Publications
2016 |
Yung, Frances ; Duh, Kevin ; Komura, Taku ; Matsumoto, Yuji Modelling the Usage of Discourse Connectives as Rational Speech Acts Inproceedings Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning, pp. 302–313, Association for Computational Linguistics, Berlin, Germany, 2016. @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} } |
Ding, Shuoyang ; Duh, Kevin ; Khayrallah, Huda ; Koehn, Philipp ; Post, Matt The JHU Machine Translation Systems for WMT 2016 Inproceedings Proceedings of the First Conference on Machine Translation, pp. 272–280, Association for Computational Linguistics, Berlin, Germany, 2016. @inproceedings{ding16wmt, title = {The JHU Machine Translation Systems for WMT 2016}, author = {Ding, Shuoyang and Duh, Kevin and Khayrallah, Huda and Koehn, Philipp and Post, Matt}, url = {http://www.aclweb.org/anthology/W16-2310}, year = {2016}, date = {2016-08-01}, booktitle = {Proceedings of the First Conference on Machine Translation}, pages = {272--280}, publisher = {Association for Computational Linguistics}, address = {Berlin, Germany}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Yung, Frances ; Duh, Kevin ; Komura, Taku ; Matsumoto, Yuji Modelling the Interpretation of Discourse Connectives by Bayesian Pragmatics Inproceedings Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 531–536, Association for Computational Linguistics, Berlin, Germany, 2016. @inproceedings{yung16connectives, title = {Modelling the Interpretation of Discourse Connectives by Bayesian Pragmatics}, author = {Yung, Frances and Duh, Kevin and Komura, Taku and Matsumoto, Yuji}, url = {http://anthology.aclweb.org/P16-2086}, year = {2016}, date = {2016-08-01}, booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)}, pages = {531--536}, publisher = {Association for Computational Linguistics}, address = {Berlin, Germany}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Chen, L; Vogelstein, J T; Lyzinski, V; Priebe, C E A joint graph inference case study: the c. elegans chemical and electrical connectomes Inproceedings Worm, Taylor & Francis 2016. @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} } |
Sell, Gregory; McCree, Alan; Garcia-Romero, Daniel Priors for Speaker Counting and Diarization with AHC Inproceedings Proceedings of Interspeech, 2016. @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://engineering.jhu.edu/hltcoe/wp-content/uploads/sites/92/2016/10/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} } 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. |
McCree, Alan; Sell, Gregory; Garcia-Romero, Daniel Augmented Data Training of Joint Acoustic/Phonotactic DNN i-vectors for NIST LRE15 Inproceedings Proceedings of Odyssey, 2016. @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://engineering.jhu.edu/hltcoe/wp-content/uploads/sites/92/2016/10/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} } 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. |
Lyzinski, Vince ; Tang, Minh ; Athreya, Avanti ; Park, Youngser ; Priebe, Carey E Community Detection and Classification in Hierarchical Stochastic Blockmodels Journal Article IEEE Transactions on Network Science and Engineering, accepted for publication , 2016. @article{lyzinski2015community, title = {Community Detection and Classification in Hierarchical Stochastic Blockmodels}, author = {Lyzinski, Vince and Tang, Minh and Athreya, Avanti and Park, Youngser and Priebe, Carey E}, year = {2016}, date = {2016-01-01}, journal = {IEEE Transactions on Network Science and Engineering}, volume = {accepted for publication}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Lyzinski, V Information Recovery in Shuffled Graphs via Graph Matching Journal Article arXiv preprint arXiv:1605.02315, 2016. @article{lyzinski2016information, title = {Information Recovery in Shuffled Graphs via Graph Matching}, author = {Lyzinski, V.}, year = {2016}, date = {2016-01-01}, journal = {arXiv preprint arXiv:1605.02315}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Lyzinski, V; Levin, K; Fishkind, D E; Priebe, C E On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching Journal Article Journal of Machine Learning Research, 17 (179), pp. 1–34, 2016. @article{VN3, title = {On the Consistency of the Likelihood Maximization Vertex Nomination Scheme: Bridging the Gap Between Maximum Likelihood Estimation and Graph Matching}, author = {Lyzinski, V. and Levin, K. and Fishkind, D. E. and Priebe, C. E.