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The application of statistical relational learning to a database of criminal and terrorist activity

April 29, 2010
  |
Conference Paper
Author:
Brian W. Delaney
…
Published in:
SIAM Conf. on Data Mining, 29 April - 1 May 2010.
Topic:
social network
R&D area:
R&D group:

Summary

We apply statistical relational learning to a database of criminal and terrorist activity to predict attributes and event outcomes. The database stems from a collection of news articles and court records which are carefully annotated with a variety of variables, including categorical and continuous fields. Manual analysis of this data can help inform decision makers seeking to curb violent activity within a region. We use this data to build relational models from historical data to predict attributes of groups, individuals, or events. Our first example involves predicting social network roles within a group under a variety of different data conditions. Collective classification can be used to boost the accuracy under data poor conditions. Additionally, we were able to predict the outcome of hostage negotiations using models trained on previous kidnapping events. The overall framework and techniques described here are flexible enough to be used to predict a variety of variables. Such predictions could be used as input to a more complex system to recognize intent of terrorist groups or as input to inform human decision makers.
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Summary

We apply statistical relational learning to a database of criminal and terrorist activity to predict attributes and event outcomes. The database stems from a collection of news articles and court records which are carefully annotated with a variety of variables, including categorical and continuous fields. Manual analysis of this data...

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The application of statistical relational learning to a database of criminal and terrorist activity

Detection and simulation of scenarios with hidden Markov models and event dependency graphs

March 15, 2010
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Conference Paper
Author:
William M. Campbell
…
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 15 March 2010, pp. 5434-5437.
Topic:
social network
R&D area:
R&D group:

Summary

The wide availability of signal processing and language tools to extract structured data from raw content has created a new opportunity for the processing of structured signals. In this work, we explore models for the simulation and recognition of scenarios - i.e., time sequences of structured data. For simulation, we construct two models - hidden Markov models (HMMs) and event dependency graphs. Combined, these two simulation methods allow the specification of dependencies in event ordering, simultaneous execution of multiple scenarios, and evolving networks of data. For scenario recognition, we consider the application of multi-grained HMMs. We explore, in detail, mismatch between training scenarios and simulated test scenarios. The methods are applied to terrorist scenario detection with a simulation coded by a subject matter expert.
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Summary

The wide availability of signal processing and language tools to extract structured data from raw content has created a new opportunity for the processing of structured signals. In this work, we explore models for the simulation and recognition of scenarios - i.e., time sequences of structured data. For simulation, we...

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Detection and simulation of scenarios with hidden Markov models and event dependency graphs

The MIT-LL/AFRL IWSLT-2008 MT System

December 1, 2009
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Conference Paper
Author:
Wade Shen
…
Published in:
Int. Workshop on Spoken Language Translation, IWSLT, 1-2 December 2009.
Topic:
machine translation
R&D area:
R&D group:

Summary

This paper describes the MIT-LL/AFRL statistical MT system and the improvements that were developed during the IWSLT 2008 evaluation campaign. As part of these efforts, we experimented with a number of extensions to the standard phrase-based model that improve performance for both text and speech-based translation on Chinese and Arabic translation tasks. We discuss the architecture of the MIT-LL/AFRL MT system, improvements over our 2007 system, and experiments we ran during the IWSLT-2008 evaluation. Specifically, we focus on 1) novel segmentation models for phrase-based MT, 2) improved lattice and confusion network decoding of speech input, 3) improved Arabic morphology for MT preprocessing, and 4) system combination methods for machine translation.
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Summary

This paper describes the MIT-LL/AFRL statistical MT system and the improvements that were developed during the IWSLT 2008 evaluation campaign. As part of these efforts, we experimented with a number of extensions to the standard phrase-based model that improve performance for both text and speech-based translation on Chinese and Arabic...

