Universal Schema for Entity Type Prediction

Invalid Date (modified: Jun 29, 2013) submission readers: everyone
  • Decision: conferencePoster
  • Abstract: Categorizing entities by their type is useful in many applications, such as knowledge base construction, relation extraction and query intent prediction. Fine-grained entity type ontologies are especially valuable, but typically difficult to design because of endless quandaries about level of detail and boundary cases. Automatically classifying entities by type is challenging as well, usually involving hand-labeling data and training a supervised predictor. This paper presents a {it universal schema} approach to fine-grained entity type prediction. The set of types is taken as the union of textual surface patterns (e.g. appositives) and pre-defined types from available databases (e.g. Freebase)---yielding not tens or hundreds of types, but tens of thousands of entity types, such as financier, criminologist, and musical trio. We robustly learn mutual implicature among this large union by matrix completion using embeddings learned from probabilistic matrix factorization, thus avoiding the need for hand-labeled data. Experimental results demonstrate more than 30% reduction in error versus a traditional classification approach on predicting fine-grained entities types.
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