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CLTL
Software and Web
Services
Rubén Izquierdo Beviá
Rubén Izquierdo Beviá
About me
 5-year degree on Computer Science (University of
Alicante, Alicante, Spain)

 National NLP projects and 1 European project (QALLME)
(University of Alicante, Alicante, Spain)

 Thesis about NLP & Word Sense Disambiguation (University
of Alicante, Alicante, Spain. Sept 2010)

 Postdoc position at DutchSemCor Project (University of
Tilburg, Tilburg. Sept 2011-Sept2012)

 Postdoc position at OpeNER Project (Vrije
University, Amsterdam. Sept 2012-)
CLTL software
 In general common input/output format
 KAF
 NAF, as an extension of KAF

 Single components performing single tasks
 Integration of existing modules
 Adaptation of input/output formats

 Development of new ones
KAF
Kyoto Annotation Format
 Stand-off, layered, XML-based representation format





Different types of information are stored in different layers
Layers are linked by means of references
Suitable for creating pipelines based on this format
Layers:
 Text  tokens
 Term  lemmas, part-of-speech, term sentiment, word
senses
 Entities, chunks, opinions…
KAF
Kyoto Annotation Format
NAF
NewsReader Annotation Format
 Extension of KAF

 Allow the cross-document processing
 Event coreference

 ID’s are converted into valid URI’s

 Store the same type of information provided by different
tools
 Result of two different pos-taggers
How the software is provided I
 All modules are publicly available on GitHub
 CLTL GitHub
 http://github.com/cltl

 NewsReader GitHub
 http://github.com/newsreader

 OpeNER GitHub
 http://github.com/opener-project/
How the software is provided
II
 Some are available as Web Services
 Exposed as REST web services
 Accept and input stream (KAF/NAF)
 Generate an output stream (KAF/NAF)
 Easy to call from command line with CURL
 Easy to create module pipelines in the same way you create a
linux commands pipeline

 http://wordpress.let.vupr.nl/web-services/
How the software is provided
II
How the software is provided
II
Our software I
 General modules (integrated)
 Tokenizers: whitespace based, open-nlp trained...
 Sentence splitters: based on rules, open-nlp
 Pos-taggers: treetagger, open-nlp pos taggers
 Chunker: trained on Alpino data with open-nlp
 Parsers: Alpino (nl), Stanford (en)
Our software II
 General modules (developed by us)
 Wordnet Tools
 Functions to use a WordNet in LMF format

 Word Sense Disambiguation systems
 UKB: unsupersived
 SVM: supervised (for nl derived from DutchSemcor)

 Multiword tagger
 multiword sequences of terms according the WordNet

 OntoTagger
 Ontotagger inserts (semantic) labels into KAF representation on the basis
of lemma or wordnet synset representations of text
Our software III
 General modules (developed by us)
 Named Entity Recognizer
 Detects dates and locations using specific resources +
GeoNames

 KyBot
 Extract tuples and relations from a set of profiles formulated
using semantic and structural properties
Our software IV
 OpeNER related (developed by us)
 Hotel property tagger
 Detect aspects related with
cleanliness, staff, breakfast, rooms…

 Term polarity tagger
 Positive/negative terms, intensifiers, negators …
 Opinion miner
 Detect opinions: target + holder + expression
 2 rule based version // 1 machine learning version
Our software V
 NewsReader related (developed by us)
 Discourse Module
 Splits incoming texts into headers and paragraphs
 Factuality Classifier
 Classifies whether a statement is factual/probable/possible or
not

 Event Coreference
 Compares descriptions of events within and across
documents to decide if they refer to the same events.
CLTL
Software and Web
Services
Rubén Izquierdo Beviá

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CLTL Software and Web Services Guide

  • 2. Rubén Izquierdo Beviá About me  5-year degree on Computer Science (University of Alicante, Alicante, Spain)  National NLP projects and 1 European project (QALLME) (University of Alicante, Alicante, Spain)  Thesis about NLP & Word Sense Disambiguation (University of Alicante, Alicante, Spain. Sept 2010)  Postdoc position at DutchSemCor Project (University of Tilburg, Tilburg. Sept 2011-Sept2012)  Postdoc position at OpeNER Project (Vrije University, Amsterdam. Sept 2012-)
  • 3. CLTL software  In general common input/output format  KAF  NAF, as an extension of KAF  Single components performing single tasks  Integration of existing modules  Adaptation of input/output formats  Development of new ones
  • 4. KAF Kyoto Annotation Format  Stand-off, layered, XML-based representation format     Different types of information are stored in different layers Layers are linked by means of references Suitable for creating pipelines based on this format Layers:  Text  tokens  Term  lemmas, part-of-speech, term sentiment, word senses  Entities, chunks, opinions…
  • 6. NAF NewsReader Annotation Format  Extension of KAF  Allow the cross-document processing  Event coreference  ID’s are converted into valid URI’s  Store the same type of information provided by different tools  Result of two different pos-taggers
  • 7. How the software is provided I  All modules are publicly available on GitHub  CLTL GitHub  http://github.com/cltl  NewsReader GitHub  http://github.com/newsreader  OpeNER GitHub  http://github.com/opener-project/
  • 8. How the software is provided II  Some are available as Web Services  Exposed as REST web services  Accept and input stream (KAF/NAF)  Generate an output stream (KAF/NAF)  Easy to call from command line with CURL  Easy to create module pipelines in the same way you create a linux commands pipeline  http://wordpress.let.vupr.nl/web-services/
  • 9. How the software is provided II
  • 10. How the software is provided II
  • 11. Our software I  General modules (integrated)  Tokenizers: whitespace based, open-nlp trained...  Sentence splitters: based on rules, open-nlp  Pos-taggers: treetagger, open-nlp pos taggers  Chunker: trained on Alpino data with open-nlp  Parsers: Alpino (nl), Stanford (en)
  • 12. Our software II  General modules (developed by us)  Wordnet Tools  Functions to use a WordNet in LMF format  Word Sense Disambiguation systems  UKB: unsupersived  SVM: supervised (for nl derived from DutchSemcor)  Multiword tagger  multiword sequences of terms according the WordNet  OntoTagger  Ontotagger inserts (semantic) labels into KAF representation on the basis of lemma or wordnet synset representations of text
  • 13. Our software III  General modules (developed by us)  Named Entity Recognizer  Detects dates and locations using specific resources + GeoNames  KyBot  Extract tuples and relations from a set of profiles formulated using semantic and structural properties
  • 14. Our software IV  OpeNER related (developed by us)  Hotel property tagger  Detect aspects related with cleanliness, staff, breakfast, rooms…  Term polarity tagger  Positive/negative terms, intensifiers, negators …  Opinion miner  Detect opinions: target + holder + expression  2 rule based version // 1 machine learning version
  • 15. Our software V  NewsReader related (developed by us)  Discourse Module  Splits incoming texts into headers and paragraphs  Factuality Classifier  Classifies whether a statement is factual/probable/possible or not  Event Coreference  Compares descriptions of events within and across documents to decide if they refer to the same events.