Entity Typing Using Distributional Semantics and DBpedia
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Transcript of Entity Typing Using Distributional Semantics and DBpedia
Entity Typing Using Distributional Semantics and DBpediaMarieke van Erp and Piek Vossen
Conclusions
• Finegrained entity typing is necessary for semantic queries over text
• Search space for word2vec is large, topics help
• Combining Distributional Semantics with DBpedia can help overcome NIL and Dark Entities
https://github.com/MvanErp/entity-typing/
Dark entities: little or no information available in KB
https://github.com/MvanErp/entity-typing/
Dark entities: little or no information available in KB
https://github.com/MvanErp/entity-typing/
Distributional Semantics
• Similar concepts (denoted by words) occur in similar contexts
• Word2Vec (Mikolov et al., 2013) explores this notion in a popular implementation
SushiTeriyakiUdon
Okonomiyaki
SobaSashimi
KimonoYukataNemakiKFC
Steak
HamburgerMcDonald’s
JeansT-shirt
Skirt
Research Question:
• Can we predict the type of the concept ‘Sushi’ by modelling it in a distributional semantics space and comparing its vector to the vectors of concepts for which we do know the type?
SushiTeriyakiUdon
Okonomiyaki
SobaSashimi
KimonoYukataNemakiKFC
Steak
HamburgerMcDonald’s
JeansT-shirt
Skirt
Setup
• 7 Named Entity Linking Benchmark datasets (AIDA-YAGO, 2014 NEEL, 2015 NEEL, OKE2015, RSS500, WES2015, Wikinews)
• 3 Word2Vec models: GoogleNews, English Wikipedia, Reuters RCV1*
• Compare all entities within datasets to each other and return highest ranking type (as taken from DBpedia)
* AIDA-YAGO is part of Reuters RCV1
https://github.com/MvanErp/entity-typing/
Initial results
• Not so great?
https://github.com/MvanErp/entity-typing/
Initial results (some footnotes)
• Ranking approach favours fine-grained entity types
• The Word2Vec corpus matters! NEEL2014&2015 are derived from Tweets, typically low coverage when querying news
• Smaller datasets (Wikinews, WES2015, OKE2015) do better?
https://github.com/MvanErp/entity-typing/
Let’s zoom in on topics
• Initially, all entities within a benchmark dataset were compared to all other entities.
• What if we only compare entities from sports documents to other entities from sports documents?
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AIDA−YAGO Coarsegrained Categories GoogleNews Fine
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1001510
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AIDA−YAGO Coarsegrained Categories RCV1 Fine
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1001510
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AIDA−YAGO Coarsegrained Categories Wikipedia Fine
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AIDA−YAGO Finegrained Categories GoogleNews Fine
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AIDA−YAGO Finegrained Categories RCV1 Fine
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1001510
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AIDA−YAGO Finegrained Categories Wikipedia Fine
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1001510
https://github.com/MvanErp/entity-typing/
Conclusions and Future Work
• Difficult task, but topics help
• Ranking needs to be improved
• Multi-class classification (KFC: food & organisation, Arnold Schwarzenegger: Actor & Politician)
• What else can we discover beyond type?
https://github.com/MvanErp/entity-typing/
Thank you!
https://github.com/MvanErp/entity-typing/
This research was made possible by the CLARIAH-CORE project financed by NWO.
http://www.clariah.nl