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The MIT-LL/AFRL IWSLT-2008 MT System

Modeling and detection techniques for counter-terror social network analysis and intent recognition

March 7, 2009
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Conference Paper
Author:
Clifford J. Weinstein
…
Published in:
2009 IEEE Aerospace Conf., March 7-14 2009.
Topic:
social network
R&D area:
R&D group:

Summary

In this paper, we describe our approach and initial results on modeling, detection, and tracking of terrorist groups and their intents based on multimedia data. While research on automated information extraction from multimedia data has yielded significant progress in areas such as the extraction of entities, links, and events, less progress has been made in the development of automated tools for analyzing the results of information extraction to ?connect the dots.? Hence, our Counter-Terror Social Network Analysis and Intent Recognition (CT-SNAIR) work focuses on development of automated techniques and tools for detection and tracking of dynamically-changing terrorist networks as well as recognition of capability and potential intent. In addition to obtaining and working with real data for algorithm development and test, we have a major focus on modeling and simulation of terrorist attacks based on real information about past attacks. We describe the development and application of a new Terror Attack Description Language (TADL), which is used as a basis for modeling and simulation of terrorist attacks. Examples are shown which illustrate the use of TADL and a companion simulator based on a Hidden Markov Model (HMM) structure to generate transactions for attack scenarios drawn from real events. We also describe our techniques for generating realistic background clutter traffic to enable experiments to estimate performance in the presence of a mix of data. An important part of our effort is to produce scenarios and corpora for use in our own research, which can be shared with a community of researchers in this area. We describe our scenario and corpus development, including specific examples from the September 2004 bombing of the Australian embassy in Jakarta and a fictitious scenario which was developed in a prior project for research in social network analysis. The scenarios can be created by subject matter experts using a graphical editing tool. Given a set of time ordered transactions between actors, we employ social network analysis (SNA) algorithms as a filtering step to divide the actors into distinct communities before determining intent. This helps reduce clutter and enhances the ability to determine activities within a specific group. For modeling and simulation purposes, we generate random networks with structures and properties similar to real-world social networks. Modeling of background traffic is an important step in generating classifiers that can separate harmless activities from suspicious activity. An algorithm for recognition of simulated potential attack scenarios in clutter based on Support Vector Machine (SVM) techniques is presented. We show performance examples, including probability of detection versus probability of false alarm tradeoffs, for a range of system parameters.
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Summary

In this paper, we describe our approach and initial results on modeling, detection, and tracking of terrorist groups and their intents based on multimedia data. While research on automated information extraction from multimedia data has yielded significant progress in areas such as the extraction of entities, links, and events, less...

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Modeling and detection techniques for counter-terror social network analysis and intent recognition

The MIT-LL/AFRL IWSLT-2007 MT System

October 15, 2007
  |
Conference Paper
Author:
Wade Shen
…
Published in:
Int. Workshop on Spoken Language Translation, IWSLT, 15-16 October 2007.
Topic:
machine translation
R&D area:
R&D group:

Summary

The MIT-LL/AFRL MT system implements a standard phrase-based, statistical translation model. It incorporates a number of extensions that improve performance for speech-based translation. During this evaluation our efforts focused on the rapid porting of our SMT system to a new language (Arabic) and novel approaches to translation from speech input. This paper discusses the architecture of the MIT-LL/AFRL MT system, improvements over our 2007 system, and experiments we ran during the IWSLT-2007 evaluation. Specifically, we focus on 1) experiments comparing the performance of confusion network decoding and direct lattice decoding techniques for speech machine translation, 2) the application of lightweight morphology for Arabic MT pre-processing and 3) improved confusion network decoding.
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Summary

The MIT-LL/AFRL MT system implements a standard phrase-based, statistical translation model. It incorporates a number of extensions that improve performance for speech-based translation. During this evaluation our efforts focused on the rapid porting of our SMT system to a new language (Arabic) and novel approaches to translation from speech input...

