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derives from the work of Yu and his collaborators [28,29]...According to Doyle [32]p
...The model in this chapter also connects with two other ideas in earlier research
...or in words,for any document the probability of relevance is inversely proportional the probability with which it will occur on a random basis
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subsets differing in the extent to which they are about a word w then the distribution of w can be described by a mixture of two Poisson distributions
...here p 1 is the probability of a random document belonging to one of the subsets and x 1 and x 2 are the mean occurrences in the two classes
...Although Harter [31]uses function in his wording of this assumption,I think measure would have been more appropriate
...assumption 1 we can calculate the probability of relevance for any document from one of these classes
...that is used to make the decision whether to assign an index term w that occurs k times in a document
...Finally,although tests have shown that this model assigns sensible index terms,it has not been tested from the point of view of its effectiveness in retrieval
...Discrimination and or representation There are two conflicting ways of looking at the problem of characterising documents for retrieval
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The structure of the book The introduction presents some basic background material,demarcates the subject and discusses loosely some of the problems in IR
...The two major chapters are those dealing with automatic classification and evaluation
...Outline Chapter 2:Automatic Text Analysis contains a straightforward discussion of how the text of a document is represented inside a computer
...Chapter 3:Automatic Classification looks at automatic classification methods in general and then takes a deeper look at the use of these methods in information retrieval
...Chapter 4:File Structures here we try and discuss file structures from the point of view of someone primarily interested in information retrieval
...Chapter 5:Search Strategies gives an account of some search strategies when applied to document collections structured in different ways
...Chapter 6:Probabilistic Retrieval describes a formal model for enhancing retrieval effectiveness by using sample information about the |
| 128 |
objected to on the same grounds that one might object to the probability of Newton s Second Law of Motion being the case
...To approach the problem in this way would be useless unless one believed that for many index terms the distribution over the relevant documents is different from that over the non relevant documents
...The elaboration in terms of ranking rather than just discrimination is trivial:the cut off set by the constant in g x is gradually relaxed thereby increasing the number of documents retrieved or assigned to the relevant category
...If one is prepared to let the user set the cut off after retrieval has taken place then the need for a theory about cut off disappears
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The model also assumes that a document can be about a word to some degree
...Harter [31]has identified two assumptions,based upon which the above ideas can be used to provide a method of automatic indexing
...1 The probability that a document will be found relevant to a request for information on a subject is a function of the relative extent to which the topic is treated in the document
...2 The number of tokens in a document is a function of the extent to which the subject referred to by the word is treated in the document
...In these assumptions a topic is identified with the subject of the request and with the subject referred to by the word
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we work with the ratio In the latter case we do not see the retrieval problem as one of discriminating between relevant and non relevant documents,instead we merely wish to compute the P relevance x for each document x and present the user with documents in decreasing order of this probability
...The decision rules derived above are couched in terms of P x wi
...I will now proceed to discuss ways of using this probabilistic model of retrieval and at the same time discuss some of the practical problems that arise
...The curse of dimensionality In deriving the decision rules I assumed that a document is represented by an n dimensional vector where n is the size of the index term vocabulary
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collection
...I am arguing that in using distributional information about index terms to provide,say,index term weighting we are really attacking the old problem of controlling exhaustivity and specificity
...These terms are defined in the introduction on page 10
...If we go back to Luhn s original ideas,we remember that he postulated a varying discrimination power for index terms as a function of the rank order of their frequency of occurrence,the highest discrimination power being associated with the middle frequencies
...Attempts have been made to apply weighting based on the way the index terms are distributed in the entire collection
...The difference between the last mode of weighting and the previous one may be summarised by saying that document frequency weighting places emphasis on content description whereas weighting by specificity attempts to emphasise the ability of terms to discriminate one document from another
...Salton and Yang [24]have recently attempted to combine both methods of weighting by looking at both inter document frequencies |
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Probabilistic indexing In the past few years,a detailed quantitative model for automatic indexing based on some statistical assumptions about the distribution of words in text has been worked out by Bookstein,Swanson,and Harter [29,30,31]...In their model they consider the difference in the distributional behaviour of words as a guide to whether a word should be assigned as an index term
...In general the parameter x will vary from word to word,and for a given word should be proportional to the length of the text
...The Bookstein Swanson Harter model assumes that specialty words are content bearing whereas function words are not
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the system to its user will be the best that is obtainable on the basis of those data
...Of course this principle raises many questions as to the acceptability of the assumptions
...The probability ranking principle assumes that we can calculate P relevance document,not only that,it assumes that we can do it accurately
...So returning now to the immediate problem which is to calculate,or estimate,P relevance document
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