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A Language Modeling Approach to Tracking

This paper presents a detailed analysis of a language modeling approach applied to tracking in Topic Detection and Tracking (TDT) systems, specifically highlighting filtering versus tracking methods. Key experiments explore the impact of stemming, normalization steps, and the merging of training stories on tracking efficiency. Our findings indicate that simple language models can be highly effective, emphasizing the importance of normalization and stemming, and demonstrating that an unbiased merging procedure aids in handling varying training story lengths. Future research will focus on refining models and applying unsupervised adaptation techniques.

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A Language Modeling Approach to Tracking

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  1. A Language Modeling Approach to Tracking Martijn Spitters & Wessel Kraaij TNO - TPD TDT2000 at NIST, Gaithersburg

  2. Outline • Introduction: • Filtering vs. Tracking • Tracking Model • Experiments: • stemming • Influence of normalisation steps • merging training stories • Conclusions

  3. Adaptive filtering vs. tracking • Adaptive filtering: • initial short topic statement, relevance judgements available ‘along the way’ • Individual threshold adaptation • Evaluation: Utility function • Tracking: • 1-4 training stories, no other relevance information • uniform threshold • Evaluation: Normalized detection Cost

  4. Changes in system • Model based on P(S|T) instead of P(Q|D) ==>reversed orientation • Uniform threshold requires careful normalization • Likelihood ratio • Story length normalization • Gaussian normalization

  5. TREC8 Adaptive filtering

  6. Tracking Model

  7. Normalization Story length Gaussian:

  8. Experiment 1: Stemming

  9. Experiment 2: Normalization

  10. Experiment 3: Merging Influence of the unbiased training story merging method on four TDT2 topics

  11. TDT2000 Evaluation Results (1)

  12. TDT2000 Evaluation Results (2)

  13. Conclusions • simple language models are effective • Normalisation is important • Stemming is important • Unbiased training story merging procedure seems to help when training story lengths differ substantially

  14. Future plans • Apply EM to learn optimal i • Refine background models • Apply expansion techniques • Unsupervised adaptation

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