}, year = {2016}, date = {2016-01-01}, journal = {Journal of Machine Learning Research}, volume = {17}, number = {179}, pages = {1--34}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Tang, M; Athreya, A; Sussman, D L; Lyzinski, V; Park, Y; Priebe, C E A semiparametric two-sample hypothesis testing problem for random dot product graphs Journal Article Journal of Computational and Graphical Statistics, Accepted for publication , 2016. @article{MT2, title = {A semiparametric two-sample hypothesis testing problem for random dot product graphs}, author = {M. Tang and A. Athreya and D. L. Sussman and V. Lyzinski and Y. Park and C. E. Priebe}, year = {2016}, date = {2016-01-01}, journal = {Journal of Computational and Graphical Statistics}, volume = {Accepted for publication}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Lyzinski, Vince ; Fishkind, Donniell E; Fiori, Marcelo ; Vogelstein, Joshua T; Priebe, Carey E; Sapiro, Guillermo Graph matching: Relax at your own risk Journal Article IEEE Transactions on Pattern Analysis and Machine Intelligence, 38 (1), pp. 60–73, 2016. @article{rel, title = {Graph matching: Relax at your own risk}, author = {Lyzinski, Vince and Fishkind, Donniell E and Fiori, Marcelo and Vogelstein, Joshua T and Priebe, Carey E and Sapiro, Guillermo}, year = {2016}, date = {2016-01-01}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {38}, number = {1}, pages = {60--73}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Zheng, D; Mhembere, D; Lyzinski, V; Vogelstein, J; Priebe, C E; Burns, R Semi-External Memory Sparse Matrix Multiplication on Billion-node Graphs in a Multicore Architecture Journal Article IEEE Transactions in Parallel and Distributed Systems, Accepted for publication , 2016. @article{zheng2016semi, title = {Semi-External Memory Sparse Matrix Multiplication on Billion-node Graphs in a Multicore Architecture}, author = {Zheng, D. and Mhembere, D. and Lyzinski, V. and Vogelstein, J. and Priebe, C. E. and Burns, R.}, year = {2016}, date = {2016-01-01}, journal = {IEEE Transactions in Parallel and Distributed Systems}, volume = {Accepted for publication}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Lyzinski, V; Park, Y; Priebe, C E; Trosset, M W Fast Embedding for JOFC Using the Raw Stress Criterion Journal Article Journal of Computational and Graphical Statistics, Accepted for publication , 2016. @article{lyzinski2015fast, title = {Fast Embedding for JOFC Using the Raw Stress Criterion}, author = {Lyzinski, V. and Park, Y. and Priebe, C. E. and Trosset, M. W.}, year = {2016}, date = {2016-01-01}, journal = {Journal of Computational and Graphical Statistics}, volume = {Accepted for publication}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Tanaka, Tomohiro; Moriya, Takafumi; Shinozaki, Takahiro; Watanabe, Shinji; Hori, Takaaki; Duh, Kevin Automated structure discovery and parameter tuning of neural network language model based on evolution strategy Inproceedings Proceedings of the 2016 IEEE Workshop on Spoken Language Technology, 2016. @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} } |
Tsubaki, Masashi; Duh, Kevin; Shimbo, Masashi; Matsumoto, Yuji Non-Linear Similarity Learning for Compositionality Inproceedings AAAI Conference on Artificial Intelligence, 2016. @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} } 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. |
2015 |
Bojar, Ondřej ; Chatterjee, Rajen ; Federmann, Christian ; Haddow, Barry ; Huck, Matthias ; Hokamp, Chris ; Koehn, Philipp ; Logacheva, Varvara ; Monz, Christof ; Negri, Matteo ; Post, Matt ; Scarton, Carolina ; Specia, Lucia ; Turchi, Marco Findings of the 2015 Workshop on Statistical Machine Translation Inproceedings Proceedings of the Tenth Workshop on Statistical Machine Translation, pp. 1–46, Association for Computational Linguistics, Lisbon, Portugal, 2015. @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} } |
Napoles, Courtney ; Sakaguchi, Keisuke ; Post, Matt ; Tetreault, Joel Ground Truth for Grammatical Error Correction Metrics Inproceedings 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), pp. 588–593, Association for Computational Linguistics, Beijing, China, 2015. @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} } |
Coppersmith, Glen ; Dredze, Mark ; Harman, Craig ; Hollingshead, Kristy From ADHD to SAD: analyzing the language of mental health on Twitter through self-reported diagnoses Inproceedings NAACL Workshop on Computational Linguistics and Clinical Psychology, 2015. @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} } 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. |
Sylak-Glassman, John ; Kirov, Christo ; Post, Matt ; Que, Roger ; Yarowsky, David A Universal Feature Schema for Rich Morphological Annotation and Fine-Grained Cross-Lingual Part-of- Speech Tagging Incollection Systems and Frameworks for Computational Morphology, Springer, 2015. @incollection{sylak-glassman2015a-universal, title = {A Universal Feature Schema for Rich Morphological Annotation and Fine-Grained Cross-Lingual Part-of- Speech Tagging}, author = {Sylak-Glassman, John and Kirov, Christo and Post, Matt and Que, Roger and Yarowsky, David}, year = {2015}, date = {2015-01-01}, booktitle = {Systems and Frameworks for Computational Morphology}, publisher = {Springer}, keywords = {}, pubstate = {published}, tppubtype = {incollection} } |
