Lm approaches to filtering
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LM Approaches to Filtering. Richard Schwartz, BBN LM/IR ARDA 2002 September 11-12, 2002 UMASS. Topics. LM approach What is it? Why is it preferred? Controlling Filtering decision. What is LM Approach?. We distinguish all ‘statistical’ approaches from ‘probabilistic’ approaches.

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LM Approaches to Filtering

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Lm approaches to filtering

LM Approaches to Filtering

Richard Schwartz, BBN

LM/IR ARDA 2002

September 11-12, 2002

UMASS


Topics

Topics

  • LM approach

    • What is it?

    • Why is it preferred?

  • Controlling Filtering decision


What is lm approach

What is LM Approach?

  • We distinguish all ‘statistical’ approaches from ‘probabilistic’ approaches.

  • The tf-idf metric computes various statistics of words and documents.

  • By ‘probabilistic’ approaches, we (I) mean methods where we compute the probability of a document being relevant to a user’s need, given the query, the document, and the rest of the world, using a formula that arguably computes

    P(Doc is Relevant | Query, Document, Collection, etc.)

  • If we use Bayes’ rule, we end up with the prior for each document, p(Doc is Relevant | Everything except Query) and the likelihood of the query p(Q | Doc is Relevant)

  • The LM approach is a solution to the second part of this.

  • The prior probability component is also important.


What it is not

What it is not

  • If we compute a LM for the query and a document and ask the probability that the two underlying LMs are the same, I would NOT call this a posterior probability model.

  • The LMs would not be expected to be the same even with long queries.


Issues in lm approaches for filtering

Issues in LM Approaches for Filtering

  • We (ideally) have three sets of documents:

    • Positive documents

    • Negative documents

    • Large corpus of unknown (mostly negative) documents

  • We can estimate a model for both positive and negative documents

    • We can find more positive documents in large corpus

    • We use large corpus to smooth models from positive and negative documents

  • We compute the probability of each of each new document given each of the models

  • The log of the ratio of these two likelihoods is a score that indicates whether the document is positive or negative.


Language modeling choices

Language Modeling Choices

  • We can model the probability of the document given the topic in many ways.

  • A simple unigram mixture works surprisingly well.

    • Weighted mixture of distributions from the topic training and the full corpus

  • We improve over the ‘naïve Bayes’ model significantly by using the Estimate Maximize technique

  • We can extend the model in many ways:

    • Ngram model of words

    • Phrases: proper names, collocations

  • Because we use a formal generative model, we know how to incorporate any effect we want.

    • E.g., probability of features of top-5 documents given some document is relevant


How to set the threshold

How to Set the Threshold

  • For filtering, we are required to make a hard decision of whether to accept the document, rather than just rank the documents.

  • Problems:

    • The score for a particular document depends on many factors that are not important for the decision

      • Length of document

      • Percentage of low-likelihood words

    • The range of scores depends on the particular topic.

  • Would like to map the score for any document and topic into a real posterior probability


Score normalization techniques

Score Normalization Techniques

  • By using the relative score for two models, we remove some of the variance due to the particular document.

  • We can normalize for the peculiarities of the topic by computing the distribution of scores for Off-Topic documents.

  • Advantages of using Off-Topic documents:

    • We have a very large number of documents

    • We can fix the probability of false alarms


The bottom line

The Bottom Line

  • For TDT tracking, the probabilistic approach to modeling the document and to score normalization results in better performance, whether for mono-language, cross-language, speech recognition output, etc.

  • Large improvement will come after multiple sites start using similar techniques.


Grand challenges

Grand Challenges

  • Tested in TDT

    • Operating with small amounts of training data for each category

      • 1 to 4 documents per event

    • Robustness to changes over time

      • adaptation

    • Multi-lingual domains

    • How to set threshold for filtering

    • Using model of ‘eventness’

  • Large hierarchical category sets

    • How to use the structure

  • Effective use of prior knowledge

  • Predicting performance and characterizing classes

  • Need a task where both the discriminative and the LM approach will be tested.


What do you really want

What do you really want?

  • If a user provides a document about the 9/11 World Trade Center crash and says they want “more like this”, do they want documents about:

    • Airplane crashes

    • Terrorism

    • Building fires

    • Injuries and Death

    • Some combination of the above

  • In general, we need a way to clarify which combination of topics the user wants

  • In TDT, we predefine the task to mean we want more about this specific event (and not about some other terrorist airplane crash into a building).


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