Web Search

1
Web Search Information Retrieval
2
Boolean queries Examples

• Simple queries involving relationships between
  terms and documents
• Documents containing the word Java
• Documents containing the word Java but not the
  word coffee
• Proximity queries
• Documents containing the phrase Java beans or the
  term API
• Documents where Java and island occur in the same
  sentence

3
Document preprocessing

• Tokenization
• Filtering away tags
• Tokens regarded as nonempty sequence of
  characters excluding spaces and punctuations.
• Token represented by a suitable integer, tid,
  typically 32 bits
• Optional stemming/conflation of words
• Result document (did) transformed into a
  sequence of integers (tid, pos)

4
Storing tokens

• Straight-forward implementation using a
  relational database
• Example figure
• Space scales to almost 10 times
• Accesses to table show common pattern
• reduce the storage by mapping tids to a
  lexicographically sorted buffer of (did, pos)
  tuples.
• Indexing transposing document-term matrix

5

Two variants of the inverted index data
structure, usually stored on disk. The
simpler version in the middle does not store term
offset information the version to the right
stores term offsets. The mapping from terms to
documents and positions (written as
document/position) may be implemented using a
B-tree or a hash-table.
6
Stopwords

• Function words and connectives
• Appear in large number of documents and little
  use in pinpointing documents
• Indexing stopwords
• Stopwords not indexed
• For reducing index space and improving
  performance
• Replace stopwords with a placeholder (to remember
  the offset)
• Issues
• Queries containing only stopwords ruled out
• Polysemous words that are stopwords in one sense
  but not in others
• E.g. can as a verb vs. can as a noun

7
Stemming

• Conflating words to help match a query term with
  a morphological variant in the corpus.
• Remove inflections that convey parts of speech,
  tense and number
• E.g. university and universal both stem to
  universe.
• Techniques
• morphological analysis (e.g., Porter's algorithm)
• dictionary lookup (e.g., WordNet).
• Stemming may increase recall but at the price of
  precision
• Abbreviations, polysemy and names coined in the
  technical and commercial sectors
• E.g. Stemming ides to IDE, SOCKS to
  sock, gated to gate, may be bad !

8
Maintaining indices over dynamic collections.
9
Relevance ranking

• Keyword queries
• In natural language
• Not precise, unlike SQL
• Boolean decision for response unacceptable
• Solution
• Rate each document for how likely it is to
  satisfy the user's information need
• Sort in decreasing order of the score
• Present results in a ranked list.
• No algorithmic way of ensuring that the ranking
  strategy always favors the information need
• Query only a part of the user's information need

10
Responding to queries

• Set-valued response
• Response set may be very large
• (E.g., by recent estimates, over 12 million Web
  pages contain the word java.)
• Demanding selective query from user
• Guessing user's information need and ranking
  responses
• Evaluating rankings

11
Evaluating procedure

• Given benchmark
• Corpus of n documents D
• A set of queries Q
• For each query, an exhaustive set of
  relevant documents identified
  manually
• Query submitted system
• Ranked list of documents
  retrieved
• compute a 0/1 relevance list
• iff
• otherwise.

12
Recall and precision

• Recall at rank
• Fraction of all relevant documents included in
  .
• .
• Precision at rank
• Fraction of the top k responses that are actually
  relevant.
• .

13
Other measures

• Average precision
• Sum of precision at each relevant hit position in
  the response list, divided by the total number of
  relevant documents
• .
  
  .
• avg.precision 1 iff engine retrieves all
  relevant documents and ranks them ahead of any
  irrelevant document
• Interpolated precision
• To combine precision values from multiple queries
• Gives precision-vs.-recall curve for the
  benchmark.
• For each query, take the maximum precision
  obtained for the query for any recall greater
  than or equal to
• average them together for all queries
• Others like measures of authority, prestige etc

14
Precision-Recall tradeoff

• Interpolated precision cannot increase with
  recall
• Interpolated precision at recall level 0 may be
  less than 1
• At level k 0
• Precision (by convention) 1, Recall 0
• Inspecting more documents
• Can increase recall
• Precision may decrease
• we will start encountering more and more
  irrelevant documents
• Search engine with a good ranking function will
  generally show a negative relation between recall
  and precision.
• Higher the curve, better the engine

15
Precision and interpolated precision plotted
against recall for the given relevance
vector. Missing are zeroes.
16
The vector space model

• Documents represented as vectors in a
  multi-dimensional Euclidean space
• Each axis a term (token)
• Coordinate of document d in direction of term t
  determined by
• Term frequency TF(d,t)
• number of times term t occurs in document d,
  scaled in a variety of ways to normalize document
  length
• Inverse document frequency IDF(t)
• to scale down the coordinates of terms that occur
  in many documents

17
Term frequency

• .
  .
• Cornell SMART system uses a smoothed version

18
Inverse document frequency

• Given
• D is the document collection and is the set
  of documents containing t
• Formulae
• mostly dampened functions of
• SMART
• .

19
Vector space model

• Coordinate of document d in axis t
• .
• Transformed to in the TFIDF-space
• Query q
• Interpreted as a document
• Transformed to in the same TFIDF-space as d

20
Measures of proximity

• Distance measure
• Magnitude of the vector difference
• .
• Document vectors must be normalized to unit (
  or ) length
• Else shorter documents dominate (since queries
  are short)
• Cosine similarity
• cosine of the angle between and
• Shorter documents are penalized

21
Relevance feedback

• Users learning how to modify queries
• Response list must have least some relevant
  documents
• Relevance feedback
• correcting' the ranks to the user's taste
• automates the query refinement process
• Rocchio's method
• Folding-in user feedback
• To query vector
• Add a weighted sum of vectors for relevant
  documents D
• Subtract a weighted sum of the irrelevant
  documents D-
• .

22
Relevance feedback (contd.)

• Pseudo-relevance feedback
• D and D- generated automatically
• E.g. Cornell SMART system
• top 10 documents reported by the first round of
  query execution are included in D
• typically set to 0 D- not used
• Not a commonly available feature
• Web users want instant gratification
• System complexity
• Executing the second round query slower and
  expensive for major search engines

23
Ranking by odds ratio

• R Boolean random variable which represents the
  relevance of document d w.r.t. query q.
• Ranking documents by their odds ratio for
  relevance
• .
• Approximating probability of d by product of the
  probabilities of individual terms in d
• .
• Approximately

24
Meta-search systems

• Take the search engine to the document
• Forward queries to many geographically
  distributed repositories
• Each has its own search service
• Consolidate their responses.
• Advantages
• Perform non-trivial query rewriting
• Suit a single user query to many search engines
  with different query syntax
• Surprisingly small overlap between crawls
• Consolidating responses
• Function goes beyond just eliminating duplicates
• Search services do not provide standard ranks
  which can be combined meaningfully

25
Similarity search

• Cluster hypothesis
• Documents similar to relevant documents are also
  likely to be relevant
• Handling find similar queries
• Replication or duplication of pages
• Mirroring of sites

26
Document similarity

• Jaccard coefficient of similarity between
  document and
• T(d) set of tokens in document d
• .
• Symmetric, reflexive, not a metric
• Forgives any number of occurrences and any
  permutations of the terms.
• is a metric