Curation Technologies for Multilingual Europe

Curation Technologies for Multilingual Europe

Georg RehmDFKI, Germany

META-FORUM 2016 – Lisbon, Portugal – 04/05 July 2016

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OutputInput SoftwareProcesses


•  Author•  Scholar•  TV editor•  Researcher•  Knowledge worker•  Investigative journalist•  Designer of an exhibition•  Curator of digital information

SectorsInput Processes Software Output

tweet analyse text processor newspaper articlenewspaper article select presentation multimedia websitewire copy focus spreadsheet tv reportfacebook status update revise email exhibition cataloguesearch result read up on browser mobile application email write groupware mashup (e.g., map)text message create sector-specific application text piececoncept research CMS concepttext file assess ECMS timelinevideo evaluate CRM studymap arrange enterprise software presentationstockphoto sort graphics/layouting software fact collectionin-house database structure IP telephony description of an exhibitcalendar entry summarise etc. analysisspreadsheet shorten etc.archive translateetc. catch up on

combineabstractintegratevisualisegenerateannotatereferenceetc.

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language and knowledge technologies

curation technologies

sector-specific technologies

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form

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sector-specific solutions!

Digital Curation Technologies•  Make curation processes in four SMEs (and sectors) more

efficient through language and knowledge technologies.•  Technology transfer project to arrive at proofs of concept. •  Curation services for real companies and real use cases.•  The human expert/curator is always in the centre and loop.•  Platform for digital curation technologies: innovation boost.



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Structure visualisation

Multilingual multimedia sources

Crossmedia recommendations

Multilingual summarisation

Event timelining

Semantification of content

Multilingual sentiment analysis

Semantic storytelling

Ontology-based knowledge structures

Automatic hyperlinking of document collections

Curation Processes Processing, exploration and

re-aggregation of domain- and task-

specific document collections.

Key Characteristics•  Technology transfer and integration project•  Broad set of tools and technologies•  Focus on building proofs of concept•  Our technologies don’t have to be perfect•  Human expert, i.e., the curator, always in the loop•  Important for all SME partners: domain-adaptability.•  WPs: Semantic Analysis, Semantic Generation,

Multilingual Technologies, Integration into Curation Tech


platform for digital curation technologies

broker REST API

curation service 1 language or knowledge

technology

curation service 2 language or knowledge

technology

client using the API

externalservice 1

externalservice 2




pipelined curation workflow


•  Curation process: e-service available through REST API.•  Services can be combined to form pipelines or workflows.•  Domain-adaptability: every curation process has a training API to create

and use domain-specific models.

Current Results•  Implemented the following baseline services:

–  NER – e-entityrecognition e-service –  Geolocation – e-entityrecognition and visualisation–  Temporal Analyser – e-entityrecognition and visualisation–  Classification – e-classification e-service–  Clustering – e-clustering e-service–  Machine Translation – e-translation e-service

•  Curation Dashboard (first prototype)•  Semantic Storytelling (work in progress)


NER, Entity Linking, Geolocation


...In the Viking colony of Iceland, an extraordinary vernacular literature blossomed in the 12th through 14th centuries......The ships were scuttled there in the 11th century, to block a navigation channel and thus protect Roskilde, then Copenhagen from seaborne assault......Viking Age inscriptions have also been discovered on the Manx runestones on the Isle of Man.…

Plain Text NIF enrichment visualisationhttp://api.digitale-kuratierung.de/api/e-nlp/namedEntityRecognition?analysis=ner http://http://dev.digitale-kuratierung.de/admini/pages/geolocalization.php

•  Currently based on OpenNLP (with NIF integration) •  Mode 1: model-based (for domains where annotated

data is available) •  Mode 2: dictionary-based (for domains where only a

list of names is available)

•  Entity Linking through SPARQL queries to DBPedia•  For locations, GPS-coordinates are retrieved,

document level average and standard deviation (over all locations) are calculated to visualise positioning of documents on a map.


NER Traininghttp://api.digitale-kuratierung.de/api/e-nlp/trainModel?analysis=dict (in the suboptimal case that only a list of terms and their URIs in an ontology is available)

http://api.digitale-kuratierung.de/api/e-nlp/trainModel?analysis=ner(if annotated training data is available)

directly usable on new input

NER model


Temporal Analysis...The ships were scuttled there in the 11th century, to block a navigation channel and thus protect Roskilde, then Copenhagen from seaborne assault......Viking Age inscriptions have also been discovered on the Manx runestones on the Isle of Man.......In the Viking colony of Iceland, an extraordinary vernacular literature blossomed in the 12th through 14th centuries…

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1600

http://api.digitale-kuratierung.de/api/e-nlp/namedEntityRecognition?analysis=temphttp://dev.digitale-kuratierung.de/admini/pages/timelining.php

Plain Text NIF enrichment visualisation

•  Sort and rank documents from a collection on chronological scale.

•  Developed rule-based system due to our focus in terms of languages (EN, DE), domain adaptability, normalisation requirements.

•  Analysis of temporal expressions in a document (or, later, paragraphs or even sentences).

•  Compute mean value for date and time, allowing positioning on a timeline.

•  Future plans: adaptability through user-specific rules.

•  Related work: SUTime, HeidelTime, Tango, Tarsgi; many papers at LREC 2016

Classification•  Mallet – Maximum Entropy Algorithm•  Algorithm for text classification, easy integration.•  Goal: text classification, i.e., assign a topic (class) to a

document (or parts of a document) to apply domain- or topic-specific NLP processing techniques.