Post, Matt ; Cao, Yuan ; Kumar, Gaurav Joshua 6: A phrase-based and hierarchical statistical machine translation system Journal Article The Prague Bulletin of Mathematical Linguistics, 2015. @article{post2015joshua, title = {Joshua 6: A phrase-based and hierarchical statistical machine translation system}, author = {Post, Matt and Cao, Yuan and Kumar, Gaurav}, year = {2015}, date = {2015-01-01}, journal = {The Prague Bulletin of Mathematical Linguistics}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Smith, Michael; Broniatowski, David; Paul, Michael; Dredze, Mark Tracking Public Awareness of Influenza through Twitter Inproceedings 3rd International Conference on Digital Disease Detection (DDD), 2015. @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} } |
Paul, Michael; Dredze, Mark SPRITE: Generalizing Topic Models with Structured Priors Journal Article 2015. @article{Paul:2015sf, title = {SPRITE: Generalizing Topic Models with Structured Priors}, author = {Michael Paul and Dredze, Mark}, url = {https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/403/106}, year = {2015}, date = {2015-01-01}, abstract = {We introduce SPRITE, a family of topic models that incorporates structure into model priors as a function of underlying components. The structured priors can be constrained to model topic hierarchies, factorizations, correlations, and supervision, allowing SPRITE to be tailored to particular settings. We demonstrate this flexibility by constructing a SPRITE-based model to jointly infer topic hierarchies and author perspective, which we apply to corpora of political debates and online reviews. We show that the model learns intuitive topics, outperforming several other topic models at predictive tasks.}, keywords = {}, pubstate = {published}, tppubtype = {article} } We introduce SPRITE, a family of topic models that incorporates structure into model priors as a function of underlying components. The structured priors can be constrained to model topic hierarchies, factorizations, correlations, and supervision, allowing SPRITE to be tailored to particular settings. We demonstrate this flexibility by constructing a SPRITE-based model to jointly infer topic hierarchies and author perspective, which we apply to corpora of political debates and online reviews. We show that the model learns intuitive topics, outperforming several other topic models at predictive tasks. |
Westmaas, J; Ayers, John; Dredze, Mark ; Althouse, Benjamin Evaluation of the Great American Smokeout by Digital Surveillance Inproceedings Society of Behavioral Medicine, 2015. @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} } |
Wang, Haoyu; Hovy, Eduard; Dredze, Mark The Hurricane Sandy Twitter Corpus Inproceedings AAAI Workshop on the World Wide Web and Public Health Intelligence, 2015. @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} } |
Yu, Mo; Dredze, Mark Learning Composition Models for Phrase Embeddings Journal Article pp. 227–242, 2015. @article{TACL586, title = {Learning Composition Models for Phrase Embeddings}, author = {Mo Yu and Dredze, Mark}, url = {https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/586/125}, year = {2015}, date = {2015-01-01}, pages = {227--242}, abstract = {Lexical embeddings can serve as useful representations for words for a variety of NLP tasks, but learning embeddings for phrases can be challenging. While separate embeddings are learned for each word, this is infeasible for every phrase. We construct phrase embeddings by learning how to compose word embeddings using features that capture phrase structure and context. We propose efficient unsupervised and task-specific learning objectives that scale our model to large datasets. We demonstrate improvements on both language modeling and several phrase semantic similarity tasks with various phrase lengths. We make the implementation of our model and the datasets available for general use.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Lexical embeddings can serve as useful representations for words for a variety of NLP tasks, but learning embeddings for phrases can be challenging. While separate embeddings are learned for each word, this is infeasible for every phrase. We construct phrase embeddings by learning how to compose word embeddings using features that capture phrase structure and context. We propose efficient unsupervised and task-specific learning objectives that scale our model to large datasets. We demonstrate improvements on both language modeling and several phrase semantic similarity tasks with various phrase lengths. We make the implementation of our model and the datasets available for general use. |
