@inproceedings{yung16rational,

title = {Modelling the Usage of Discourse Connectives as Rational Speech Acts},

author = {Yung, Frances and Duh, Kevin and Komura, Taku and Matsumoto, Yuji},

url = {http://www.aclweb.org/anthology/K16-1030},

year  = {2016},

date = {2016-08-01},

booktitle = {Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning},

pages = {302--313},

publisher = {Association for Computational Linguistics},

address = {Berlin, Germany},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{McCree_Sell_Garcia-Romero_2016A,

title = {Augmented Data Training of Joint Acoustic/Phonotactic DNN i-vectors for NIST LRE15},

author = {Alan McCree and Gregory Sell and Daniel Garcia-Romero},

url = {https://hltcoe.jhu.edu/wp-content/uploads/2016/10/McCree_Sell_Garcia-Romero_2016A.pdf},

year  = {2016},

date = {2016-01-01},

booktitle = {Proceedings of Odyssey},

abstract = {This paper presents the JHU HLTCOE submission to the NIST 2015 Language Recognition Evaluation, including critical and novel algorithmic components, use of limited and augmented training data, and additional post-evaluation analysis and improvements. All of our systems used i-vectors based on Deep Neural Networks (DNNs) with discriminatively-trained Gaussian classifiers, and linear fusion was performed with duration-dependent scaling. A key innovation was the use of three different kinds of i-vectors: acoustic, phonotactic, and joint. In addition, data augmentation was used to overcome the limited training data of this evaluation. Post-evaluation analysis shows the benefits of these design decisions as well as further potential improvements.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{tanaka16evolution,

title = {Automated structure discovery and parameter tuning of neural network language model based on evolution strategy},

author = {Tomohiro Tanaka and Takafumi Moriya and Takahiro Shinozaki and Shinji Watanabe and Takaaki Hori and Kevin Duh},

year  = {2016},

date = {2016-01-01},

booktitle = {Proceedings of the 2016 IEEE Workshop on Spoken Language Technology},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sell_McCree_Garcia-Romero_2016A,

title = {Priors for Speaker Counting and Diarization with AHC},

author = {Gregory Sell and Alan McCree and Daniel Garcia-Romero},

url = {https://hltcoe.jhu.edu/wp-content/uploads/2016/10/Sell_McCree_Garcia-Romero_2016A.pdf},

year  = {2016},

date = {2016-01-01},

booktitle = {Proceedings of Interspeech},

abstract = {Estimating the number of speakers in an audio segment is a necessary step in the process of speaker diarization, but current diarization algorithms do not explicitly define a prior probability on this estimation. This work proposes a process for including priors in speaker diarization with agglomerative hierarchical clustering (AHC). It is also shown that the exclusion of a prior with AHC is itself implicitly a prior, which is found to be geometric growth in the number of speakers. By using more sensible priors, we are able to demonstrate significantly improved robustness to calibration error for speaker counting and speaker diarization.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{chen2016joint,

title = {A joint graph inference case study: the c. elegans chemical and electrical connectomes},

author = {L. Chen and J. T. Vogelstein and V. Lyzinski and C. E. Priebe},

year  = {2016},

date = {2016-01-01},

booktitle = {Worm},

volume = {5},

number = {2},

organization = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{tsubaki16nonlinear,

title = {Non-Linear Similarity Learning for Compositionality},

author = {Masashi Tsubaki and Kevin Duh and Masashi Shimbo and Yuji Matsumoto},

url = {http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12373},

year  = {2016},

date = {2016-01-01},

booktitle = {AAAI Conference on Artificial Intelligence},

abstract = {Many NLP applications rely on the existence ofsimilarity measures over text data.Although word vector space modelsprovide good similarity measures between words,phrasal and sentential similarities derived from compositionof individual words remain as a difficult problem.In this paper, we propose a new method of ofnon-linear similarity learning for semantic compositionality.In this method, word representations are learnedthrough the similarity learning of sentencesin a high-dimensional space with kernel functions.On the task of predicting the semantic similarity oftwo sentences (SemEval 2014, Task 1),our method outperforms linear baselines,feature engineering approaches,recursive neural networks,and achieve competitive results with long short-term memory models.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bojar-EtAl:2015:WMT,

title = {Findings of the 2015 Workshop on Statistical Machine Translation},

author = {Bojar, Ondřej and Chatterjee, Rajen and Federmann, Christian and Haddow, Barry and Huck, Matthias and Hokamp, Chris and Koehn, Philipp and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Post, Matt and Scarton, Carolina and Specia, Lucia and Turchi, Marco},

