APPLICATIONS OF DATA MINING IN INFORMATION RETRIEVAL

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APPLICATIONS OF DATA MINING IN INFORMATION
RETRIEVAL
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Nearest Neighbor Classifiers

• Basic intuition similar documents should have
  the same class label
• We can use the vector space model and the cosine
  similarity
• Training is simple
• Remember the class value of the initial documents
• Index them using an inverted index structure
• Testing is also simple
• Use each test document dt as a query
• Fetch k training documents most similar to it
• Use majority voting to determine the class of dt

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Nearest Neighbor Classifiers

• Instead of pure counts of classes, we can use the
  weights wrt the similarity
• If training document d has label cd, then cd
  accumulates a score of s(dq, d)
• The class with maximum score is selected
• Per-class offsets could be used and tuned later
  on

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Nearest Neighbor Classifiers

• Choosing the value k
• Try various values of k and use a portion of the
  documents for validation.
• Cluster the documents and choose a value of k
  proportional to the size of small clusters

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Nearest Neighbor Classifiers

• kNN is a lazy strategy compared to decision trees
• Advantages
• No-training needed to construct a model
• When properly tuned for k, and bc, they are
  comparable in accuracy to other classifiers.
• Disadvantages
• May involve many inverted index lookups, scoring,
  sorting and picking the best k results takes time
  (since k is small compared to the retrieved
  documents, such types of queries are called
  iceberg queries)

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Nearest Neighbor Classifiers

• For better performance, some effort is spent
  during training
• Documents are clustered, and only a few
  statistical parameters are stored per-cluster
• A test document is first compared with the
  cluster representatives, then with the individual
  documents from appropriate clusters

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Measures of accuracy

• We may have the following
• Each document is associated with exactly one
  class
• Each document is associated with a subset of
  classes
• The ideas from precision and recall can be used
  to measure the accuracy of the classifier
• Calculate the average precision and recall for
  all the classes

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Hypertext Classification

• An HTML document can be thought of a hierarchy of
  regions represented by a tree-structured Document
  Object Model (DOM). www.w3.org/DOM
• DOM tree consists of
• Internal nodes
• Leaf nodes segments of text
• Hyperlinks to other nodes

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Hypertext Classification

• An example DOM in XML format
• Is is important to distinguish the two
  occurrences of the term surfing which can be
  achieved by prefixing the term by the sequence of
  tags in the DOM tree.
• resume.publication.title.surfing
• resume.hobbies.item.surfing

ltresumegt ltpublicationgt lttitlegt Statistical
models for web-surfing lt/titlegt lt/publicationgt lt
hobbiesgt ltitemgt Wind-surfing lt/itemgt lt/hobbiesgt
lt/resumegt
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Hypertext Classification

• Use relations to give meaning to textual features
  such as
• contains-text(domNode, term)
• part-of(domNode1, domNode2)
• tagged(domNode, tagName)
• links-to(srcDomNode, dstDomNode)
• Contains-anchor-text(srcDomNode, dstDomNode,
  term)
• Classified(domNode, label)
• Discover rules from collection of relations such
  as
• Classifed(A, facultyPage) - contains-text(A,
  professor), contains-text(A, phd), links-to(B,A),
  contains0text(B,faculty)
• Where - means if, and comma stands for
  conjunction.

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Hypertext Classification

• Rule Induction with two-class setting
• FOIL (First order Inductive Learner by Quinlan,
  1993)
• a greedy algorithm that learns rules to
  distinguish positive examples from negative ones
• Repeatedly searches for the current best rule and
  removes all the positive examples covered by the
  rule until all the positive examples in the data
  set are covered
• Tries to maximize the gain of adding literal p to
  rule r
• P is the set of positive and N is the set of
  negative examples
• When p is added to r, then there are P positive
  and N negative examples satisfying the new rule

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Hypertext Classification
Let R be the set of rules learned, initially
empty While D ! EmptySet do // learn a new
rule Let r be true and be the new rule
while some d in D- satisfies r // Add a
new possibly negated literal to r to specialize
it Add best possible literal p as a
conjunction to r endwhile R lt- R U r
Remove from D all instances for which r
evaluates to true Endwhile Return R
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Hypertext Classification

• Let R be the set of rules learned, initially
  empty
• While D ! EmptySet do
• // learn a new rule
• Let r be true and be the new rule
• while some d in D- satisfies r
• // Add a new possibly negated literal to
  r to specialize it
• Add best possible literal p as a
  conjunction to r
• endwhile
• R lt- R U r
• Remove from D all instances for which r
  evaluates to true
• Endwhile
• Return R
• Types of literals explored
• XiXj, Xic, XigtXj etc where Xi, Xj are variables
  and c is a constant
• Q(X1,X2,..Xk) where Q is a relation and Xi are
  variables
• Not(L), where L is a literal of the above forms

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Hypertext Classification

• With relational learning, we can learn class
  labels for individual pages, and relationships
  between them
• Member(homePage, department)
• Teaches(homePage, coursePage)
• Advises(homePage, homePage)
• We can also incorporate other classifiers such as
  naïve bayesian for rule learning

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RETRIEVAL UTILITIES
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Retrieval Utilities

• Relevance feedback
• Clustering
• Passage-based Retrieval
• Parsing
• N-grams
• Thesauri
• Semantic Networks
• Regression Analysis

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Relevance Feedback

• Do the retrieval in multiple steps
• User refines the query at each step wrt the
  results of the previous queries
• User tells the IR system which documents are
  relevant
• New terms are added to the query based on the
  feedback
• Term weights may be updated based on the user
  feedback

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Relevance Feedback

• Bypass the user for relevance feedback by
• Assuming the top-k results in the ranked list are
  relevant
• Modify the original query as done before

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Relevance Feedback

• Example find information surrounding the
  various conspiracy theories about the
  assassination of John F. Kennedy (Example from
  your text book)
• IF the highly ranked document contains the term
  Oswald then this needs to be added to the
  initial query
• If the term assassination appears in the top
  ranked document, then its weight should be
  increased.

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Relevance Feedback in Vector Space Model

• Q is the original query
• R is the set of relevant and S is the set of
  irrelevant documents selected by the user
• R n1, S n2

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Relevance Feedback in Vector Space Model

• Q is the original query
• R is the set of relevant and S is the set of
  irrelevant documents selected by the user
• R n1, S n2
• In general

The weights are referred to as Rocchio weights
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Relevance Feedback in Vector Space Model

• What if the original query retrieves only
  non-relevant documents (determined by the user)?
• Then increase the weight of the most frequently
  occurring term in the document collection.

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Relevance Feedback in Vector Space Model

• Result set clustering can be used as a utility
  for relevance feedback.
• Hierarchical clustering can be used for that
  purpose where the distance is defined by the
  cosine similarity