Gormley, Matt ; Yu, Mo; Dredze, Mark Improved Relation Extraction with Feature-Rich Compositional Embedding Models Inproceedings Empirical Methods in Natural Language Processing (EMNLP), 2015. @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} } |
Wolfe, Travis ; Dredze, Mark ; Van Durme, Benjamin Predicate Argument Alignment using a Global Coherence Model Inproceedings North American Chapter of the Association for Computational Linguistics (NAACL), 2015. @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} } 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. |
Coppersmith, Glen ; Dredze, Mark ; Harman, Craig ; Hollingshead, Kristy; Mitchell, Margaret CLPsych 2015 Shared Task: Depression and PTSD on Twitter Inproceedings NAACL Workshop on Computational Linguistics and Clinical Psychology, 2015. @inproceedings{Coppersmith:2015eu, title = {CLPsych 2015 Shared Task: Depression and PTSD on Twitter}, author = {Coppersmith, Glen and Dredze, Mark and Harman, Craig and Kristy Hollingshead and Mitchell, Margaret}, year = {2015}, date = {2015-01-01}, booktitle = {NAACL Workshop on Computational Linguistics and Clinical Psychology}, abstract = {This paper presents a summary of the Computational Linguistics and Clinical Psychology (CLPsych) 2015 shared and unshared tasks. These tasks aimed to provide apples-to-apples comparisons of various approaches to modeling language relevant to mental health from social media. The data used for these tasks is from Twitter users who state a diagnosis of depression or post traumatic stress disorder (PTSD) and demographically-matched community controls. The unshared task was a hackathon held at Johns Hopkins University in November 2014 to explore the data, and the shared task was conducted remotely, with each participating team submitted scores for a held-back test set of users. The shared task consisted of three binary classification experiments: (1) depression versus control, (2) PTSD versus control, and (3) depression versus PTSD. Classifiers were compared primarily via their average precision, though a number of other metrics are used along with this to allow a more nuanced interpretation of the performance measures.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } This paper presents a summary of the Computational Linguistics and Clinical Psychology (CLPsych) 2015 shared and unshared tasks. These tasks aimed to provide apples-to-apples comparisons of various approaches to modeling language relevant to mental health from social media. The data used for these tasks is from Twitter users who state a diagnosis of depression or post traumatic stress disorder (PTSD) and demographically-matched community controls. The unshared task was a hackathon held at Johns Hopkins University in November 2014 to explore the data, and the shared task was conducted remotely, with each participating team submitted scores for a held-back test set of users. The shared task consisted of three binary classification experiments: (1) depression versus control, (2) PTSD versus control, and (3) depression versus PTSD. Classifiers were compared primarily via their average precision, though a number of other metrics are used along with this to allow a more nuanced interpretation of the performance measures. |
Bushman, Brad; Newman, Katherine; Calvert, Sandra; Downey, Geraldine; Dredze, Mark ; Gottfredson, Michael; Jablonski, Nina; Masten, Ann; Morrill, Calvin; Neill, Daniel; Romer, Daniel; Webster, Daniel Youth Violence: What We Know and What We Need to Know Journal Article 2015. @article{Bushman:2015fj, title = {Youth Violence: What We Know and What We Need to Know}, author = {Brad Bushman and Katherine Newman and Sandra Calvert and Geraldine Downey and Dredze, Mark and Michael Gottfredson and Nina Jablonski and Ann Masten and Calvin Morrill and Daniel Neill and Daniel Romer and Daniel Webster}, year = {2015}, date = {2015-01-01}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Peng, Nanyun; Ferraro, Francis; Yu, Mo; Andrews, Nicholas ; DeYoung, Jay; Thomas, Max; Gormley, Matt ; Wolfe, Travis ; Harman, Craig ; Van Durme, Benjamin ; Dredze, Mark A Chinese Concrete NLP Pipeline Inproceedings North American Chapter of the Association for Computational Linguistics (NAACL), Demonstration Session, 2015. @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} } 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. |
Cohen, Joanna; Shillenn, Rebecca; Dredze, Mark ; Ayers, John Tobacco Watcher: Real-Time Global Tobacco Surveillance Using Online News Media Inproceedings Annual Meeting of the Society for Research on Nicotine and Tobacco, 2015. @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} } |
Volkova, Svitlana ; Bachrach, Yoram; Armstrong, Michael; Sharma, Vijay Inferring Latent User Properties from Texts Published in Social Media (Demo) Inproceedings Proceedings of the Twenty-Ninth Conference on Artificial Intelligence (AAAI), Austin, TX, 2015. @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} } |