year  = {2015},

date = {2015-09-01},

booktitle = {Proceedings of the Tenth Workshop on Statistical Machine Translation},

pages = {1--46},

publisher = {Association for Computational Linguistics},

address = {Lisbon, Portugal},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{napoles2015ground,

title = {Ground Truth for Grammatical Error Correction Metrics},

author = {Napoles, Courtney and Sakaguchi, Keisuke and Post, Matt and Tetreault, Joel},

year  = {2015},

date = {2015-07-01},

booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},

pages = {588--593},

publisher = {Association for Computational Linguistics},

address = {Beijing, China},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{volkova-EtAl:2015:AAAI,

title = {Inferring Latent User Properties from Texts Published in Social Media (Demo)},

author = {Volkova, Svitlana and Yoram Bachrach and Michael Armstrong and Vijay Sharma},

url = {https://hltcoe.jhu.edu/wp-content/uploads/2016/11/17310_slides.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the Twenty-Ninth Conference on Artificial Intelligence (AAAI)},

address = {Austin, TX},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wintrode_Sell_EtAl_2015A,

title = {Content-Based Recommender Systems for Spoken Documents},

author = {Jonathan Wintrode and Gregory Sell and Aren Jansen and Michelle Fox and Daniel Garcia-Romero and Alan McCree},

url = {https://hltcoe.jhu.edu/wp-content/uploads/2016/10/Wintrode_Sell_EtAl_2015A.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing},

abstract = {Content-based recommender systems use preference ratings and features that characterize media to model users’ interests or information needs for making future recommendations. While previously developed in the music and text domains, we present an initial exploration of content-based recommendation for spoken documents using a corpus of public domain internet audio. Unlike familiar speech technologies of topic identification and spoken document retrieval, our recommendation task requires a more comprehensive notion of document relevance than bags-of-words would supply. Inspired by music recommender systems, we automatically extract a wide variety of content-based features to characterize non-linguistic aspects of the audio such as speaker, language, gender, and environment. To combine these heterogeneous information sources into a single relevance judgement, we evaluate feature, score, and hybrid fusion techniques. Our study provides an essential first exploration of the task and clearly demonstrates the value of a multisource approach over a bag-of-words baseline.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sell_Garcia-Romero_2015A,

title = {Diarization Resegmentation in the Factor Analysis Subspace},

author = {Gregory Sell and Daniel Garcia-Romero},

url = {https://hltcoe.jhu.edu/wp-content/uploads/2016/10/Sell_Garcia-Romero_2015A.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing},

abstract = {Resegmentation is an important post-processing step to refine the rough boundaries of diarization systems that rely on segment clustering of an initial uniform segmentation. Past work has primarily used a Viterbi resegmentation with MFCC features for this purpose. In this paper, we examine an algorithm for resegmentation that operates instead in factor analysis subspace. By combining this system with a speaker clustering front-end, we yield a diarization error rate of 11.5% on the CALLHOME conversational telephone speech corpus.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Sell_Garcia-Romero_McCree_2015A,

title = {Speaker Diarization with I-Vectors from DNN Senone Posteriors},

author = {Gregory Sell and Daniel Garcia-Romero and Alan McCree},

url = {https://hltcoe.jhu.edu/wp-content/uploads/2016/10/Sell_Garcia-Romero_McCree_2015A.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of Interspeech},

abstract = {Motivated by recent gains in speaker identification by incorporating senone posteriors from deep neural networks (DNNs) into i-vector extraction, we examine similar enhancements to speaker diarization with i-vector clustering. We examine two DNNs with different numbers of senone targets in combination with a diagonal or full covariance universal background model (UBM) in the context of the multilingual corpus CALLHOME. Results show that the larger DNN with a full covariance UBM gives the best performance. The improvements appear to have a strong dependence on number of speakers in a conversation, and a lesser dependence on language. Overall, when combined with resegmentation, the proposed system improves CALLHOME performance to 10.3% DER.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wang:2015eu,

title = {Social Media as a Sensor of Air Quality and Public Response in China},

author = {Shiliang Wang and Michael Paul and Dredze, Mark},

year  = {2015},

date = {2015-01-01},

booktitle = {AAAI Workshop on the World Wide Web and Public Health Intelligence},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Pavlick:2015bs,

title = {FrameNet+: Fast Paraphrastic Tripling of FrameNet},

author = {Ellie Pavlick and Wolfe, Travis and Pushpendre Rastogi and Callison-Burch, Chris and Dredze, Mark and Van Durme, Benjamin},

year  = {2015},

date = {2015-01-01},

booktitle = {Association for Computational Linguistics (ACL) (short paper)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Peng:2015rt,

