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Anomaly detection for random graphs using distributions of vertex invariants

Anomaly detection for random graphs using distributions of v...

引用

Annual Conference on Information Sciences and Systems (CISS)

作者： Nash Borges Glen A. Coppersmith Gerard G. L. Meyer Carey E. Priebe Human Language Technology Center of Excellence Department of Electrical and Computer Engineering Johns Hopkins University USA Human Language Technology Center of Excellence Department of Applied Mathematics and Statistics Johns Hopkins University USA

Anomaly detection is a longstanding problem with many applications in signal processing. We consider anomaly detection on graphs, a subject which has not previously had treatment in such depth. Our approach is inspired largely by previous work, where anomaly detection in an acoustic signal is accomplished by measuring and comparing the distribution of localized measurements to those available from a non-anomalous signal. In similar spirit, we proceed by comparing distributions of vertex invariants to those obtained from non-anomalous graphs. Specifically, we consider homogeneous Erdös-Rényi random graphs (where each vertex is connected independently with equal probability p) to be non-anomalous, and compare them to four classes of heterogeneous alternatives (where a subset of the vertices are connected according to a different process). Our contributions are (1) a novel method of incorporating information from vertex invariants for anomaly detection on graphs, (2) a principled approach to fusing information from an arbitrary number of such statistics, and (3) evaluation on several types of anomalous graphs. We demonstrate superior performance to available state-of-the-art approaches against the specific type of anomalies optimized for, and further demonstrate superior generalization to an entire class of anomalies.

关键词： Histograms Entropy Joints Erbium Training Acoustic measurements Image edge detection

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Anomaly detection for random graphs using distributions of vertex invariants

Anomaly detection for random graphs using distributions of v...

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Annual Conference on Information Sciences and Systems

作者： Borges, Nash Coppersmith, Glen A. Meyer, Gerard G. L. Priebe, Carey E. Johns Hopkins University Human Language Technology Center of Excellence United States Johns Hopkins University Department of Electrical and Computer Engineering United States Johns Hopkins University Department of Applied Mathematics and Statistics United States

ISBN: (纸本)9781424498475

Anomaly detection is a longstanding problem with many applications in signal processing. We consider anomaly detection on graphs, a subject which has not previously had treatment in such depth. Our approach is inspired largely by previous work [1]-[3], where anomaly detection in an acoustic signal is accomplished by measuring and comparing the distribution of localized measurements to those available from a non-anomalous signal. In similar spirit, we proceed by comparing distributions of vertex invariants to those obtained from non-anomalous graphs. Specifically, we consider homogeneous Erdös-Rényi random graphs (where each vertex is connected independently with equal probability p) to be non-anomalous, and compare them to four classes of heterogeneous alternatives (where a subset of the vertices are connected according to a different process). Our contributions are (1) a novel method of incorporating information from vertex invariants for anomaly detection on graphs, (2) a principled approach to fusing information from an arbitrary number of such statistics, and (3) evaluation on several types of anomalous graphs. We demonstrate superior performance to available state-of-the-art approaches against the specific type of anomalies optimized for, and further demonstrate superior generalization to an entire class of anomalies. © 2011 IEEE.

关键词： Graph theory

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Hierarchical Bayesian Models for Latent Attribute Detection in Social Media 5

Hierarchical Bayesian Models for Latent Attribute Detection ...

引用

5th International AAAI Conference on Weblogs and Social Media, ICWSM 2011

作者： Rao, Delip Paul, Michael Fink, Clay Yarowsky, David Oates, Timothy Coppersmith, Glen Human Language Technology Center of Excellence Johns Hopkins University BaltimoreMD21218 United States Applied Physics Laboratory Johns Hopkins University LaurelMD20723 United States University of Maryland Baltimore County BaltimoreMD21250 United States

ISBN: (纸本)9781577355052

We present several novel minimally-supervised models for detecting latent attributes of social media users, with a focus on ethnicity and gender. Previous work on ethnicity detection has used coarse-grained widely separated classes of ethnicity and assumed the existence of large amounts of training data such as the US census, simplifying the problem. Instead, we examine content generated by users in addition to name morphophonemics to detect ethnicity and gender. Further, we address this problem in a challenging setting where the ethnicity classes are more fine grained – ethnicity classes in Nigeria – and with very limited training data. Copyright © 2011, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Bayesian networks

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Estimating document frequencies in a speech corpus

Estimating document frequencies in a speech corpus

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

作者： Karakos, Damianos Dredze, Mark Church, Ken Jansen, Aren Khudanpur, Sanjeev Human Language Technology Center of Excellence Johns Hopkins University Baltimore MD United States Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD United States Department of Computer Science Johns Hopkins University Baltimore MD United States

ISBN: (纸本)9781467303675

Inverse Document Frequency (IDF) is an important quantity in many applications, including Information Retrieval. IDF is defined in terms of document frequency, df (w), the number of documents that mention w at least once. This quantity is relatively easy to compute over textual documents, but spoken documents are more challenging. This paper considers two baselines: (1) an estimate based on the 1-best ASR output and (2) an estimate based on expected term frequencies computed from the lattice. We improve over these baselines by taking advantage of repetition. Whatever the document is about is likely to be repeated, unlike ASR errors, which tend to be more random (Poisson). In addition, we find it helpful to consider an ensemble of language models. There is an opportunity for the ensemble to reduce noise, assuming that the errors across language models are relatively uncorrelated. The opportunity for improvement is larger when WER is high. This paper considers a pairing task application that could benefit from improved estimates of df. The pairing task inputs conversational sides from the English Fisher corpus and outputs estimates of which sides were from the same conversation. Better estimates of df lead to better performance on this task. © 2011 IEEE.

