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Name Phylogeny: A Generative Model of String Variation 12

Name Phylogeny: A Generative Model of String Variation

引用

Conference on Empirical Methods in Natural language processing

作者： Nicholas Andrews Jason Eisner Mark Dredze Department of Computer Science and Human Language Technology Center of Excellence Johns Hopkins University 3400 N. Charles St. Baltimore MD 21218 USA

ISBN: (纸本)9781622765034

Many linguistic and textual processes involve transduc-tion of strings. We show how to learn a stochastic transducer from an unorganized collection of strings (rather than string pairs). The role of the transducer is to organize the collection. Our generative model explains similarities among the strings by supposing that some strings in the collection were not generated ab initio, but were instead derived by transduction from other, "similar" strings in the collection. Our variational EM learning algorithm alternately reestimates this phylogeny and the transducer parameters. The final learned transducer can quickly link any test name into the final phylogeny, thereby locating variants of the test name. We find that our method can effectively find name variants in a corpus of web strings used to refer to persons in Wikipedia, improving over standard untrained distances such as Jaro-Winkler and Leven-shtein distance.

关键词： Cosmic strings Transducers Phylogeny collection Transduction

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Joshua 3.0: Syntax-based Machine Translation with the Thrax Grammar Extractor 6

Joshua 3.0: Syntax-based Machine Translation with the Thrax ...

引用

6thWorkshop on Statistical Machine Translation, WMT 2011

作者： Weese, Jonathan Ganitkevitch, Juri Callison-Burch, Chris Post, Matt Lopez, Adam 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)9781937284121

We present progress on Joshua, an open-source decoder for hierarchical and syntax-based machine translation. The main focus is describing Thrax, a flexible, open source synchronous context-free grammar extractor. Thrax extracts both hierarchical (Chiang, 2007) and syntax-augmented machine translation (Zollmann and Venugopal, 2006) grammars. It is built on Apache Hadoop for efficient distributed performance, and can easily be extended with support for new grammars, feature functions, and output formats. © 2011 Association for Computational Linguistics

关键词： Machine translation

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Malpractice and malcontent: Analyzing medical complaints in twitter

Malpractice and malcontent: Analyzing medical complaints in ...

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2012 AAAI Fall Symposium

作者： Nakhasi, Atul Passarella, Ralph J. Bell, Sarah G. Paul, Michael J. Dredze, Mark Pronovost, PeterJ Johns Hopkins University School of Medicine Johns Hopkins University Baltimore MD United States Departments of Anesthesiology/Critical Care Medicine and Surgery Johns Hopkins University Baltimore MD United States Department of Computer Science and Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD United States University of Michigan Medical School Ann Arbor MI United States

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Efficient spoken term discovery using randomized algorithms

Efficient spoken term discovery using randomized algorithms

引用

2011 IEEE Workshop on Automatic speech Recognition and Understanding, ASRU 2011

作者： Jansen, Aren Van Durme, Benjamin Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University 3400 N. Charles St. Baltimore MD 21218 United States

ISBN: (纸本)9781467303675

Spoken term discovery is the task of automatically identifying words and phrases in speech data by searching for long repeated acoustic patterns. Initial solutions relied on exhaustive dynamic time warping-based searches across the entire similarity matrix, a method whose scalability is ultimately limited by the O(n2) nature of the search space. Recent strategies have attempted to improve search efficiency by using either unsupervised or mismatched-language acoustic models to reduce the complexity of the feature representation. Taking a completely different approach, this paper investigates the use of randomized algorithms that operate directly on the raw acoustic features to produce sparse approximate similarity matrices in O(n) space and O(n log n) time. We demonstrate these techniques facilitate spoken term discovery performance capable of outperforming a model-based strategy in the zero resource setting. © 2011 IEEE.

关键词： randomized algorithms similarity matrix Scalability Searches Acoustic models Acoustics

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New ℌ∞ bounds for the recursive least squares algorithm exploiting input structure

New ℌ∞ bounds for the recursive least squares algorithm ex...

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International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Koby Crammer Alex Kulesza Mark Dredze Department of Electrical Engineering Technion-Israel Institute of Technology Haifa Israel Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD USA

The recursive least squares (RLS) algorithm is well known and has been widely used for many years. Most analyses of RLS have assumed statistical properties of the data or the noise process, but recent robust ℌ ∞ analyses have been used to bound the ratio of the performance of the algorithm to the total noise. In this paper, we provide an additive analysis bounding the difference between performance and noise. Our analysis provides additional convergence guarantees in general, and particular benefits for structured input data. We illustrate the analysis using human speech and white noise.

关键词： Noise Additives Signal processing algorithms Algorithm design and analysis speech Learning systems Prediction algorithms

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Bootstrapping a spoken language identification system using unsupervised integrated sensing and processing decision trees

Bootstrapping a spoken language identification system using ...

