LIS618 lecture 2

1
LIS618 lecture 2

• Thomas Krichel
• 2002-09-22

2
Structure of talk

• General round trip on theoretical matters, part
• Information retrieval models
• vector model
• Probabilistic model
• Retrieval performance evaluation
• Query languages
• Introduction to online searching
• Introduction to DIALOG
• Overview
• bluesheets

3
vector model

• associates weights with each index term appearing
  in the query and in each database document.
• relevance can be calculated as the cosine between
  the two vector, i.e. their cross product divided
  be the square roots of the squares of each
  vector. This measure varies between 0 and 1.

4
tf/idf weighting technique

• Let n_i be the number of documents where the term
  d_i appears. Let F_i_j be the number of times
  term i appears in the document j.
• The normalized frequency is f_i_j, given by
• f_i_j(F_i_j/max_l(F_l_j)
• that is the raw frequency divided by the
  maximum raw frequency achieved by any term in
  the document j.

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tf/idf weighting technique

• Let N be the number of documents.
• Then the most frequently used weighting scheme is
• w_i_jf_i_j log(N/n_i)
• There are other methods, but these are variations
  on this one.

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advantages of vector model

• term weighting improves performance
• sorting is possible
• easy to compute, therefore fast
• results are difficult to improve without
• query expansion
• user feedback circle

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probabilistic model (outline only)

• starts with the assumption that there is a subset
  of documents that form the ideal answer set
• query process specifies properties of the answer
  set
• query terms can be used to form a probability
  that a document is part of the answer
• then we start an iterative process with the user
  to gain more characteristics about the answer set

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recursive method

• If we assume that the probability that the
  documents that are relevant among a set of
  initially retrieved documents is proportional to
  the appearance of index terms that are part of
  the query, the probability can further be
  refined.

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probabilistic model

• For any user requirement, we assume that there is
  an answer set and that the probability that the
  user finds a document interesting only depends on
  the document and the query
• Then the similarity of the document to the query
  can be expressed as
• s(probability that the document is part of the
  answer set / probability that it is not part of
  the answer set).
• There are ways to calculate this (with some more
  assumptions).

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retrieval performance evaluation

• There are two classic measures. Both assume that
  there is an answer set.
• Recall is the fraction of the relevant documents
  that the query result has captured.
• Precision is the fraction of the retrieved
  documents that is relevant.

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recall and precision curves

• assume that all the retrieved documents arrive at
  once and are being examined.
• during that process, the user discover more and
  more relevant documents. Recall increases.
• during the same process, at least eventually,
  there will be less and less useful document.
  Precision declines (usually).

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Example

• let the answer set be 4,7,5,3,6,1,0,8,9 and
  non-relevant documents represented by letters.
• A query reveals the following result
• 7,a,3,b,c,9,n,j,l,5,r,o,s,e,4.
• for the first document, (recall, precison) is
  (10,100), for the third, (20,60), then follow
  (30,50), (40,40), (50,27)

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recall/precision curves

• Such curves can be formed for each query.
• An average curve, for each recall level, can be
  calculated for several queries.
• Recall and precision levels can also be used to
  calculate two single-valued summaries.

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average precision at seen document

• sum all the precision level for each new relevant
  document and divide by the total number of
  relevant documents is the query.
• In our example, it is 0.57
• This measure favors retrieval methods that get
  the relevant documents to the top.

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R-precision

• a more ad-hoc measure.
• Let R be the size of the answer set.
• Take the first R results of the query.
• Find the number of relevant documents
• Divide by R.
• In our example, the R-precision is .4.
• An average can be calculated for a number of
  queries.

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critique of recall precision

• recall has to be estimated by an expert
• recall is very difficult to estimate in a large
  collection
• measures most appropriate to a situation where
  queries are run in batch mode, they are difficult
  to reconcile with the idea of interactive use.
• there are some other measures.

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simple queries

• single-word queries
• one word only
• Hopefully some word combinations are understood
  as one word, e.g. on-line
• Context queries
• phrase queries (be aware of stop words)
• proximity queries, generalize phrase queries
• Boolean queries

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simple pattern queries

• prefix queries (e.g. "anal" for analogy)
• suffix queries (e.g. "oral" for choral)
• substring (e.g. "al" for talk)
• ranges (e.g. form "held" to "hero")
• within a distance, usually Levenshtein distance
  (i.e. the minimum number of insertions,
  deletions, and replacements) of query term

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regular expressions

• come from UNIX computing
• build form strings where certain characters are
  metacharacters.
• example "pro(blem)(tein)s?" matches problem,
  problem, protein and proteins.
• example New .y matches "New Jersey" and "New
  York City", and "New Delhy".
• great variety of dialects, usually very powerful.
  
• Extremely important in digital libraries.

