Timothy Dozat

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Revisiting the now de facto standard Stanford dependency representation, we propose an improved taxonomy to capture grammatical relations across languages, including morphologically rich ones. We suggest a two-layered taxonomy: a set of broadly attested universal grammatical relations, to which language-specific relations can be added. We emphasize the(More)
This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser. We use a larger but more thoroughly regularized parser than other recent BiLSTM-based approaches, with biaffine classifiers to predict arcs and labels. Our parser gets state of the art or near state of the art performance on(More)
When attempting to improve the performance of a deep learning system, there are more or less three approaches one can take: the first is to improve the structure of the model, perhaps adding another layer, switching from simple recurrent units to LSTM cells [4], or–in the realm of NLP–taking advantage of syntactic parses (e.g. as in [13, et seq.]); another(More)
We present a gold standard annotation of syntactic dependencies in the English Web Treebank corpus using the Stanford Dependencies standard. This resource addresses the lack of a gold standard dependency treebank for English, as well as the limited availability of gold standard syntactic annotations for informal genres of English text. We also present(More)
The Stanford dependency scheme aims to provide a simple and intuitive but linguistically sound way of annotating the dependencies between words in a sentence. In this paper, we address two limitations the scheme has suffered from: First, despite providing good coverage of core grammatical relations, the scheme has not offered explicit analyses of more(More)
This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies. Our system uses relatively simple LSTM networks to produce part of speech tags and labeled dependency parses from segmented and tokenized sequences of words. In order to address the rare word problem that abounds in(More)
Revisiting the now de facto standard Stanford dependency representation, we propose an improved taxonomy to capture grammatical relations across languages, including morphologically rich ones. We suggest a two-layered taxonomy: a set of broadly attested universal grammatical relations, to which language-specific relations can be added. We emphasize the(More)
The research reported here is part of a larger project investigating similarities and differences among varieties of ellipsis constructions, including VP ellipsis, Sluicing, Pseudogapping, Gapping and NP ellipsis. Merchant (2008), and more recently Tanaka (2011), have claimed that while VP ellipsis (VPE) can tolerate a mismatch in voice between antecedent(More)
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