Kumar, Mrinal; Dredze, Mark ; Coppersmith, Glen ; Choudhury, Munmun Discovering Links between Reports of Celebrity Suicides and Suicidal Ideation from Social Media Inproceedings NAACL Workshop on Computational Linguistics and Clinical Psychology, 2015. @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} } |
Volkova, Svitlana ; Van Durme, Benjamin Online Bayesian Models for Personal Analytics in Social Media Inproceedings Proceedings of the Twenty-Ninth Conference on Artificial Intelligence (AAAI), Austin, TX, 2015. @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} } |
Fishkind, D E; Lyzinski, V; Pao, H; Chen, L; Priebe, C E Vertex nomination schemes for membership prediction Journal Article The Annals of Applied Statistics, 9 (3), pp. 1510–1532, 2015. @article{fishkind2015vertex, title = {Vertex nomination schemes for membership prediction}, author = {Fishkind, D. E. and Lyzinski, V. and Pao, H. and Chen, L. and Priebe, C. E.}, year = {2015}, date = {2015-01-01}, journal = {The Annals of Applied Statistics}, volume = {9}, number = {3}, pages = {1510--1532}, publisher = {Institute of Mathematical Statistics}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Wintrode, Jonathan; Sell, Gregory; Jansen, Aren; Fox, Michelle; Garcia-Romero, Daniel; McCree, Alan Content-Based Recommender Systems for Spoken Documents Inproceedings Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2015. @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://engineering.jhu.edu/hltcoe/wp-content/uploads/sites/92/2016/10/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} } 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. |
Sell, Gregory; Garcia-Romero, Daniel Diarization Resegmentation in the Factor Analysis Subspace Inproceedings Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2015. @inproceedings{Sell_Garcia-Romero_2015A, title = {Diarization Resegmentation in the Factor Analysis Subspace}, author = {Gregory Sell and Daniel Garcia-Romero}, url = {https://engineering.jhu.edu/hltcoe/wp-content/uploads/sites/92/2016/10/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} } 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. |
Kumar, Mrinal; Dredze, Mark ; Coppersmith, Glen ; Choudhury, Munmun Shifts in Suicidal Ideation Manifested in Social Media Following Celebrity Suicides Inproceedings Conference on Hypertext and Social Media, 2015. @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} } 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. |
Sell, Gregory; Garcia-Romero, Daniel; McCree, Alan Speaker Diarization with I-Vectors from DNN Senone Posteriors Inproceedings Proceedings of Interspeech, 2015. @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://engineering.jhu.edu/hltcoe/wp-content/uploads/sites/92/2016/10/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} } 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. |
Lyzinski, Vince; Sell, Gregory; Jansen, Aren An Evaluation of Graph Clustering Methods for Unsupervised Term Discovery Inproceedings Proceedings of Interspeech, 2015. @inproceedings{Lyzinski_Sell_Jansen_2015A, title = {An Evaluation of Graph Clustering Methods for Unsupervised Term Discovery}, author = {Vince Lyzinski and Gregory Sell and Aren Jansen}, url = {https://engineering.jhu.edu/hltcoe/wp-content/uploads/sites/92/2016/10/Lyzinski_Sell_Jansen_2015A.pdf}, year = {2015}, date = {2015-01-01}, booktitle = {Proceedings of Interspeech}, abstract = {Unsupervised term discovery (UTD) is the task of automatically identifying the repeated words and phrases in a collection of speech audio without relying on any language-specific resources. While the solution space for the task is far from fully explored, the dominant approach to date decomposes the discovery problem into two steps, where (i) segmental dynamic time warping is used to search the speech audio for repeated acoustic patterns, and (ii) these individual repetitions are partitioned into word/phrase categories using graph clustering. In this paper, we perform an unprecedented evaluation of a wide range of advanced graph clustering methods for the UTD task. We conduct our study in the evaluation framework of the Zero Resource Speech Challenge. We find that, for a range of features and languages, modularity-based clustering improves UTD performance most consistently, often by a wide margin. When paired with out-of-language deep neural net bottleneck features, we find performance near that of a high-resource UTD system.