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# LM Approaches to Filtering - PowerPoint PPT Presentation

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

Richard Schwartz, BBN

LM/IR ARDA 2002

September 11-12, 2002

UMASS

• LM approach

• What is it?

• Why is it preferred?

• Controlling Filtering decision

• 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.

• 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.

• 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.

• 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

• 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

• 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

• 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.

• Tested in TDT

• Operating with small amounts of training data for each category

• 1 to 4 documents per event

• Robustness to changes over time

• 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.

• 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