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Entity Typing and Event Extraction
The document discusses entity typing and event extraction, which involve classifying entities and recognizing events in text. It highlights the value of linking entities to background knowledge for refined querying and the ability to generate event descriptions through an understanding of participants and contexts. It also outlines ongoing research and the integration of various resources for semantic analysis and querying capabilities.
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Entity Typing and Event Extraction
- 1. Entity Typing andEvent Extraction Marieke van Erp http://mariekevanerp.com
- 2. VU WP3 team IsaMaks Antske Fokkens Marieke van Erp Piek Vossen
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- 4. What is entitytyping? • 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 theadded 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 listsare easy to plug in
- 10. New synonym/concept listsare 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)
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- 17. Event Extraction • EventExtraction 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 toconcepts • 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 toWordNet/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 • Enablesearches 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 researchsoftware (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?