引用

2011 IEEE Workshop on Automatic speech Recognition and Understanding, ASRU 2011

作者： Huang, Shuai Karakos, Damianos Coppersmith, Glen A. Church, Kenneth W. Siniscalchi, Sabato Marco Center for Language and Speech Processing Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States Kore University of Enna Enna Italy

ISBN: (纸本)9781467303675

In many inference and learning tasks, collecting large amounts of labeled training data is time consuming and expensive, and oftentimes impractical. Thus, being able to efficiently use small amounts of labeled data with an abundance of unlabeled data-the topic of semi-supervised learning (SSL) [1]-has garnered much attention. In this paper, we look at the problem of choosing these small amounts of labeled data, the first step in a bootstrapping paradigm. Contrary to traditional active learning where an initial trained model is employed to select the unlabeled data points which would be most informative if labeled, our selection has to be done in an unsupervised way, as we do not even have labeled data to train an initial model. We propose using unsupervised clustering algorithms, in particular integrated sensing and processing decision trees (ISPDTs) [2], to select small amounts of data to label and subsequently use in SSL (e.g. transductive SVMs). In a language identification task on the CallFriend1 and 2003 NIST language Recognition Evaluation corpora [3], we demonstrate that the proposed method results in significantly improved performance over random selection of equivalently sized training data. © 2011 IEEE.

关键词： Decision trees

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WikiTopics: What is Popular on Wikipedia and Why 11

WikiTopics: What is Popular on Wikipedia and Why

引用

Workshop on Automatic Summarization for Different Genres, Media and languages

作者： Byung Gyu Ahn Benjamin Van Durme Chris Callison-Burch Center for Language and Speech Processing Center for Language and Speech Processing Human Language Technology Center of Excellence Johns Hopkins University

ISBN: (纸本)9781618392480

We establish a novel task in the spirit of news summarization and topic detection and tracking (TDT): daily determination of the topics newly popular with Wikipedia readers. Central to this effort is a new public dataset consisting of the hourly page view statistics of all Wikipedia articles over the last three years. We give baseline results for the tasks of: discovering individual pages of interest, clustering these pages into coherent topics, and extracting the most relevant summarizing sentence for the reader. When compared to human judgements, our system shows the viability of this task, and opens the door to a range of exciting future work.

关键词： Wikipedia DOOR Reader Device Component Themes Religious Missions Viability

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Efficient discriminative training of long-span language models

Efficient discriminative training of long-span language mode...

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IEEE Workshop on Automatic speech Recognition and Understanding

作者： Ariya Rastrow Mark Dredze Sanjeev Khudanpur Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University Baltimore MD USA

Long-span language models, such as those involving syntactic dependencies, produce more coherent text than their n-gram counterparts. However, evaluating the large number of sentence-hypotheses in a packed representation such as an ASR lattice is intractable under such long-span models both during decoding and discriminative training. The accepted compromise is to rescore only the N-best hypotheses in the lattice using the long-span LM. We present discriminative hill climbing, an efficient and effective discriminative training procedure for long-span LMs based on a hill climbing rescoring algorithm [1]. We empirically demonstrate significant computational savings as well as error-rate reduction over N-best training methods in a state of the art ASR system for Broadcast News transcription.

关键词： Training Lattices Acoustics Syntactics speech Feature extraction Complexity theory

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Efficient spoken term discovery using randomized algorithms

Efficient spoken term discovery using randomized algorithms

引用

IEEE Workshop on Automatic speech Recognition and Understanding

作者： Aren Jansen Benjamin Van Durme Human Language Technology Center of Excellence & The Center for Language and Speech Processing Johns Hopkins University Baltimore MD USA

Spoken term discovery is the task of automatically identifying words and phrases in speech data by searching for long repeated acoustic patterns. Initial solutions relied on exhaustive dynamic time warping-based searches across the entire similarity matrix, a method whose scalability is ultimately limited by the O(n 2 ) nature of the search space. Recent strategies have attempted to improve search efficiency by using either unsupervised or mismatched-language acoustic models to reduce the complexity of the feature representation. Taking a completely different approach, this paper investigates the use of randomized algorithms that operate directly on the raw acoustic features to produce sparse approximate similarity matrices in O(n) space and O(n log n) time. We demonstrate these techniques facilitate spoken term discovery performance capable of outperforming a model-based strategy in the zero resource setting.

关键词： Approximation methods Acoustics Sparse matrices speech Approximation algorithms Vectors Image segmentation

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Adapting n-gram maximum entropy language models with conditional entropy regularization

Adapting n-gram maximum entropy language models with conditi...

引用

IEEE Workshop on Automatic speech Recognition and Understanding

作者： Ariya Rastrow Mark Dredze Sanjeev Khudanpur Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University Baltimore MD USA

Accurate estimates of language model parameters are critical for building quality text generation systems, such as automatic speech recognition. However, text training data for a domain of interest is often unavailable. Instead, we use semi-supervised model adaptation; parameters are estimated using both unlabeled in-domain data (raw speech audio) and labeled out of domain data (text.) In this work, we present a new semi-supervised language model adaptation procedure for Maximum Entropy models with n-gram features. We augment the conventional maximum likelihood training criterion on out-of-domain text data with an additional term to minimize conditional entropy on in-domain audio. Additionally, we demonstrate how to compute conditional entropy efficiently on speech lattices using first- and second-order expectation semirings. We demonstrate improvements in terms of word error rate over other adaptation techniques when adapting a maximum entropy language model from broadcast news to MIT lectures.

关键词： Entropy Adaptation models Training Lattices speech Data models Computational modeling

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