20
structured queries

• make use of document structures
• simplest example is when the documents are
  database records, we can search for terms is a
  certain field only.
• if there is sufficient structure to field
  contents, the field can be interpreted as meaning
  something different than the word it contains.
  example dates

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query protocols

• There are some standard languages
• Z39.50 queries
• CCL, "common command language" is a development
  of Z39.50
• CD-RDx "compact disk read only data exchange" is
  supported by US government agencies such as CIA
  and NASA
• SFQL "structure full text query language" built
  on SQL

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document preprocessing

• operations done on the documents before indexing
• lexical analysis
• elimination of stop words
• stemming of words
• selection of index term
• construction of term categorization structures
• receives a decline in attention

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lexical analysis

• divides a stream of characters into a stream of
  words
• seems easy enough but.
• should we keep numbers?
• hyphens. compare "state-of-the-art" with "b-52"
• removal of punctuation, but "333B.C."
• casing. compare "bank" and "Bank"

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elimination of stop words

• some words carry no meaning and should be
  eliminated
• in fact any word that appears in 80 of all
  documents is pretty much useless, but
• consider a searcher for "to be or not to be".

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stemming

• in general, users search for the occurrence of a
  term irrespective of grammar
• plural, gerund forms, past tense can be subject
  to stemming
• important algorithm by Porter
• evidence on effect on retrieval is mixed

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index term selection

• some engines try to capture nouns only
• some nouns that appear heavily together can be
  considered to be one index term, such as
  "computer science"
• Most web engines, however, index all words, why?

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thesauri

• a list of words and for each word, a list of
  related words
• synonyms
• broader terms
• narrower terms
• used
• to provide a consistent vocabulary for indexing
  and searching
• to assist users with locating terms for query
  formulation
• allow users to broaden or narrow query

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use of thesauri

• most users want to get a quick response
• often the selection of terms is erroneous
• frequently the relationship between terms in the
  query is badly served by the relationships in the
  query. Thus thesaurus expansion of an initial
  query (if performed automatically) can lead to
  bad results.

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Online database searching
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before a search I

• what is purpose
• brief overview
• comprehensive search
• What perspective on the topic
• scholarly
• technical
• business
• popular

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before search II

• What type of information
• Fulltext
• Bibliographic
• Directory
• Numeric
• Are there any known sources?
• Authors
• Journals
• Papers
• Conferences

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before search III

• What are the language restrictions?
• What, if any, are the cost restrictions?
• How current need the data to be?
• How much of each record is required?

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DIALOG
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Literature

• http//training.dialog.com/sem_info/courses/pdf_se
  m/dlg1.pdf
• http//training.dialog.com/sem_info/courses/pdf_se
  m/dlg2.pdf
• http//training.dialog.com/sem_info/courses/pdf_se
  m/dlg3.pdf
• http//training.dialog.com/sem_info/courses/pdf_se
  m/dlg4.pdf

35
databank

• over 500 different databases
• references and abstracts for published
  literature,
• business information and financial data
• complete text of articles and news stories
• statistical tables
• Directories
• Two interfaces to all this stuff.
• Guided search (for neophytes)
• Command search (for Masters) ? for us!!

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Four steps in a search

• Use the Databases Selection Tool to select
  databases
• Identify search terms
• Use Dialog basic commands to conduct a search
• View records online

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B E S T strategy I

• begin
• b 630,636
• b papersmj, not 630
• expand
• e colong island university
• e aukrichel, t

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B E S T II

• select
• s (mate?(N)drink?) or (lex(N)para?)
• s s1 and s2
• type
• type s1/3/1,6

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Command search

• The first thing to be done is to select a
  database.
• 8 categories
• Government Medicine Pharmaceuticals
• News Science Technology
• Business Intellectual property
• Reference Social Sciences and Humanities
• there we go to command search

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databases menus

• databases are ordered in hierarchical fashion
• at each level a Boolean search can be executed
• on all of Dialog
• on the databases in the current hierarchical
  level

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searching

• result may be a just a blank screen
• otherwise, a table with the file number, the
  database name and the number of hits appears
• wait until the display is complete.
• sorting of database is possible by the number of
  hits for the current query

42
blue sheet

• each database name is linked to a blueish pop-up
  window called the blue sheet for the database
• Contents of bluesheet is covered later
• at this stage we choose a database and hit
  "begin". We see that there is a command selected
  "be numbers" where numbers are the ones for the
  databases selected, separated by comma.

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finding a database

• file 411 contains the database of databases
• 'sf category' selects files belonging to a
  category category
• categories are listed at http//library.dialog.com
  /bluesheets
• 'rank files' will rank the results
• 'b ref,ref' will select databases using rank
  references.

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closer look at the bluesheet

• file description
• subject coverage (free vocabulary)
• format options, lists all formats
• by number (internal)
• by dialog web format (external, i.e.
  cross-database)
• search options
• basic index, i.e. subject contents
• additional index, i.e. non-subject

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search options basic index

• select without qualifiers searches in all fields
  in the basic index
• bluesheet lists field indicators available for a
  database
• also note if field is indexed by word or phrase.
  proximity searching only works with word indices.

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other search options

• additional indices lists those terms that can
  lead a query. Often, these are phrase indexed.
• special features will list other features that
  the database has and that can be used in queries

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http//openlib.org/home/krichel

• Thank you for your attention!