Universal schema for entity type prediction

Categorizing entities by their types is useful in many applications, including knowledge base construction, relation extraction and query intent prediction. Fine-grained entity type ontologies are especially valuable, but typically difficult to design because of unavoidable 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 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 more than ten thousands of entity types, such as financier, criminologist, and musical trio. We robustly learn mutual implication among this large union by learning latent vector embeddings 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. © 2013 ACM

Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking

Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies. Previous attempts to incorporate hierarchical structure have yielded little benefit and are restricted to shallow ontologies. This paper presents new methods using real and complex bilinear mappings for integrating hierarchical information, yielding substantial improvement over flat predictions in entity linking and fine-grained entity typing, and achieving new state-of-the-art results for end-to-end models on the benchmark FIGER dataset. We also present two new human-annotated datasets containing wide and deep hierarchies which we will release to the community to encourage further research in this direction: MedMentions, a collection of PubMed abstracts in which 246k mentions have been mapped to the massive UMLS ontology; and TypeNet, which aligns Freebase types with the WordNet hierarchy to obtain nearly 2k entity types. In experiments on all three datasets we show substantial gains from hierarchy-aware training.Comment: ACL 201