John Miller

Also published as: John E. Miller


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Toward Universal Dependencies for Shipibo-Konibo
Alonso Vasquez | Renzo Ego Aguirre | Candy Angulo | John Miller | Claudia Villanueva | Željko Agić | Roberto Zariquiey | Arturo Oncevay
Proceedings of the Second Workshop on Universal Dependencies (UDW 2018)

We present an initial version of the Universal Dependencies (UD) treebank for Shipibo-Konibo, the first South American, Amazonian, Panoan and Peruvian language with a resource built under UD. We describe the linguistic aspects of how the tagset was defined and the treebank was annotated; in addition we present our specific treatment of linguistic units called clitics. Although the treebank is still under development, it allowed us to perform a typological comparison against Spanish, the predominant language in Peru, and dependency syntax parsing experiments in both monolingual and cross-lingual approaches.


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Topic Model Stability for Hierarchical Summarization
John Miller | Kathleen McCoy
Proceedings of the Workshop on New Frontiers in Summarization

We envisioned responsive generic hierarchical text summarization with summaries organized by section and paragraph based on hierarchical structure topic models. But we had to be sure that topic models were stable for the sampled corpora. To that end we developed a methodology for aligning multiple hierarchical structure topic models run over the same corpus under similar conditions, calculating a representative centroid model, and reporting stability of the centroid model. We ran stability experiments for standard corpora and a development corpus of Global Warming articles. We found flat and hierarchical structures of two levels plus the root offer stable centroid models, but hierarchical structures of three levels plus the root didn’t seem stable enough for use in hierarchical summarization.

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Globally Normalized Reader
Jonathan Raiman | John Miller
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Rapid progress has been made towards question answering (QA) systems that can extract answers from text. Existing neural approaches make use of expensive bi-directional attention mechanisms or score all possible answer spans, limiting scalability. We propose instead to cast extractive QA as an iterative search problem: select the answer’s sentence, start word, and end word. This representation reduces the space of each search step and allows computation to be conditionally allocated to promising search paths. We show that globally normalizing the decision process and back-propagating through beam search makes this representation viable and learning efficient. We empirically demonstrate the benefits of this approach using our model, Globally Normalized Reader (GNR), which achieves the second highest single model performance on the Stanford Question Answering Dataset (68.4 EM, 76.21 F1 dev) and is 24.7x faster than bi-attention-flow. We also introduce a data-augmentation method to produce semantically valid examples by aligning named entities to a knowledge base and swapping them with new entities of the same type. This method improves the performance of all models considered in this work and is of independent interest for a variety of NLP tasks.


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Traversing Knowledge Graphs in Vector Space
Kelvin Guu | John Miller | Percy Liang
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing


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Experimental Design to Improve Topic Analysis Based Summarization
John Miller | Kathleen McCoy
Proceedings of the 8th International Natural Language Generation Conference (INLG)


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Adaptation of POS Tagging for Multiple BioMedical Domains
John E. Miller | Manabu Torii | K. Vijay-Shanker
Biological, translational, and clinical language processing

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Building Domain-Specific Taggers without Annotated (Domain) Data
John Miller | Manabu Torii | K. Vijay-Shanker
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)


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Rapid Adaptation of POS Tagging for Domain Specific Uses
John E. Miller | Michael Bloodgood | Manabu Torii | K. Vijay-Shanker
Proceedings of the HLT-NAACL BioNLP Workshop on Linking Natural Language and Biology