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Entity clustering across languages

Entity clustering across languages

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2012 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL HLT 2012

作者： Green, Spence Andrews, Nicholas Gormley, Matthew R. Dredze, Mark Manning, Christopher D. Computer Science Department Stanford University United States Human Language Technology Center of Excellence Johns Hopkins University United States

ISBN: (纸本)1937284204

Standard entity clustering systems commonly rely on mention (string) matching, syntactic features, and linguistic resources like English WordNet. When co-referent text mentions appear in different languages, these techniques cannot be easily applied. Consequently, we develop new methods for clustering text mentions across documents and languages simultaneously, producing cross-lingual entity clusters. Our approach extends standard clustering algorithms with cross-lingual mention and context similarity measures. Crucially, we do not assume a pre-existing entity list (knowledge base), so entity characteristics are unknown. On an Arabic-English corpus that contains seven different text genres, our best model yields a 24.3% F1 gain over the baseline. © 2012 Association for Computational Linguistics.

关键词： Clustering algorithms

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Feature extraction using 2-D autoregressive models for speaker recognition

Feature extraction using 2-D autoregressive models for speak...

引用

Speaker and language Recognition Workshop, Odyssey 2012

作者： Ganapathy, Sriram Thomas, Samuel Hermansky, Hynek Dept. of ECE Johns Hopkins University United States Human Language Technology Center of Excellence Johns Hopkins University United States

ISBN: (纸本)9789810730932

The degradation in performance of a typical speaker verification system in noisy environments can be attributed to the mis-match in the features derived from clean training and noisy test conditions. The mis-match is severe in low-energy regions of the signal where noise dominates the speech signal. A robust feature extraction scheme should focus on the high-energy peaks in the time-frequency region. In this paper, we develop a signal analysis technique which attempts to model these high-energy peaks using two-dimensional (2-D) autoregressive (AR) models. The first AR model of the sub-band Hilbert envelopes is derived using frequency domain linear prediction (FDLP). Then, these all-pole envelopes from each sub-band are converted to short-term energy estimates and the energy values across various sub-bands are used as a sampled power spectral estimate for the second AR model. The output prediction coefficients from the second AR model are converted to cepstral coefficients and are used for speaker recognition. Experiments are performed using noisy versions of NIST 2010 speaker recognition evaluation (SRE) data with the state-of-art speaker recognition system. In these experiments, the proposed features provide significant improvements compared to baseline MFCC features (relative improvements of 30%). We also experiment on a large dataset of IARPA NIST 2011 speaker recognition challenge, where the 2-D AR model provides noticeable improvements (relative improvements of 15 − 20%). © Odyssey 2012 - Speaker and language Recognition Workshop. All rights reserved.

关键词： Large dataset

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Using Categorial Grammar to Label Translation Rules 12

Using Categorial Grammar to Label Translation Rules

引用

Workshop on Statistical Machine Translation

作者： Jonathan Weese Chris Callison-Burch Adam Lopez Human Language Technology Center of Excellence Johns Hopkins University

ISBN: (纸本)9781622765928;1622765923

Adding syntactic labels to synchronous context-free translation rules can improve performance, but labeling with phrase structure constituents, as in GHKM (Galley et al., 2004), excludes potentially useful translation rules. SAMT (Zollmann and Venugopal, 2006) introduces heuristics to create new non-constituent labels, but these heuristics introduce many complex labels and tend to add rarely-applicable rules to the translation grammar. We introduce a labeling scheme based on categorial grammar, which allows syntactic labeling of many rules with a minimal, well-motivated label set. We show that our labeling scheme performs comparably to SAMT on an Urdu-English translation task, yet the label set is an order of magnitude smaller, and translation is twice as fast.

关键词： Using Categorial Translation Translations Translation labeling scheme syntax Heuristics Translation Process Rule Fasting

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Putting human Assessments of Machine Translation Systems in Order 12

Putting Human Assessments of Machine Translation Systems in ...

