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Named entity recognition for Chinese social media with jointly trained embeddings

Named entity recognition for Chinese social media with joint...

引用

Conference on Empirical Methods in Natural language processing, EMNLP 2015

作者： Peng, Nanyun Dredze, Mark Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781941643327

We consider the task of named entity recognition for Chinese social media. The long line of work in Chinese NER has focused on formal domains, and NER for social media has been largely restricted to English. We present a new corpus of Weibo messages annotated for both name and nominal mentions. Additionally, we evaluate three types of neural embeddings for representing Chinese text. Finally, we propose a joint training objective for the embeddings that makes use of both (NER) labeled and unlabeled raw text. Our methods yield a 9% improvement over a stateof-the-art baseline. © 2015 Association for Computational Linguistics.

关键词： Social networking (online)

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Parallel training of DNNs with natural gradient and parameter averaging 3

Parallel training of DNNs with natural gradient and paramete...

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3rd International Conference on Learning Representations, ICLR 2015

作者： Povey, Daniel Zhang, Xiaohui Khudanpur, Sanjeev Center for Language and Speech Processing and Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21218 United States

We describe the neural-network training framework used in the Kaldi speech recognition toolkit, which is geared towards training DNNs with large amounts of training data using multiple GPU-equipped or multi-core machines. In order to be as hardware-agnostic as possible, we needed a way to use multiple machines without generating excessive network traffic. Our method is to average the neural network parameters periodically (typically every minute or two), and redistribute the averaged parameters to the machines for further training. Each machine sees different data. By itself, this method does not work very well. However, we have another method, an approximate and efficient implementation of Natural Gradient for Stochastic Gradient Descent (NG-SGD), which seems to allow our periodic-averaging method to work well, as well as substantially improving the convergence of SGD on a single machine. © 2015 International Conference on Learning Representations, ICLR. All rights reserved.

关键词： speech recognition

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Summary of the 2015 NIST language recognition i-vector machine learning challenge

Summary of the 2015 NIST language recognition i-vector machi...

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Speaker and language Recognition Workshop, Odyssey 2016

作者： Tong, Audrey Greenberg, Craig Martin, Alvin Bansé, Désiré Howard, John Zhao, Hui Doddington, George Garcia-Romero, Daniel McCree, Alan Reynolds, Douglas Singer, Elliot Hernández-Cordero, Jaime Mason, Lisa National Institute of Standards and Technology United States Human Language Technology Center of Excellence Johns Hopkins University United States MIT Lincoln Laboratory United States U.S. Department of Defense United States

In 2015 NIST coordinated the first language recognition evaluation (LRE) that used i-vectors as input, with the goals of attracting researchers outside of the speech processing community to tackle the language recognition problem, exploring new ideas in machine learning for use in language recognition, and improving recognition accuracy. The language Recognition i-Vector Machine Learning Challenge, taking place over a period of four months, was well-received with 56 participants from 44 unique sites and over 3700 submissions, surpassing the participation levels of all previous traditional track LREs. The results of 46 of the 56 participants were better than the provided baseline system, with the best system achieving approximately 55% relative improvement over the baseline. © Odyssey 2016: Speaker and language Recognition Workshop. All rights reserved.

关键词： speech processing

来源：评论

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Geolocation for Twitter: Timing matters 15

Geolocation for Twitter: Timing matters

引用

15th Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2016

作者： Dredze, Mark Osborne, Miles Kambadur, Prabhanjan Bloomberg L.P. 731 Lexington Ave New YorkNY10022 United States Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21211 United States

ISBN: (纸本)9781941643914

Automated geolocation of social media messages can benefit a variety of downstream applications. However, these geolocation systems are typically evaluated without attention to how changes in time impact geolocation. Since different people, in different locations write messages at different times, these factors can significantly vary the performance of a geolocation system over time. We demonstrate cyclical temporal effects on geolocation accuracy in Twitter, as well as rapid drops as test data moves beyond the time period of training data. We show that temporal drift can effectively be countered with even modest online model updates. ©2016 Association for Computational Linguistics.

关键词： Timing circuits

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Multiview LSA: Representation learning via generalized CCA

Multiview LSA: Representation learning via generalized CCA

引用

Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2015

作者： Rastogi, Pushpendre Van Durme, Benjamin Arora, Raman Center for Language and Speech Processing Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States

ISBN: (纸本)9781941643495

Multiview LSA (MVLSA) is a generalization of Latent Semantic Analysis (LSA) that supports the fusion of arbitrary views of data and relies on Generalized Canonical Correlation Analysis (GCCA). We present an algorithm for fast approximate computation of GCCA, which when coupled with methods for handling missing values, is general enough to approximate some recent algorithms for inducing vector representations of words. Experiments across a comprehensive collection of test-sets show our approach to be competitive with the state of the art. © 2015 Association for Computational Linguistics.