•  Future plans: improvement of classification schema by means of new training data and additional algorithms.


@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .@prefix nif: <http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core#> .@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .<http://dkt.dfki.de/documents/#char=0,1257> a nif:RFC5147String , nif:String , nif:Context ; nif:beginIndex "0"^^xsd:nonNegativeInteger ; nif:endIndex "1257"^^xsd:nonNegativeInteger ; nif:documentClassificationLabel "Frühjahrsoffensive_1918"^^xsd:string ; nif:isString "Ceylon-Teestube B. Walther München Maximilian-Strasse 44 Gegenüber dem Königl. Hoftheater Telephon 428 München, den 26.XI.13. Von hier nach Dresden ab München 8.25 9.00 10.20 an Dresden 7.28 10.47 9.48 Sie müssen unbedingt Donnerstag hier bleiben. So können Sie doch nicht vorbeifahren. Donnerstag Abend eine interessante Uraufführung in den Kammerspielen "unseligen Gedenkens " Ich werde Billets dafür besorgen. […]"^^xsd:string .

Clustering•  WEKA (Expectation Maximisation algorithm)•  Easy integration, availability, additional algorithms.•  Goal: identification of distinct features of document collections.•  Example use case: a user has to prepare a museum exhibit on

“Birds”. Knowing which documents can be grouped can be useful to split the documents into exhibition rooms.

•  Future plans: allow users to easily recognize groups of documents in new domains and collections; faceted search.


ARFF Input JSON Output

@RELATION iris@ATTRIBUTE sepallength NUMERIC@ATTRIBUTE sepalwidth NUMERIC@ATTRIBUTE petallength NUMERIC@ATTRIBUTE petalwidth [email protected],3.5,1.4,0.24.9,3.0,1.4,0.24.7,3.2,1.3,0.24.6,3.1,1.5,0.25.0,3.6,1.4,0.25.4,3.9,1.7,0.44.6,3.4,1.4,0.35.0,3.4,1.5,0.24.4,2.9,1.4,0.24.9,3.1,1.5,0.1

{ "results": { "numberClusters": -1, "clusters": {"cluster1": { "clusterId": 1, "entitites": { "entity1": { "meanValue": 3.3099999999999996, "label": "sepalwidth" }, "entity2": { "meanValue": 1.45, "label": "petallength" }, "entity3": { "meanValue": 0.22000000000000003, "label": "petalwidth" } }}}}}

Machine Translation


Workflow

Language & Translation

Models trained on DGT, News, Europarl, TED

Herr Modi befindet sich auf einer fünftägigenReise nach Japan, um die wirtschaftlichen

Beziehungen mit der drittgrößtenWirtschaftsnation der Welt zu festigen.

Mr Modi is located on a five-day trip to Japan to strengthen the economic ties with the third largest economy in the world.

Named Entity Recognition

Entity Linking

Temporal Expressions

Metadata Processing

Post-Edit Retraining

Example

•  Robust, adaptable and customised models of MT as e-services (Moses-based SMT)•  Scenarios: museums, showrooms; news, media; publishers; cultural institutions, archives •  Integration in curation workflows with other DKT services (NER, Temporal Analyser)•  Plug-in multiple knowledge sources (Linked Data)

Semantic Storytelling•  Important objective for all partner use cases: Automatic

hyper-linking of task-specific, self-contained collections.•  Input: coherent, self-contained document collection

•  Output: processed collection with added analysis information, easily accessible as a hypertext, for efficient browsing

•  Semantic Storytelling – operates on the hypertext graph that we construct on top of the original collection

•  Enables multiple different paths through the collection

•  Semantic storytelling is the identification, ranking and recommendation of meaningful hypertext paths.



<http://d-nb.info/gnd/11858071X, met, http://d-nb.info/gnd/129094722>

http://dev.digitale-kuratierung.de/2ds3/index.php

<http://d-nb.info/gnd/118589768, wrote, http://d-nb.info/gnd/118623230><http://d-nb.info/gnd/123242231, visited, http://d-nb.info/gnd/188402519><http://d-nb.info/gnd/118569015, said, http://d-nb.info/gnd/11947509X><http://d-nb.info/gnd/119173425, was, http://d-nb.info/gnd/118629867><http://d-nb.info/gnd/119178893, designed, http://d-<nb.info/gnd/118629867><http://d-nb.info/gnd/118876759, love, http://d-nb.info/gnd/118629867><http://d-nb.info/gnd/118545892, depart, http://d-nb.info/gnd/107363569><http://d-nb.info/gnd/128830751, write, http://d-nb.info/gnd/118606026><http://d-nb.info/gnd/11858071X, protect, http://d-nb.info/gnd/39650438><http://d-nb.info/gnd/116713704, married, http://d-nb.info/gnd/52754181>…

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Curation Dashboard

Conclusions•  Curation technologies are smart technologies to support

knowledge workers handling content and knowledge.•  The multilingual Digital Single Market will create a

massive need for multilingual Curation Technologies due to an ever-increasing need for multilingual content.

•  DKT is mostly centred around German and English.•  We cater for a small set of curation processes.•  To be extended in a larger follow-up project.•  Extended set of curation processes, more complex

approaches, many more languages.


Thank you!

supported by

Curation Technologies for Multilingual Europe

Technology

Transcript of Curation Technologies for Multilingual Europe