David Belanger

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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 observed in raw text. In most previous applications of universal schema, each textual pattern is represented as a single embedding, preventing generalization to unseen patterns. Recent work employs a(More)
Accurately segmenting a citation string into fields for authors, titles, etc. is a challenging task because the output typically obeys various global constraints. Previous work has shown that modeling soft constraints , where the model is encouraged, but not require to obey the constraints, can substantially improve segmentation performance. On the other(More)
We compare the performance of two different relation prediction architectures based on the same relation predictors. The knowledge base construction architecture builds a complete knowledge base for the entire corpus, and commits to entity linking and clustering decisions ahead of time. The query-driven slot filling architecture can make entity expansion(More)
Linear chains and trees are basic building blocks in many applications of graphi-cal models, and they admit simple exact maximum a-posteriori (MAP) inference algorithms based on message passing. However, in many cases this computation is prohibitively expensive, due to quadratic dependence on variables' domain sizes. The standard algorithms are inefficient(More)
Dual decomposition provides the opportunity to build complex, yet tractable, structured prediction models using linear constraints to link together submodels that have available MAP inference routines. However, since some constraints might not hold on every single example, such models can often be improved by relaxing the requirement that these constraints(More)
We employ universal schema for slot filling and cold start. In universal schema, we allow each surface pattern from raw text, and each type defined in ontology, i.e. TACKBP slots to represent relations. And we use matrix factorization to discover implications among surface patterns and target slots. First, we identify mentions of entities from the whole(More)
Many machine learning tasks can be formulated in terms of predicting structured outputs. In frameworks such as the structured support vector machine (SVM-Struct) and the structured per-ceptron, discriminative functions are learned by iteratively applying efficient maximum a posteri-ori (MAP) decoding. However, maximum likelihood estimation (MLE) of(More)