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Relevance-Based Language Models

Relevance-Based Language Models. Victor Lavrenko and W.Bruce Croft Department of Computer Science University of Massachusetts, Amherst, MA 01003 SIGIR 2001 Presented by Yi-Ting. Outline. Introduction

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Relevance-Based Language Models

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  1. Relevance-Based Language Models Victor Lavrenko and W.Bruce Croft Department of Computer Science University of Massachusetts, Amherst, MA 01003 SIGIR 2001 Presented by Yi-Ting

  2. Outline • Introduction • Related Work－Classical Probabilistic Model、Language Modeling Approaches • Relevance Model • Experimental Results • Conclusions

  3. introduction • A departure from the traditional models of relevance and emergence of language modeling frameworks. • To introduce a technique for constructing a relevance model from the query alone and compare resulting query-based relevance models to language models constructed with training data.

  4. Related Work • Classical Probabilistic Models • Probability ranking principle：P(D|R)/P(D|N) • Generative language models：P(w|R)/P(w|N) • Binary Independence Model • multiple-Bernoulli language models： • 2-Poisson Model

  5. Related Work • Language Modeling Approaches • Focused on viewing documents themselves as models and queries as strings of text randomly sampled from these models. • By Ponte and Croft, • By Miller et al., Song and Croft, • Berger and Lafferty view the query Q as a potential translation of the document D, and use powerful estimation techniques.

  6. Relevance Model • To refer to a mechanism that determines the probability P(w|R) of observing a word w in the documents relevant. • Justified way of estimating the relevance model when no training data is available. • Ranking with a Relevance Model：

  7. Relevance Model • Assume that the query is a sample from any specific document model. Instead we assume that both the query and the documents are samples from an unknown relevance model R.

  8. Relevance Model • Let Q=q…qk. The probability of w to the conditional probability of observing w given that we just observed q1…qk:

  9. Relevance Model • Method1：i.i.d sampling • Assume that the query q…qk words and the w in relevant documents are sampled identically and independently from a unigram distribution MR.

  10. Relevance Model • Method2：conditional sampling • Fixing a value of w according to some prior P(w). Then pick a distribution Mi according to P(Mi|w), sample the query word qi from Mi with probability P(qi|Mi). • Assume the query words q…qk to be independent of each other, but keep their dependence on w：

  11. Relevance Model • Method 2 performs slightly better in terms of retrieval effectiveness and tracking errors.

  12. Relevance Model • Estimation Details • Set the query prior P(q…qk) in equation(6) to be： • P(w) to be: • P(w|MD) to be: • P(Mi|w) to be: P(Mi|w) = P(w|Mi)P(w)/P(Mi)

  13. Experimental Results • Model cross-entropy • Cross-entropy is an information-theoretic measure of distance between two distribution, measured bits. • Both models exhibit lowest cross-entropy around 50 documents, however the model estimated with Method 2 achieves lower absolute cross-entropy.

  14. Experimental Results

  15. Experimental Results • TREC ad-hoc retrieval • Two query sets: TREC title queries 101-150 and 151-200. • Baseline: the performance of the language modeling approach, where we rank documents by their probability of generating the query. • Average precision、Wilcoxon test、R_Precision • Relevance models is an attractive choice in high-precision applications.

  16. Experimental Results

  17. Experimental Results • TDT topic tracking • The tracking task in TDT is evaluated using Detection Error Tradeoff (DET) : the tradeoff between Misses and False errors. • Ran the tracking system on a modified task. • TDT tracking systems with 1 to 4 training examples.

  18. Experimental Results

  19. Conclusions • Our technique is also relatively insensitive to the number of documents used in “expansion”. • our model is simple to implement and does not require any training data. • There are a number of interesting directions for further investigation of relevance models. • Our method produces accurate models of relevance, which leads to significantly improved retrieval performance. • demonstrated that unsupervised Relevance Models can be competitive with supervised topic models in TDT.

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