关键词： Semantics

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Performance monitoring for automatic speech recognition in noisy multi-channel environments

Performance monitoring for automatic speech recognition in n...

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2016 IEEE Workshop on Spoken language technology, SLT 2016

作者： Meyer, Bernd T. Mallidi, Sri Harish Castro Martínez, Angel Mario Paya-Vaya, Guillermo Kayser, Hendrik Hermansky, Hynek Center for Language and Speech Processing Johns Hopkins University BaltimoreMD United States Medizinische Physik Carl von Ossietzky Universität Oldenburg Germany Institute of Microelectronic Systems Leibniz Universität Hannover Germany 4Cluster of Excellence Hearing4all Germany

ISBN: (纸本)9781509049035

In many applications of machine listening it is useful to know how well an automatic speech recognition system will do before the actual recognition is performed. In this study we investigate different performance measures with the aim of predicting word error rates (WERs) in spatial acoustic scenes in which the type of noise, the signal-to-noise ratio, parameters for spatial filtering, and the amount of reverberation are varied. All measures under consideration are based on phoneme posteriorgrams obtained from a deep neural net. While frame-wise entropy exhibits only medium predictive power for factors other than additive noise, we found the medium temporal distance between posterior vectors (M-Measure) as well as matched phoneme filters (MaP) to exhibit excellent correlations with WER across all conditions. Since our results were obtained with simulated behind-the-ear hearing aid signals, we discuss possible applications for speech-aware hearing devices. © 2016 IEEE.

关键词： Audition

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Demographer: Extremely Simple Name Demographics 1

Demographer: Extremely Simple Name Demographics

引用

EMNLP 2016 1st Workshop on Natural language processing and Computational Social Science, NLP + CSS 2016

作者： Knowles, Rebecca Carroll, Josh Dredze, Mark Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Qntfy CrownsvilleMD21032 United States Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21211 United States

ISBN: (纸本)9781945626265

The lack of demographic information available when conducting passive analysis of social media content can make it difficult to compare results to traditional survey results. We present DEMOGRAPHER,1 a tool that predicts gender from names, using name lists and a classifier with simple character-level features. By relying only on a name, our tool can make predictions even without extensive user-authored content. We compare DEMOGRAPHER to other available tools and discuss differences in performance. In particular, we show that DEMOGRAPHER performs well on Twitter data, making it useful for simple and rapid social media demographic inference. ©2016 Association for Computational Linguistics.

关键词： Surveys

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Learning to predict script events from domain-specific text 4

Learning to predict script events from domain-specific text

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4th Joint Conference on Lexical and Computational Semantics, *SEM 2015

作者： Rudinger, Rachel Demberg, Vera Modi, Ashutosh Van Durme, Benjamin Pinkal, Manfred Center for Language and Speech Processing United States Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD United States MMCI Cluster of Excellence Saarland University Saarbrucken Germany

ISBN: (纸本)9781941643396

The automatic induction of scripts (Schank and Abelson, 1977) has been the focus of many recent works. In this paper, we employ a variety of these methods to learn Schank and Abelson's canonical restaurant script, using a novel dataset of restaurant narratives we have compiled from a website called "Dinners from Hell." Our models learn narrative chains, script-like structures that we evaluate with the "narrative cloze" task (Chambers and Jurafsky, 2008).

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Combining word embeddings and feature embeddings for fine-grained relation extraction

Combining word embeddings and feature embeddings for fine-gr...

引用

Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2015

作者： Yu, Mo Gormley, Matthew R. Dredze, Mark Machine Intelligence and Translation Lab Harbin Institute of Technology Harbin China Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781941643495

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 maintaing 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. © 2015 Association for Computational Linguistics.

关键词： Extraction

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FrameNet+: Fast paraphrastic tripling of FrameNet 53

FrameNet+: Fast paraphrastic tripling of FrameNet

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53rd Annual Meeting of the Association for Computational Linguistics and 7th International Joint Conference on Natural language processing of the Asian Federation of Natural language processing, ACL-IJCNLP 2015

作者： Pavlick, Ellie Wolfe, Travis Rastogi, Pushpendre Callison-Burch, Chris Dredze, Mark Van Durme, Benjamin Computer and Information Science Department University of Pennsylvania United States Center for Language and Speech Processing Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States

ISBN: (纸本)9781510808461

We increase the lexical coverage of FrameNet through automatic paraphrasing. We use crowdsourcing to manually filter out bad paraphrases in order to ensure a high-precision resource. Our expanded FrameNet contains an additional 22K lexical units, a 3-fold increase over the current FrameNet, and achieves 40% better coverage when evaluated in a practical setting on New York Times data. © 2015 Association for Computational Linguistics.

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