Task:
1.IIR Chapter 11 and Chapter 12
If we have some known relevant and nonrelevant documents, then we can straightforwardly start to estimate the probability of a term t appearing in a relevant document, and that this could be the basis of a classifier that decides whether documents are relevant or not. Chapter 11 introduced some probablistic-based IR model, such as Binary Inde-Model and some more advance one like Bayesian Network Model. I'm wondering that there is the fact that some terms might be relevent between themselves, if the probablity of relevance might be biased if calculated in the way that ignoring the relevance between terms?
The language modeling approach to IR, in Chapter 12, directly models the idea: a document is a good match to a query if the document model is likely to generate the query, which will in turn happen if the document contains the query words often. Such language model includes Query Likihood Model. I think the language modeling is still based on probability, but adding some probabilistic relevance between the sequence of query and document.
The language modeling approach to IR, in Chapter 12, directly models the idea: a document is a good match to a query if the document model is likely to generate the query, which will in turn happen if the document contains the query words often. Such language model includes Query Likihood Model. I think the language modeling is still based on probability, but adding some probabilistic relevance between the sequence of query and document.
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