A presentation as part of the 11 November workshop "Text Mining in Europe: Challenges and Action"

1. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
Infrastructure
crossroads
Richard Eckart de Castilho
UKP LAB
Technische Universität Darmstadt
...and the way we walked them in dkpro

2. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
PRESENTER
Dr. Richard
Eckart de Castilho
• Interoperability WP lead @ OpenMinTeD
• Technical Lead @ UKP
• Java developer
• Open source guy
• NLP software infrastructure researcher
• Apache UIMA developer
• DKPro person
@i_am_rec
https://github.com/reckart
Ubiquitous Knowledge Processing Lab
Technische Universität Darmstadt

3. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
Ubiquitous knowledge
Processing LAB
• Argumentation Mining
• Language Technology for Digital Humanities
• Lexical-Semantic Resources &Algorithms
• Text Mining & Analytics
• Writing Assistance and Language Learning
@UKPLab
http://www.ukp.tu-darmstadt.de
Prof. Dr. Iryna Gurevych
Technische Universität Darmstadt

4. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
DKPro – reuse not reinvent
• What?
• Collection of open-source projects related to NLP
• Community of communities
• Interoperability between projects
• Target group: programmers, researchers, application developers
• Why?
• Flexibility and control – liberal licensing and redistributable software
• Sustainability – open community not bound to specific grants
• Replicability – portable software distributed through repositories
• Usability – the the edge out of installation
• Projects
• DKPro Core – linguistic preprocessing, interoperable third-party tools
• DKPro TC – text classification experimentation suite
• UBY – unified semantic resource
• CSniper – integrated search and annotaton
• … https://github.io/dkpro

5. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
… but why like this?
… how else could it be done?
Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague

6. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
Analytics
• Analytics layer
• Analytics tools (tagger, parser, etc.)
• Interoperability layer
• Input/output conversion
• Tool wrappers
• Pivot data model
• Workflow layer
• Workflow descriptions
• Workflow engines
• UI layer
• Workflow editors
• Annotation editors
• Exploration / visualization
Complete!
Solution!
Analytics stack

7. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
Automatic text analysis
• pragmatic
• Gain insight about a particular field of interest
• Investigate data
• Use latest data available
• Results relevant for the moment
• No need for reproducibility
• principled
• Interest in reproducibility
• Investigate methods
• Use a fixed data set
• Results should be reproducible

8. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
Manual text analysis
• pragmatic
• Collaborative analysis
• Get as much done as quickly as possible
• All see/edit the same data / annotations
• No means of measuring quality / single truth
• Principled
• Training data for supervised machine learning
• Evaluation of automatic methods
• Distributed analysis
• Guideline-driven process
• Multiple independent analyses/annotations
• Inter-annotator agreement as quality indicator
• Human in the loop
• Analytics make suggestions / guide human
• Human input guides analystics
Human!Machine!

9. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
deployment
• Distributed / static
• Service oriented
• High network traffic
• Running cost
• Risk of decay / limited availability of older versions
• More control to providers
• Localized / dynamic
• Cloud computing
• Reduced cost
• Data locality
• Scalability
• Large freedom choosing a version
• More control to users
• Gateways
• Make dynamic setup appear static
• Handle input/output and workflow management
• Walled garden vs. convenience
Software!
Repository!
Gateway!
Gateway!

10. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
“openness”
• Open
• Liberal licensing
• Freedom to choose deployment
• Integrate custom resources/analytics
• Control to the user
• Not open/closed
• Copyleft/proprietary licensing
• Prescribed deployment
• Difficult to customize for the user
• Control to the provider

11. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
A peek at the landscape
Service-based
• ARGO*
• Pipeline builder, annotation editor
• Online platform accessible through
gateway
• Internally dynamic deployment (afaik)
• Closed source
• Weblicht / Alveo / LAPPS
• Pipeline builder
• Online platform accessible through
gateway
• Many services distributed over multiple
locations/stakeholders
• Some offer access to non-public
content/analytics
• Some are partially open source
Software-based
• DKPro Core* / ClearTK
• Component collection
• Pipeline scripting / programming
• Repository-based
• Easy to deploy/embed anywhere
• Open source
• GATE workbench*
• Pipeline builder, annotation editor,
+++
• Desktop application
• GATE Cloud
• Open source
• …

12. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
DKPro Core – Runnable example
#!/usr/bin/env groovy
@Grab(group='de.tudarmstadt.ukp.dkpro.core',
module='de.tudarmstadt.ukp.dkpro.core.opennlp-asl',
version='1.5.0')
import de.tudarmstadt.ukp.dkpro.core.opennlp.*;
import org.apache.uima.fit.factory.JCasFactory;
import org.apache.uima.fit.pipeline.SimplePipeline;
import de.tudarmstadt.ukp.dkpro.core.api.segmentation.type.*;
import de.tudarmstadt.ukp.dkpro.core.api.syntax.type.*;
import static org.apache.uima.fit.util.JCasUtil.*;
import static org.apache.uima.fit.factory.AnalysisEngineFactory.*;
def jcas = JCasFactory.createJCas();
jcas.documentText = "This is a test";
jcas.documentLanguage = "en";
SimplePipeline.runPipeline(jcas,
createEngineDescription(OpenNlpSegmenter),
createEngineDescription(OpenNlpPosTagger),
createEngineDescription(OpenNlpParser,
OpenNlpParser.PARAM_WRITE_PENN_TREE, true));
select(jcas, Token).each { println "${it.coveredText} ${it.pos.posValue}" }
select(jcas, PennTree).each { println it.pennTree }
Fetches all required!
dependencies!
No manual installation!!
Input!
Analytics pipeline.!
Language-specific!
resources fetched !
automatically!
Output!

13. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
DKPro Core – Runnable example
#!/usr/bin/env groovy
@Grab(group='de.tudarmstadt.ukp.dkpro.core',
module='de.tudarmstadt.ukp.dkpro.core.opennlp-asl',
version='1.5.0')
import de.tudarmstadt.ukp.dkpro.core.opennlp.*;
import org.apache.uima.fit.factory.JCasFactory;
import org.apache.uima.fit.pipeline.SimplePipeline;
import de.tudarmstadt.ukp.dkpro.core.api.segmentation.type.*;
import de.tudarmstadt.ukp.dkpro.core.api.syntax.type.*;
import static org.apache.uima.fit.util.JCasUtil.*;
import static org.apache.uima.fit.factory.AnalysisEngineFactory.*;
def jcas = JCasFactory.createJCas();
jcas.documentText = "This is a test";
jcas.documentLanguage = "en";
SimplePipeline.runPipeline(jcas,
createEngineDescription(OpenNlpSegmenter),
createEngineDescription(OpenNlpPosTagger),
createEngineDescription(OpenNlpParser,
OpenNlpParser.PARAM_WRITE_PENN_TREE, true));
select(jcas, Token).each { println "${it.coveredText} ${it.pos.posValue}" }
select(jcas, PennTree).each { println it.pennTree }
Fetches all required!
dependencies!
No manual installation!!
Input!
Analytics pipeline.!
Language-specific!
resources fetched !
automatically!
Output!
Why is this cool?!
This is an actual running example!!
Requires only !
JVM + Groovy (+ Internet connection)!
Easy to parallelize / scale!
Trivial to embed in applications!
Trivial to wrap as a service!

14. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
Conclusion / Challenges
• Data is growing / analytics get more complex
• Need more powerful systems to process it
• Human in the loop
• Human interaction influences analytics and vice versa
• Need to move data and analytics around
• Often conflicts with interest in protection of investment
• Need interoperability
• To discover data, resources, and analytics
• To access data and resources
• To deploy analytics
• To retrieve and further use results

15. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
What comes next?
Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague

16. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
tomorrow @ the hague: interoperability
Data
Conversion!
Analysis!
Automatic Step /!
Analysis
Component /!
Nested workflow!
Human Annotation/
Human Correction!
Resource
repository!
(Auxiliary Data)!
Data!
Source!
Data!
Sink!
Provenance!
WG1!
WG2!
WG3!
WG4!
Data
Conversion!
A
P
I!
A
P
I!
API!
Software
repository!
API!
ID / Version!
ID / Version!
New ID / Version!
Desktop / Server!
Cloud !
resource!
| | | | | | | | | | | | | | | | !
Cluster!
Portability / Scalability / Sustainability!
Analysis!
Service!
API!
Rights and restrictions aggregation!

17. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
Thanks
Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague

18. Text and Data Mining in Europe: Defining the Challenges and Actions @ The Hague
References
• Alveo http://alveo.edu.au/
• Argo http://argo.nactem.ac.uk
• CLEARTK http://cleartk.github.io/cleartk/
• DKPro https://dkpro.github.io
• Gate https://gate.ac.uk
• Lapps http://www.lappsgrid.org
• UIMA http://uima.apache.org
• Weblicht https://weblicht.sfs.uni-tuebingen.de/