Greg Durrett

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We describe a supervised approach to predicting the set of all inflected forms of a lexical item. Our system automatically acquires the orthographic transformation rules of morphological paradigms from labeled examples, and then learns the contexts in which those transformations apply using a discriminative sequence model. Because our approach is completely(More)
We consider the problem of using a bilingual dictionary to transfer lexico-syntactic information from a resource-rich source language to a resource-poor target language. In contrast to past work that used bitexts to transfer analyses of specific sentences at the token level, we instead use features to transfer the behavior of words at a type level. In a(More)
This paper describes a parsing model that combines the exact dynamic programming of CRF parsing with the rich nonlinear featurization of neural net approaches. Our model is structurally a CRF that factors over anchored rule productions, but instead of linear potential functions based on sparse features, we use nonlinear potentials computed via a feedforward(More)
We present a parser that relies primarily on extracting information directly from surface spans rather than on propagating information through enriched grammar structure. For example, instead of creating separate grammar symbols to mark the definiteness of an NP, our parser might instead capture the same information from the first word of the NP. Moving(More)
A key challenge in entity linking is making effective use of contextual information to disambiguate mentions that might refer to different entities in different contexts. We present a model that uses convolutional neural networks to capture semantic correspondence between a mention’s context and a proposed target entity. These convolutional networks operate(More)
We present a discriminative model for detecting disfluencies in spoken language transcripts. Structurally, our model is a semiMarkov conditional random field with features targeting characteristics unique to speech repairs. This gives a significant performance improvement over standard chain-structured CRFs that have been employed in past work. We then(More)
Efficiently incorporating entity-level information is a challenge for coreference resolution systems due to the difficulty of exact inference over partitions. We describe an end-to-end discriminative probabilistic model for coreference that, along with standard pairwise features, enforces structural agreement constraints between specified properties of(More)
Analyzing the computational complexity of evolutionary algorithms (EAs) for binary search spaces has significantly informed our understanding of EAs in general. With this paper, we start the computational complexity analysis of genetic programming (GP). We set up several simplified GP algorithms and analyze them on two separable model problems, ORDER and(More)