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The MIT-LL/AFRL IWSLT-2007 MT System

An efficient graph search decoder for phrase-based statistical machine translation

November 28, 2006
  |
Conference Paper
Author:
Brian W. Delaney
…
Published in:
Int. Workshop on Spoken Language Translation, 28 November 2006.
Topic:
machine translation
R&D area:
R&D group:

Summary

In this paper we describe an efficient implementation of a graph search algorithm for phrase-based statistical machine translation. Our goal was to create a decoder that could be used for both our research system and a real-time speech-to-speech machine translation demonstration system. The search algorithm is based on a Viterbi graph search with an A* heuristic. We were able to increase the speed of our decoder substantially through the use of on-the-fly beam pruning and other algorithmic enhancements. The decoder supports a variety of reordering constraints as well as arbitrary n-gram decoding. In addition, we have implemented disk based translation models and a messaging interface to communicate with other components for use in our real-time speech translation system.
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Summary

In this paper we describe an efficient implementation of a graph search algorithm for phrase-based statistical machine translation. Our goal was to create a decoder that could be used for both our research system and a real-time speech-to-speech machine translation demonstration system. The search algorithm is based on a Viterbi...

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An efficient graph search decoder for phrase-based statistical machine translation

The MIT-LL/AFRL IWSLT-2006 MT system

November 27, 2006
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Conference Paper
Author:
Wade Shen
…
Published in:
Proc. Int. Workshop on Spoken Language Translation, IWSLT, 27-28 November 2006.
Topic:
machine translation
R&D area:
R&D group:

Summary

The MIT-LL/AFRL MT system is a statistical phrase-based translation system that implements many modern SMT training and decoding techniques. Our system was designed with the long-term goal of dealing with corrupted ASR input and limited amounts of training data for speech-to-speech MT applications. This paper will discuss the architecture of the MIT-LL/AFRL MT system, improvements over our 2005 system, and experiments with manual and ASR transcription data that were run as part of the IWSLT-2006 evaluation campaign.
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Summary

The MIT-LL/AFRL MT system is a statistical phrase-based translation system that implements many modern SMT training and decoding techniques. Our system was designed with the long-term goal of dealing with corrupted ASR input and limited amounts of training data for speech-to-speech MT applications. This paper will discuss the architecture of...

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The MIT-LL/AFRL IWSLT-2006 MT system

Toward an interagency language roundtable based assessment of speech-to-speech translation capabilitites

August 12, 2006
  |
Conference Paper
Author:
Douglas A. Jones
…
Published in:
AMTA 2006, 7th Biennial Conf. of the Association for Machine Translation in the Americas, 8-12 August 2006.
Topic:
machine translation
R&D area:
R&D group:

Summary

We present observations from three exercises designed to map the effective listening and speaking skills of an operator of a speech-to-speech translation system (S2S) to the Interagency Language Roundtable (ILR) scale. Such a mapping is nontrivial, but will be useful for government and military decision makers in managing expectations of S2S technology. We observed domain-dependent S2S capabilities in the ILR range of Level 0+ to Level 1, and interactive text-based machine translation in the Level 3 range.
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Summary

We present observations from three exercises designed to map the effective listening and speaking skills of an operator of a speech-to-speech translation system (S2S) to the Interagency Language Roundtable (ILR) scale. Such a mapping is nontrivial, but will be useful for government and military decision makers in managing expectations of...

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Toward an interagency language roundtable based assessment of speech-to-speech translation capabilitites

The MIT-LL/AFRL MT System

October 24, 2005
  |
Conference Paper
Author:
Wade Shen
…
Published in:
Int. Workshop on Spoken Language Translation, IWSLT, 24-25 October 2005.
Topic:
machine translation
R&D area:
R&D group:

Summary

The MITLL/AFRL MT system is a statistical phrase-based translation system that implements many modern SMT training and decoding techniques. Our system was designed with the long term goal of dealing with corrupted ASR input for Speech-to-Speech MT applications. This paper will discuss the architecture of the MITLL/AFRL MT system, and experiments with manual and ASR transcription data that were run as part of the IWSLT-2005 Chinese-to-English evaluation campaign.
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Summary

The MITLL/AFRL MT system is a statistical phrase-based translation system that implements many modern SMT training and decoding techniques. Our system was designed with the long term goal of dealing with corrupted ASR input for Speech-to-Speech MT applications. This paper will discuss the architecture of the MITLL/AFRL MT system, and...

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The MIT-LL/AFRL MT System
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