title = {Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings},

author = {Nanyun Peng and Dredze, Mark},

year  = {2015},

date = {2015-01-01},

booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Cohen:2015zl,

title = {Tobacco Watcher: Real-time Global Surveillance for Tobacco Control},

author = {Joanna Cohen and John Ayers and Dredze, Mark},

year  = {2015},

date = {2015-01-01},

booktitle = {World Conference on Tobacco or Health (WCTOH)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{volkova-EtAl:2015:AAAIb,

title = {Inferring Latent User Properties from Texts Published in Social Media (Demo)},

author = {Volkova, Svitlana and Yoram Bachrach and Michael Armstrong and Vijay Sharma},

url = {http://www.aclweb.org/anthology/P/P14/P14-1018},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the Twenty-Ninth Conference on Artificial Intelligence (AAAI)},

address = {Austin, TX},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Biggerstaff:2015kb,

title = {Results from the Centers for Disease Control and Prevention's Predict the 2013--2014 Influenza Season Challenge},

author = {M. Biggerstaff and D. Alper and Dredze, Mark and S. Fox and I. Fung and K. Hickmann and B. Lewis and R. Rosenfeld and J. Shaman and M-H. Tsou and P. Velardi and A. Vespignani and L. Finelli},

year  = {2015},

date = {2015-01-01},

booktitle = {International Conference of Emerging Infectious Diseases Conference},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Cohen:2015hl,

title = {Tobacco Watcher: Real-Time Global Tobacco Surveillance Using Online News Media},

author = {Joanna Cohen and Rebecca Shillenn and Dredze, Mark and John Ayers},

year  = {2015},

date = {2015-01-01},

booktitle = {Annual Meeting of the Society for Research on Nicotine and Tobacco},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wolfe:2015qf,

title = {Predicate Argument Alignment using a Global Coherence Model},

author = {Wolfe, Travis and Dredze, Mark and Van Durme, Benjamin},

url = {http://aclweb.org/anthology/N/N15/N15-1002.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)},

abstract = {We present a joint model for predicate argument alignment. We leverage multiple sources of semantic information, including temporal ordering constraints between events. These are combined in a max-margin framework to find a globally consistent view of entities and events across multiple documents, which leads to improvements over a very strong local baseline.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wang:2015ve,

title = {The Hurricane Sandy Twitter Corpus},

author = {Haoyu Wang and Eduard Hovy and Dredze, Mark},

year  = {2015},

date = {2015-01-01},

booktitle = {AAAI Workshop on the World Wide Web and Public Health Intelligence},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{coppersmith15a,

title = {From ADHD to SAD: analyzing the language of mental health on Twitter through self-reported diagnoses},

author = {Coppersmith, Glen and Dredze, Mark and Harman, Craig and Kristy Hollingshead},

year  = {2015},

date = {2015-01-01},

booktitle = {NAACL Workshop on Computational Linguistics and Clinical Psychology},

abstract = {Many significant challenges exist for the mental health field, but one in particular is a lack of data available to guide research. Language provides a natural lens for studying mental health -- much existing work and therapy have strong linguistic components, so the creation of a large, varied, language-centric dataset could provide significant grist for the field of mental health research. We examine a broad range of mental health conditions in Twitter data by identifying self-reported statements of diagnosis. We systematically explore language differences between ten conditions with respect to the general population, and to each other. Our aim is to provide guidance and a roadmap for where deeper exploration is likely to be fruitful.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Westmaas:2015rw,

title = {Evaluation of the Great American Smokeout by Digital Surveillance},

author = {J. Westmaas and John Ayers and Dredze, Mark and Benjamin Althouse},

year  = {2015},

date = {2015-01-01},

booktitle = {Society of Behavioral Medicine},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Paul:2015la,

title = {Worldwide Influenza Surveillance through Twitter},

author = {Michael Paul and Dredze, Mark and David Broniatowski and Nicholas Generous},

year  = {2015},

date = {2015-01-01},

booktitle = {AAAI Workshop on the World Wide Web and Public Health Intelligence},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Kumar:2015dq,

title = {Discovering Links between Reports of Celebrity Suicides and Suicidal Ideation from Social Media},

author = {Mrinal Kumar and Dredze, Mark and Coppersmith, Glen and Munmun Choudhury},

year  = {2015},

date = {2015-01-01},

booktitle = {NAACL Workshop on Computational Linguistics and Clinical Psychology},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Smith:2015mz,

title = {Tracking Public Awareness of Influenza through Twitter},

author = {Michael Smith and David Broniatowski and Michael Paul and Dredze, Mark},

year  = {2015},

date = {2015-01-01},

booktitle = {3rd International Conference on Digital Disease Detection (DDD)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Benton:2015qq,

