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pro vyhledávání: '"Verga, Patrick"'
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 a
Externí odkaz:
http://arxiv.org/abs/1807.05127
Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased
Externí odkaz:
http://arxiv.org/abs/1804.08199
Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. This approach often does not consider interactions across mentions, requires redundant computatio
Externí odkaz:
http://arxiv.org/abs/1802.10569
We consider the challenging problem of entity typing over an extremely fine grained set of types, wherein a single mention or entity can have many simultaneous and often hierarchically-structured types. Despite the importance of the problem, there is
Externí odkaz:
http://arxiv.org/abs/1711.05795
Most work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. However, many relation types, particularly in biomedical text, are expressed across sentences or
Externí odkaz:
http://arxiv.org/abs/1710.08312
Today when many practitioners run basic NLP on the entire web and large-volume traffic, faster methods are paramount to saving time and energy costs. Recent advances in GPU hardware have led to the emergence of bi-directional LSTMs as a standard meth
Externí odkaz:
http://arxiv.org/abs/1702.02098
Universal schema predicts the types of entities and relations in a knowledge base (KB) by jointly embedding the union of all available schema types---not only types from multiple structured databases (such as Freebase or Wikipedia infoboxes), but als
Externí odkaz:
http://arxiv.org/abs/1606.05804
Autor:
Verga, Patrick, McCallum, Andrew
Universal schema jointly embeds knowledge bases and textual patterns to reason about entities and relations for automatic knowledge base construction and information extraction. In the past, entity pairs and relations were represented as learned vect
Externí odkaz:
http://arxiv.org/abs/1604.06361
Universal schema builds a knowledge base (KB) of entities and relations by jointly embedding all relation types from input KBs as well as textual patterns expressing relations from raw text. In most previous applications of universal schema, each tex
Externí odkaz:
http://arxiv.org/abs/1511.06396
Autor:
Pace-Schott, Edward F., Rubin, Zoe S., Tracy, Lauren E., Spencer, Rebecca M.C., Orr, Scott P., Verga, Patrick W.
Publikováno v:
In Psychiatry Research 30 October 2015 229(3):999-1010