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Unsupervised term discovery (UTD) is the task of automatically identifying the repeated words and phrases in a collection of speech audio without relying on any language-specific resources. While the solution space for the task is far from fully explored, the dominant approach to date decomposes the discovery problem into two steps, where (i) segmental dynamic time warping is used to search the speech audio for repeated acoustic patterns, and (ii) these individual repetitions are partitioned into word/phrase categories using graph clustering. In this paper, we perform an unprecedented evaluation of a wide range of advanced graph clustering methods for the UTD task. We conduct our study in the evaluation framework of the Zero Resource Speech Challenge. We find that, for a range of features and languages, modularity-based clustering improves UTD performance most consistently, often by a wide margin. When paired with out-of-language deep neural net bottleneck features, we find performance near that of a high-resource UTD system. |
Lyzinski, V; Sussman, D L; Fishkind, D E; Pao, H; Chen, L; Vogelstein, J T; Park, Y; Priebe, C E Spectral clustering for divide-and-conquer graph matching Journal Article Parallel Computing, 47 , pp. 70–87, 2015. @article{lyzinski_spectral, title = {Spectral clustering for divide-and-conquer graph matching}, author = {Lyzinski, V. and Sussman, D. L. and Fishkind, D. E. and Pao, H. and Chen, L. and Vogelstein, J. T. and Park, Y. and Priebe, C. E.}, year = {2015}, date = {2015-01-01}, journal = {Parallel Computing}, volume = {47}, pages = {70--87}, publisher = {Elsevier}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Wolfe, Travis ; Dredze, Mark ; Mayfield, James ; McNamee, Paul ; Harman, Craig ; Finin, Tim ; Van Durme, Benjamin Interactive Knowledge Base Population Journal Article 2015. @article{Wolfe:2015qr, title = {Interactive Knowledge Base Population}, author = {Wolfe, Travis and Dredze, Mark and Mayfield, James and McNamee, Paul and Harman, Craig and Finin, Tim and Van Durme, Benjamin}, url = {http://arxiv.org/pdf/1506.00301.pdf}, year = {2015}, date = {2015-01-01}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Volkova, Svitlana ; Bachrach, Yoram; Armstrong, Michael; Sharma, Vijay Inferring Latent User Properties from Texts Published in Social Media (Demo) Inproceedings Proceedings of the Twenty-Ninth Conference on Artificial Intelligence (AAAI), Austin, TX, 2015. @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://engineering.jhu.edu/hltcoe/wp-content/uploads/sites/92/2016/11/17310_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} } |
Tang, M; Athreya, A; Sussman, D L; Lyzinski, V; Priebe, C E A nonparametric two-sample hypothesis testing problem for random dot product graphs Journal Article Bernoulli, Accepted for publication , 2015. @article{tang2014nonparametric, title = {A nonparametric two-sample hypothesis testing problem for random dot product graphs}, author = {Tang, M. and Athreya, A. and Sussman, D. L. and Lyzinski, V. and Priebe, C. E.}, year = {2015}, date = {2015-01-01}, journal = {Bernoulli}, volume = {Accepted for publication}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Gormley, Matt ; Eisner, Jason ; Dredze, Mark Approximation-Aware Dependency Parsing by Belief Propagation Journal Article 2015. @article{Gormley:2015sf, title = {Approximation-Aware Dependency Parsing by Belief Propagation}, author = {Gormley, Matt and Eisner, Jason and Dredze, Mark}, year = {2015}, date = {2015-01-01}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Cohen, Joanna; Ayers, John; Dredze, Mark Tobacco Watcher: Real-time Global Surveillance for Tobacco Control Inproceedings World Conference on Tobacco or Health (WCTOH), 2015. @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} } |
Benton, Adrian; Dredze, Mark Entity Linking for Spoken Language Inproceedings North American Chapter of the Association for Computational Linguistics (NAACL), 2015. @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} } 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. |
Paul, Michael; Dredze, Mark ; Broniatowski, David; Generous, Nicholas Worldwide Influenza Surveillance through Twitter Inproceedings AAAI Workshop on the World Wide Web and Public Health Intelligence, 2015. @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} } |
Peng, Nanyun; Dredze, Mark Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings Inproceedings Empirical Methods in Natural Language Processing (EMNLP), 2015. @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} } |
Yu, Mo; Gormley, Matt ; Dredze, Mark Combining Word Embeddings and Feature Embeddings for Fine-grained Relation Extraction Inproceedings North American Chapter of the Association for Computational Linguistics (NAACL), 2015. @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} } 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. |
Upcoming Events
Jul
8
Sun
all-day
IJCNN 2018
IJCNN 2018
Jul 8 – Jul 13 all-day
http://www.guide2research.com/conference/ijcnn-2018-international-joint-conference-on-neural-networks
