Name2Vec: Personal Names Embeddings
| Autor: | Luiza Antonie, Adrian d’Alessandro, Jeremy Foxcroft |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Structure (mathematical logic)
Computer science business.industry Stability (learning theory) 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Task (project management) 0202 electrical engineering electronic engineering information engineering Embedding 020201 artificial intelligence & image processing Artificial intelligence business computer Natural language processing Record linkage Word (computer architecture) 0105 earth and related environmental sciences Data integration |
| Zdroj: | Advances in Artificial Intelligence ISBN: 9783030183042 Canadian Conference on AI |
| DOI: | 10.1007/978-3-030-18305-9_52 |
| Popis: | Predicting if two names refer to the same entity is an important task for many domains, such as information retrieval, record linkage and data integration. In this paper, we propose to create name-embeddings by employing a Doc2Vec methodology, where each name is viewed as a document and each letter in the name is considered a word. Our hypothesis is that representing names as documents, with letters as words, will help capture the internal structure of names and relationships among letters. We present and discuss an experimental study where we explore the effect of various parameters, and we assess the stability of the models built for the embedding of names. Our results show that the new proposed method can predict with high accuracy when a pair of names matches. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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