StandardCharsets.UTF_8);
TokenNameFinderCrossValidator evaluator = new
TokenNameFinderCrossValidator("eng", 100, 5);
evaluator.evaluate(sampleStream, 10);
FMeasure result = evaluator.getFMeasure();
System.out.println(result.toString());

30. Apache Flink

31. Apache Flink
Mature project - 320+ contributors, > 11K commits
Very Active project on Github
Java/Scala
Streaming first
Fault-Tolerant
Unified Batch and Streaming APIs
Stateful Stream Processing

32. Apache Flink - NLP Pipeline
final StreamExecutionEnvironment env =
StreamExecutionEnvironment.getExecutionEnvironment();
DataStream<Annotation> rawStream = env.readFile(
new AnnotationInputFormat(NewsArticleAnnotationFactory.getFactory()),
parameterTool.getRequired("file"));
SplitStream<Annotation> articleStream = rawStream
.map(new LanguageDetectorFunction())
.split(new LanguageSelector(nlpLanguages));

33. Apache Flink - NLP Pipeline
articleStream.select("eng")
.map(new SentenceDetectorFunction(engSentenceModel))
.map(new TokenizerFunction(engTokenizerModel))
.map(new POSTaggerFunction(engPosModel))
.map(new ChunkerFunction(engChunkModel))
.map(new NameFinderFunction(engNerPersonModel))
.addSink(new ElasticsearchSink<>(config, transportAddresses,
new ESSinkFunction()));

34. Apache Flink - NLP Pipeline
articleStream.select("por")
.map(new SentenceDetectorFunction(porSentenceModel))
.map(new TokenizerFunction(porTokenizerModel))
.map(new POSTaggerFunction(porPosModel))
.map(new ChunkerFunction(porChunkModel))
.map(new NameFinderFunction(porNerPersonModel))
.addSink(new ElasticsearchSink<>(config, transportAddresses,
new ESSinkFunction()));

35. Apache Flink - NLP Pipeline
private static class LanguageSelector
implements OutputSelector<Tuple2<String, String>> {
public Iterable<String> select(Tuple2<String, String> s) {
List<String> list = new ArrayList<>();
list.add(languageDetectorME.predictLanguage(s.f1).getLang());
return list;
}
}

36. Apache Flink - Pos Tagger and NER
class POSTaggerMapFunction
implements RichMapFunction<Tuple2<String, String[]>, POSSample> {
…
public void open(Configuration parameters) throws Exception {
posTagger = new POSTaggerME(model);
}
public POSSample map(Tuple2<String, String[]> s) {
String[] tags = posTagger.tag(s.f1);
return new POSSample(s.f0, s.f1, tags);
}
}

37. Apache Flink - Pos Tagger and NER
class NameFinderMapFunction
implements RichMapFunction<Tuple2<String, String[]>,NameSample> {
…
public void open(Configuration parameters) throws Exception {
nameFinder = new NameFinderME(model);
}
public NameSample map(Tuple2<String, String[]> s) {
Span[] names = nameFinder.find(s.f1);
return new NameSample(s.f0, s.f1, names, null, true);
}
}

38. TODO: Add Kibana preview
screenshot

39. What’s Coming ??
● Apache MxNet: Mature Project: backed by Amazon, Apple, Intel,
NVidia
● Modular: Tensor library, reinforcement learning, ETL,..
● Focused on integrating with JVM ecosystem while
supporting state of the art like gpus on large clusters
● Implements most neural nets you’d need for language
● Named Entity Recognition using MxNet with LSTMs
● Language Detection using MxNet, short texts
● Possible: Translation using Bidirectional LSTMs with embeddings
● Computation graph architecture for more advanced use cases

40. Credits
Suneel Marthi
@suneelmarthi
Tommaso Teofili
@tteofili
William Colen
@wcolen
Rodrigo Agerri
@ragerri
Jörn Kottmann
@joernkottmann
Peter Thygesen
in:thygesen
Daniel Russ
in:daniel-russ-9541aa15
Koji Sekiguchi
@kojisays
Jeff Zemerick
in:jeffzemerick
Bruno Kinoshita
@kinow

41. Questions ???