引用

Workshop on Statistical Machine Translation

作者： Adam Lopez Human Language Technology Center of Excellence Johns Hopkins University

ISBN: (纸本)9781622765928

human assessment is often considered the gold standard in evaluation of translation systems. But in order for the evaluation to be meaningful, the rankings obtained from human assessment must be consistent and repeatable. Recent analysis by Bojar et al. (2011) raised several concerns about the rankings derived from human assessments of English-Czech translation systems in the 2010 Workshop on Machine Translation. We extend their analysis to all of the ranking tasks from 2010 and 2011, and show through an extension of their reasoning that the ranking is naturally cast as an instance of finding the minimum feedback arc set in a tournament, a well-known NP-complete problem. All instances of this problem in the workshop data are efficiently solvable, but in some cases the rankings it produces are surprisingly different from the ones previously published. This leads to strong caveats and recommendations for both producers and consumers of these rankings.

关键词： Translation systems machine translation system machine translation gold standard Reasoning human Workshops workshops Nondeterministic polynomial-time hard

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Streaming Analysis of Discourse Participants 12

Streaming Analysis of Discourse Participants

引用

Conference on Empirical Methods in Natural language processing

作者： Benjamin Van Durme Human Language Technology Center of Excellence Johns Hopkins University

ISBN: (纸本)9781622765034

Inferring attributes of discourse participants has been treated as a batch-processing task: data such as all tweets from a given author are gathered in bulk, processed, analyzed for a particular feature, then reported as a result of academic interest. Given the sources and scale of material used in these efforts, along with potential use cases of such analytic tools, discourse analysis should be reconsidered as a streaming challenge. We show that under certain common formulations, the batch-processing analytic framework can be decomposed into a sequential series of updates, using as an example the task of gender classification. Once in a streaming framework, and motivated by large data sets generated by social media services, we present novel results in approximate counting, showing its applicability to space efficient streaming classification.

关键词： Streaming Task data Batch processing Social Media Dataset TWITTER

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Name phylogeny: A generative model of string variation

Name phylogeny: A generative model of string variation

引用

2012 Joint Conference on Empirical Methods in Natural language processing and Computational Natural language Learning, EMNLP-CoNLL 2012

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

ISBN: (纸本)9781937284435

Many linguistic and textual processes involve transduction 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. © 2012 Association for Computational Linguistics.

关键词： Stochastic systems

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Query by babbling: A research agenda

Query by babbling: A research agenda

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1st ACM Workshop on Information and Knowledge Management for Developing Regions, IKM4DR 2012 - Co-located with CIKM 2012

作者： Oard, Douglas W. College of Information Studies and UMIACS University of Maryland College Park MD United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD United States

ISBN: (纸本)9781450317184

The Spoken Web, an interconnected collection of spoken content accessed through audio-only mobile phones, holds the promise of transforming information access for users in developing regions. The scale of the Spoken Web is, how- ever, limited because current speech retrieval technology is only affordably deployable for a handful of languages. This paper proposes rethinking the conventional keyword query paradigm to instead develop systems that support a longer, richer, and more fluid interaction style that is better suited to both the affordances of spoken interaction and to the lim- itations of current speech technology. Copyright 2012 ACM.

关键词： speech retrieval Spoken Web

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Multi-domain learning: When do domains matter?

Multi-domain learning: When do domains matter?

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2012 Joint Conference on Empirical Methods in Natural language processing and Computational Natural language Learning, EMNLP-CoNLL 2012

作者： Joshi, Mahesh Dredze, Mark Cohen, William W. Rose, Carolyn P. School of Computer Science Carnegie Mellon University Pittsburgh PA 15213 United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD 21211 United States

ISBN: (纸本)9781937284435

We present a systematic analysis of existing multi-domain learning approaches with respect to two questions. First, many multi-domain learning algorithms resemble ensemble learning algorithms. (1) Are multi-domain learning improvements the result of ensemble learning effects? Second, these algorithms are traditionally evaluated in a balanced class label setting, although in practice many multi-domain settings have domain-specific class label biases. When multi-domain learning is applied to these settings, (2) are multi-domain methods improving because they capture domain-specific class biases? An understanding of these two issues presents a clearer idea about where the field has had success in multi-domain learning, and it suggests some important open questions for improving beyond the current state of the art. © 2012 Association for Computational Linguistics.

关键词： Learning algorithms

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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

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

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

ISBN: (纸本)9781467303675

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. © 2011 IEEE.

关键词： Maximum likelihood

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Efficient discriminative training of long-span language models

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

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

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

ISBN: (纸本)9781467303675

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. © 2011 IEEE.

关键词： Computational linguistics

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