title = {Entity Linking for Spoken Language},

author = {Adrian Benton and Dredze, Mark},

url = {http://aclweb.org/anthology/N/N15/N15-1024.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)},

abstract = {Research on entity linking has considered a broad range of text, including newswire, blogs and web documents in multiple languages. However, the problem of entity linking for spoken language remains unexplored. Spoken language obtained from automatic speech recognition systems poses different types of challenges for entity linking; transcription errors can distort the context, and named entities tend to have high error rates. We propose features to mitigate these errors and evaluate the impact of ASR errors on entity linking using a new corpus of entity linked broadcast news transcripts.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Gormley:2015ly,

title = {Improved Relation Extraction with Feature-Rich Compositional Embedding Models},

author = {Gormley, Matt and Mo Yu and Dredze, Mark},

year  = {2015},

date = {2015-01-01},

booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Peng:2015db,

title = {An Empirical Study of Chinese Name Matching and Applications},

author = {Nanyun Peng and Mo Yu and Dredze, Mark},

year  = {2015},

date = {2015-01-01},

booktitle = {Association for Computational Linguistics (ACL) (short paper)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Kumar:2015sf,

title = {Shifts in Suicidal Ideation Manifested in Social Media Following Celebrity Suicides},

author = {Mrinal Kumar and Dredze, Mark and Coppersmith, Glen and Munmun Choudhury},

year  = {2015},

date = {2015-01-01},

booktitle = {Conference on Hypertext and Social Media},

abstract = {The Werther effect describes the increased rate of completed or attempted suicides following the depiction of an individual's suicide in the media, typically a celebrity. We present findings on the prevalence of this effect in an online platform: r/SuicideWatch on Reddit. We examine both the posting activity and post content after the death of ten high-profile suicides. Posting activity increases following reports of celebrity suicides, and post content exhibits considerable changes that indicate increased suicidal ideation. Specifically, we observe that post-celebrity suicide content is more likely to be inward focused, manifest decreased social concerns, and laden with greater anxiety, anger, and negative emotion. Topic model analysis further reveals content in this period to switch to a more derogatory tone that bears evidence of self-harm and suicidal tendencies. We discuss the implications of our findings in enabling better community support to psychologically vulnerable populations, and the potential of building suicide prevention interventions following high-profile suicides.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Peng:2015yf,

title = {A Chinese Concrete NLP Pipeline},

author = {Nanyun Peng and Francis Ferraro and Mo Yu and Andrews, Nicholas and Jay DeYoung and Max Thomas and Gormley, Matt and Wolfe, Travis and Harman, Craig and Van Durme, Benjamin and Dredze, Mark},

url = {http://aclweb.org/anthology/N/N15/N15-3018.pdf},

year  = {2015},

date = {2015-01-01},

booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL), Demonstration Session},

abstract = {Natural language processing research increasingly relies on the output of a variety of syntactic and semantic analytics. Yet integrating output from multiple analytics into a single framework can be time consuming and slow research progress. We present a CONCRETE Chinese NLP Pipeline: an NLP stack built using a series of open source systems integrated based on the CONCRETE data schema. Our pipeline includes data ingest, word segmentation, part of speech tagging, parsing, named entity recognition, relation extraction and cross document coreference resolution. Additionally, we integrate a tool for visualizing these annotations as well as allowing for the manual annotation of new data. We release our pipeline to the research community to facilitate work on Chinese language tasks that require rich linguistic annotations.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Yu:2015rt,

title = {Combining Word Embeddings and Feature Embeddings for Fine-grained Relation Extraction},

author = {Mo Yu and Gormley, Matt and Dredze, Mark},

year  = {2015},

date = {2015-01-01},

booktitle = {North American Chapter of the Association for Computational Linguistics (NAACL)},

abstract = {Compositional embedding models build a representation for a linguistic structure based on its component word embeddings. While recent work has combined these word embeddings with hand crafted features for improved performance, it was restricted to a small number of features due to model complexity, thus limiting its applicability. We propose a new model that conjoins features and word embeddings while maintaining a small number of parameters by learning feature embeddings jointly with the parameters of a compositional model. The result is a method that can scale to more features and more labels, while avoiding overfitting. We demonstrate that our model attains state-of-the-art results on ACE and ERE fine-grained relation extraction.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{volkova-vandurme:2015:AAAI,

title = {Online Bayesian Models for Personal Analytics in Social Media},

author = {Volkova, Svitlana and Van Durme, Benjamin},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the Twenty-Ninth Conference on Artificial Intelligence (AAAI)},

address = {Austin, TX},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}