May 5, 2019

1124 words 6 mins read

Paper Group NANR 15

Using Language Groundings for Context-Sensitive Text Prediction. Low-resource OCR error detection and correction in French Clinical Texts. Identifying and Categorizing Disaster-Related Tweets. SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2). KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and Answers. Exp …

Using Language Groundings for Context-Sensitive Text Prediction


Title	Using Language Groundings for Context-Sensitive Text Prediction
Authors	Timothy Lewis, Cynthia Matuszek, Amy Hurst, Matthew Taylor
Abstract
Tasks	Language Modelling, Text Generation
Published	2016-11-01
URL	https://www.aclweb.org/anthology/W16-6011/
PDF	https://www.aclweb.org/anthology/W16-6011
PWC	https://paperswithcode.com/paper/using-language-groundings-for-context
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Low-resource OCR error detection and correction in French Clinical Texts


Title	Low-resource OCR error detection and correction in French Clinical Texts
Authors	Eva D{'}hondt, Cyril Grouin, Brigitte Grau
Abstract
Tasks	Language Modelling, Optical Character Recognition
Published	2016-11-01
URL	https://www.aclweb.org/anthology/W16-6108/
PDF	https://www.aclweb.org/anthology/W16-6108
PWC	https://paperswithcode.com/paper/low-resource-ocr-error-detection-and
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Title	Identifying and Categorizing Disaster-Related Tweets
Authors	Kevin Stowe, Michael J. Paul, Martha Palmer, Leysia Palen, Kenneth Anderson
Abstract
Tasks	Decision Making
Published	2016-11-01
URL	https://www.aclweb.org/anthology/W16-6201/
PDF	https://www.aclweb.org/anthology/W16-6201
PWC	https://paperswithcode.com/paper/identifying-and-categorizing-disaster-related
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SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2)


Title	SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2)
Authors	Georgeta Bordea, Els Lefever, Paul Buitelaar
Abstract
Tasks	Natural Language Inference, Question Answering
Published	2016-06-01
URL	https://www.aclweb.org/anthology/S16-1168/
PDF	https://www.aclweb.org/anthology/S16-1168
PWC	https://paperswithcode.com/paper/semeval-2016-task-13-taxonomy-extraction
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KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and Answers


Title	KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and Answers
Authors	Simone Filice, Danilo Croce, Aless Moschitti, ro, Roberto Basili
Abstract
Tasks	Community Question Answering, Question Answering, Word Embeddings
Published	2016-06-01
URL	https://www.aclweb.org/anthology/S16-1172/
PDF	https://www.aclweb.org/anthology/S16-1172
PWC	https://paperswithcode.com/paper/kelp-at-semeval-2016-task-3-learning-semantic
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Explicit Causal Connections between the Acquisition of Linguistic Tiers: Evidence from Dynamical Systems Modeling


Title	Explicit Causal Connections between the Acquisition of Linguistic Tiers: Evidence from Dynamical Systems Modeling
Authors	Daniel Spokoyny, Jeremy Irvin, Fermin Moscoso del Prado Martin
Abstract
Tasks
Published	2016-08-01
URL	https://www.aclweb.org/anthology/W16-1910/
PDF	https://www.aclweb.org/anthology/W16-1910
PWC	https://paperswithcode.com/paper/explicit-causal-connections-between-the
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DynamicPower at SemEval-2016 Task 8: Processing syntactic parse trees with a Dynamic Semantics core


Title	DynamicPower at SemEval-2016 Task 8: Processing syntactic parse trees with a Dynamic Semantics core
Authors	Alastair Butler
Abstract
Tasks
Published	2016-06-01
URL	https://www.aclweb.org/anthology/S16-1177/
PDF	https://www.aclweb.org/anthology/S16-1177
PWC	https://paperswithcode.com/paper/dynamicpower-at-semeval-2016-task-8
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M2L at SemEval-2016 Task 8: AMR Parsing with Neural Networks


Title	M2L at SemEval-2016 Task 8: AMR Parsing with Neural Networks
Authors	Yevgeniy Puzikov, Daisuke Kawahara, Sadao Kurohashi
Abstract
Tasks	Amr Parsing, Dependency Parsing, Transition-Based Dependency Parsing
Published	2016-06-01
URL	https://www.aclweb.org/anthology/S16-1178/
PDF	https://www.aclweb.org/anthology/S16-1178
PWC	https://paperswithcode.com/paper/m2l-at-semeval-2016-task-8-amr-parsing-with
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Universal Dependencies for Japanese


Title	Universal Dependencies for Japanese
Authors	Takaaki Tanaka, Yusuke Miyao, Masayuki Asahara, Sumire Uematsu, Hiroshi Kanayama, Shinsuke Mori, Yuji Matsumoto
Abstract	We present an attempt to port the international syntactic annotation scheme, Universal Dependencies, to the Japanese language in this paper. Since the Japanese syntactic structure is usually annotated on the basis of unique chunk-based dependencies, we first introduce word-based dependencies by using a word unit called the Short Unit Word, which usually corresponds to an entry in the lexicon UniDic. Porting is done by mapping the part-of-speech tagset in UniDic to the universal part-of-speech tagset, and converting a constituent-based treebank to a typed dependency tree. The conversion is not straightforward, and we discuss the problems that arose in the conversion and the current solutions. A treebank consisting of 10,000 sentences was built by converting the existent resources and currently released to the public.
Tasks
Published	2016-05-01
URL	https://www.aclweb.org/anthology/L16-1261/
PDF	https://www.aclweb.org/anthology/L16-1261
PWC	https://paperswithcode.com/paper/universal-dependencies-for-japanese
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Biomolecular Event Extraction using a Stacked Generalization based Classifier


