Open AccessProceedings Article
Universal Stanford dependencies: A cross-linguistic typology
Marie-Catherine de Marneffe,Timothy Dozat,Natalia Silveira,Katri Haverinen,Filip Ginter,Joakim Nivre,Christopher D. Manning +6 more
- pp 4585-4592
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TLDR
This work proposes a two-layered taxonomy: a set of broadly attested universal grammatical relations, to which language-specific relations can be added, and a lexicalist stance of the Stanford Dependencies, which leads to a particular, partially new treatment of compounding, prepositions, and morphology.Abstract:
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 lexicalist stance of the Stanford Dependencies, which leads to a particular, partially new treatment of compounding, prepositions, and morphology. We show how existing dependency schemes for several languages map onto the universal taxonomy proposed here and close with consideration of practical implications of dependency representation choices for NLP applications, in particular parsing.read more
Citations
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Proceedings Article
Universal Dependencies v1: A Multilingual Treebank Collection
Joakim Nivre,Marie-Catherine de Marneffe,Filip Ginter,Yoav Goldberg,Jan Hajič,Christopher D. Manning,Ryan McDonald,Slav Petrov,Sampo Pyysalo,Natalia Silveira,Reut Tsarfaty,Daniel Zeman +11 more
TL;DR: This paper describes v1 of the universal guidelines, the underlying design principles, and the currently available treebanks for 33 languages, as well as highlighting the needs for sound comparative evaluation and cross-lingual learning experiments.
Journal ArticleDOI
CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin,Pranav Rajpurkar,Michael Ko,Yifan Yu,Silviana Ciurea-Ilcus,Christopher G. Chute,Henrik Marklund,Behzad Haghgoo,Robyn L. Ball,Katie Shpanskaya,Jayne Seekins,David A. Mong,Safwan Halabi,Jesse K. Sandberg,Ricky Jones,David B. Larson,Curtis P. Langlotz,Bhavik N. Patel,Matthew P. Lungren,Andrew Y. Ng +19 more
TL;DR: CheXpert as discussed by the authors is a large dataset of chest radiographs of 65,240 patients annotated by 3 board-certified radiologists with 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation.
Book ChapterDOI
SPICE: Semantic Propositional Image Caption Evaluation
TL;DR: This paper proposed a new automated caption evaluation metric defined over scene graphs coined SPICE, which captures human judgments over model-generated captions better than other automatic metrics (e.g., system-level correlation of 0.88 with human judgments on the MS COCO dataset, versus 0.43 for CIDEr and 0.53 for METEOR).
Posted Content
SPICE: Semantic Propositional Image Caption Evaluation
TL;DR: It is hypothesized that semantic propositional content is an important component of human caption evaluation, and a new automated caption evaluation metric defined over scene graphs coined SPICE is proposed, which can answer questions such as which caption-generator best understands colors?
Posted Content
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
Jeremy Irvin,Pranav Rajpurkar,Michael Ko,Yifan Yu,Silviana Ciurea-Ilcus,Christopher G. Chute,Henrik Marklund,Behzad Haghgoo,Robyn L. Ball,Katie Shpanskaya,Jayne Seekins,David A. Mong,Safwan Halabi,Jesse K. Sandberg,Ricky Jones,David B. Larson,Curtis P. Langlotz,Bhavik N. Patel,Matthew P. Lungren,Andrew Y. Ng +19 more
TL;DR: CheXpert as discussed by the authors is a large dataset of chest radiographs of 65,240 patients annotated by 3 board-certified radiologists with 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation and different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs.
References
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Accurate Unlexicalized Parsing
Dan Klein,Christopher D. Manning +1 more
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