Entity Typing and Event Extraction
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Presentation about entity typing and event extraction in the context of CLARIAH as given at the CLARIAH tech day 2016, Utrecht 7 October 2016
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Entity Typing and Event Extraction
- 1. Entity Typing and Event Extraction Marieke van Erp http://mariekevanerp.com
- 2. VU WP3 team Isa Maks Antske Fokkens Marieke van Erp Piek Vossen
- 4. What is entity typing? • Entity typing is the task of classifying an entity mention • An entity mention is a recognised name in a text that refers to a real world person, location, organisation or other interesting ‘thing’
- 5. What is the added value of entity typing? • It allows you to query for fine-grained entity types: give me all electricians in the dataset, give me all historic buildings • Entity typing often includes linking an entity to background knowledge • The background knowledge provides additional filters: give me all politicians born after 1900 in the dataset • Caveat: the background knowledge is not complete
- 9. New synonym/concept lists are easy to plug in
- 10. New synonym/concept lists are easy to plug in
- 12. Brouwers: concept-100350 (ponstypiste) isRelatedTo class-Schrijfkunst concept-100343 (tachygraaf) isRelatedTo class-Schrijfkunst concept-100313 (schrijver) isRelatedTo class-Schrijfkunst
- 13. Brouwers: concept-100350 (ponstypiste) isRelatedTo class-Schrijfkunst concept-100343 (tachygraaf) isRelatedTo class-Schrijfkunst concept-100313 (schrijver) isRelatedTo class-Schrijfkunst
- 14. Brouwers: concept-100350 (ponstypiste) isRelatedTo class-Schrijfkunst concept-100343 (tachygraaf) isRelatedTo class-Schrijfkunst concept-100313 (schrijver) isRelatedTo class-Schrijfkunst
- 15. Named Entity Recognition & Linking • We are creating links between HISCO and Brouwers • We are building on entity and concept linkers that can recognise concepts from HISCO and Brouwers in texts • We are developing a new general purpose entity linker that allows for use of datasets other than DBpedia and is less sensitive to general entity popularity • Discovering more about Dark and NIL entities is also ongoing work (cf. Van Erp & Vossen (2016) Entity Typing using Distributional Semantics and DBpedia. To appear in: Proceedings of the 4th NLP&DBpedia workshop. Kobe, Japan 18 October 2016)
- 16. Event Extraction
- 17. Event Extraction • Event Extraction is the task of recognising and classifying mentions of ‘things that happen’ in text • Events are multifaceted: they take place at a certain time and place and have participants involved • By recognising participants, times and places, we can generate event descriptions and compare events
- 18. From words to concepts • Linking terms to synonyms to obtain a higher level of abstraction • Word-sense disambiguation + WordNet + Multilingual Central Repository + Framenet + PropBank • Stop, quit, leave, relinquish, bow out -> all linked to the concept wn:leave_office
- 19. Why link to WordNet/ConceptNet/etc? • It allows you to query for types rather than instances: give me all lawsuits in the dataset • In the context of CLARIAH, we are converting various diachronous lexicons to Linked Data • integrate resources • tag interesting concepts in text • query expansion
- 20. Semantic Role Labelling • Detecting the agent, patient, recipient and theme of a sentence • Mary sold the book to John • Agent: Mary • Recipient: John • Theme: the book
- 21. Event12 buy/sell fn:Seller fn:Commerce_money_transfer fn:Goods fn:Money fn:Buyer dbp:Porsche_fa mily dbp:QatarHolding ?Entity23 10% stake type Qatar Holding sells 10% stake in Porsche to founding families Porsche family buys back 10pc stake from Qatar http://english.alarabiya.net http://www.telegraph.co.uk 2013-06-17 sem:hasTime 2013-06-17
- 23. Event abstractions • Enable searches such as: Give me all lawsuits in which a politician was involved between 1990 and 2000. • Current developments: expand resources to the historic domain, devise new crystallisation strategies for aggregating event information
- 24. Find out more • All modules and evaluations are described in: http:// kyoto.let.vu.nl/newsreader_deliverables/NWR-D4-2-3.pdf (158 pages!) • Selection to be adapted within CLARIAH: https:// github.com/CLARIAH/wp3-semantic-parsing-Dutch • New developments: http://www.clariah.nl & https:// github.com/clariah
- 25. Discussion • It’s research software (no fancy interface) • Currently not adapted to deal with old spelling variants/OCR/ etc • NLP isn’t perfect (but humans don’t always agree either!) • What would it take for you to start using such tools? • What types of analyses are most interesting to the community? • What use cases are most useful to the community at this point in time?