Title	Biomolecular Event Extraction using a Stacked Generalization based Classifier
Authors	Amit Majumder, Asif Ekbal, Sudip Kumar Naskar
Abstract
Tasks	Edge Detection, Information Retrieval, Named Entity Recognition, Relation Extraction
Published	2016-12-01
URL	https://www.aclweb.org/anthology/W16-6308/
PDF	https://www.aclweb.org/anthology/W16-6308
PWC	https://paperswithcode.com/paper/biomolecular-event-extraction-using-a-stacked
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Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics


Title	Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics
Authors	Wei-Shou Hsu, Pascal Poupart
Abstract	Latent Dirichlet Allocation (LDA) is a very popular model for topic modeling as well as many other problems with latent groups. It is both simple and effective. When the number of topics (or latent groups) is unknown, the Hierarchical Dirichlet Process (HDP) provides an elegant non-parametric extension; however, it is a complex model and it is difficult to incorporate prior knowledge since the distribution over topics is implicit. We propose two new models that extend LDA in a simple and intuitive fashion by directly expressing a distribution over the number of topics. We also propose a new online Bayesian moment matching technique to learn the parameters and the number of topics of those models based on streaming data. The approach achieves higher log-likelihood than batch and online HDP with fixed hyperparameters on several corpora.
Tasks
Published	2016-12-01
URL	http://papers.nips.cc/paper/6077-online-bayesian-moment-matching-for-topic-modeling-with-unknown-number-of-topics
PDF	http://papers.nips.cc/paper/6077-online-bayesian-moment-matching-for-topic-modeling-with-unknown-number-of-topics.pdf
PWC	https://paperswithcode.com/paper/online-bayesian-moment-matching-for-topic
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A Recurrent Neural Network Architecture for De-identifying Clinical Records


Title	A Recurrent Neural Network Architecture for De-identifying Clinical Records
Authors	{Shweta}, Ankit Kumar, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya
Abstract
Tasks	Named Entity Recognition
Published	2016-12-01
URL	https://www.aclweb.org/anthology/W16-6325/
PDF	https://www.aclweb.org/anthology/W16-6325
PWC	https://paperswithcode.com/paper/a-recurrent-neural-network-architecture-for
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Graph theoretic interpretation of Bangla traditional grammar


Title	Graph theoretic interpretation of Bangla traditional grammar
Authors	Samir Karmakar, Sayantani Banerjee, Soumya Ghosh
Abstract
Tasks
Published	2016-12-01
URL	https://www.aclweb.org/anthology/W16-6317/
PDF	https://www.aclweb.org/anthology/W16-6317
PWC	https://paperswithcode.com/paper/graph-theoretic-interpretation-of-bangla
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Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models


Title	Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models
Authors	Amin Jalali, Qiyang Han, Ioana Dumitriu, Maryam Fazel
Abstract	The Stochastic Block Model (SBM) is a widely used random graph model for networks with communities. Despite the recent burst of interest in community detection under the SBM from statistical and computational points of view, there are still gaps in understanding the fundamental limits of recovery. In this paper, we consider the SBM in its full generality, where there is no restriction on the number and sizes of communities or how they grow with the number of nodes, as well as on the connectivity probabilities inside or across communities. For such stochastic block models, we provide guarantees for exact recovery via a semidefinite program as well as upper and lower bounds on SBM parameters for exact recoverability. Our results exploit the tradeoffs among the various parameters of heterogenous SBM and provide recovery guarantees for many new interesting SBM configurations.
Tasks	Community Detection
Published	2016-12-01
URL	http://papers.nips.cc/paper/6574-exploiting-tradeoffs-for-exact-recovery-in-heterogeneous-stochastic-block-models
PDF	http://papers.nips.cc/paper/6574-exploiting-tradeoffs-for-exact-recovery-in-heterogeneous-stochastic-block-models.pdf
PWC	https://paperswithcode.com/paper/exploiting-tradeoffs-for-exact-recovery-in
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Findings of the 2016 Conference on Machine Translation


Title	Findings of the 2016 Conference on Machine Translation
Authors	Ond{\v{r}}ej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aur{'e}lie N{'e}v{'e}ol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, Marcos Zampieri
Abstract
Tasks	Automatic Post-Editing, Machine Translation, Multimodal Machine Translation
Published	2016-08-01
URL	https://www.aclweb.org/anthology/W16-2301/
PDF	https://www.aclweb.org/anthology/W16-2301
PWC	https://paperswithcode.com/paper/findings-of-the-2016-conference-on-machine
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