关键词： Random errors

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Establishing a human language technology center of excellence

Establishing a human language technology center of excellenc...

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作者： Strong, G. Eisner, J. Piatko, C. JHU Human Language Technology Center of Excellence Baltimore MD United States JHU Whiting School of Engineering Baltimore MD United States JHU Applied Physics Laboratory Laurel MD United States

JHU was awarded a long-term contract in January, 2007 to establish and operate a human language technology center of excellence (HLTCOE) near the JHU Homewood campus. The HLTCOE's research focused on advanced technology for automatically analyzing a wide range of speech text and document image data in multiple languages. The focus of the technical program was on automatic population of knowledge bases from text, proof-of-context experiments for robust speech technology and stream characterization from content. These projects addressed key issues in extracting information from large sources of text and speech.

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Creating speech and language data with Amazon's Mechanical Turk

Creating speech and language data with Amazon's Mechanical T...

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2010 Workshop on Creating speech and language Data with Amazon's Mechanical Turk, Mturk 2010 at the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2010

作者： Callison-Burch, Chris Dredze, Mark Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University United States

In this paper we give an introduction to using Amazon's Mechanical Turk crowdsourcing platform for the purpose of collecting data for human language technologies. We survey the papers published in the NAACL-2010 Workshop. 24 researchers participated in the workshop's shared task to create data for speech and language applications with $100. © 2010 Association for Computational Linguistics

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Sparse auto-associative neural networks: Theory and application to speech recognition

Sparse auto-associative neural networks: Theory and applicat...

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作者： Sivaram, G.S.V.S. Ganapathy, Sriram Hermansky, Hynek Center for Language and Speech Processing Human Language Technology Center of Excellence Johns Hopkins University United States

This paper introduces the sparse auto-associative neural network (SAANN) in which the internal hidden layer output is forced to be sparse. This is achieved by adding a sparse regularization term to the original reconstruction error cost function, and updating the parameters of the network to minimize the overall cost. We show applicability of this network to phoneme recognition by extracting sparse hidden layer outputs (used as features) from a network which is trained using perceptual linear prediction (PLP) cepstral coefficients in an unsupervised manner. Experiments with the SAANN features on a state-of-the-art TIMIT phoneme recognition system show a relative improvement in phoneme error rate of 5.1% over the baseline PLP features. © 2010 ISCA.

关键词： speech recognition

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Using Mechanical Turk to build machine translation evaluation sets

Using Mechanical Turk to build machine translation evaluatio...

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作者： Bloodgood, Michael Callison-Burch, Chris Human Language Technology Center of Excellence Johns Hopkins University United States Center for Language and Speech Processing Johns Hopkins University United States

Building machine translation (MT) test sets is a relatively expensive task. As MT becomes increasingly desired for more and more language pairs and more and more domains, it becomes necessary to build test sets for each case. In this paper, we investigate using Amazon's Mechanical Turk (MTurk) to make MT test sets cheaply. We find that MTurk can be used to make test sets much cheaper than professionally-produced test sets. More importantly, in experiments with multiple MT systems, we find that the MTurk-produced test sets yield essentially the same conclusions regarding system performance as the professionally-produced test sets yield. © 2010 Association for Computational Linguistics

关键词： Machine translation

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NLP on spoken documents without ASR

NLP on spoken documents without ASR

引用

Conference on Empirical Methods in Natural language processing, EMNLP 2010

作者： Dredze, Mark Jansen, Aren Coppersmith, Glen Church, Ken Human Language Technology Center of Excellence Johns Hopkins University United States Center for Language and Speech Processing Johns Hopkins University United States

ISBN: (纸本)1932432868

There is considerable interest in interdisciplinary combinations of automatic speech recognition (ASR), machine learning, natural language processing, text classification and information retrieval. Many of these boxes, especially ASR, are often based on considerable linguistic resources. We would like to be able to process spoken documents with few (if any) resources. Moreover, connecting black boxes in series tends to multiply errors, especially when the key terms are out-of-vocabulary (OOV). The proposed alternative applies text processing directly to the speech without a dependency on ASR. The method finds long (∼ 1 sec) repetitions in speech, and clusters them into pseudo-terms (roughly phrases). Document clustering and classification work surprisingly well on pseudoterms;performance on a Switchboard task approaches a baseline using gold standard manual transcriptions. © 2010 Association for Computational Linguistics.

关键词： Text processing

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Entity disambiguation for knowledge base population

Entity disambiguation for knowledge base population

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23rd International Conference on Computational Linguistics, Coling 2010

作者： Dredze, Mark Mcnamee, Paul Rao, Delip Gerber, Adam Finin, Tim Human Language Technology Center of Excellence Center for Language and Speech Processing Johns Hopkins University United States University of Maryland Baltimore County United States

The integration of facts derived from information extraction systems into existing knowledge bases requires a system to disambiguate entity mentions in the text. This is challenging due to issues such as non-uniform variations in entity names, mention ambiguity, and entities absent from a knowledge base. We present a state of the art system for entity disambiguation that not only addresses these challenges but also scales to knowledge bases with several million entries using very little resources. Further, our approach achieves performance of up to 95% on entities mentioned from newswire and 80% on a public test set that was designed to include challenging queries.

关键词： Information retrieval systems

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