LIS618 lecture 2

1
LIS618 lecture 2

• Thomas Krichel
• 2004-02-08

2
Structure

• Theory information retrieval performance
• Practice more advanced dialog.

3
retrieval performance evaluation

• "Recall" and "Precision" are two classic measures
  to measure the performance of information
  retrieval in a single query.
• Both assume that there is an answer set of
  documents that contain the answer to the query.
• Performance is optimal if
• the database returns all the documents in the
  answer set
• the database returns only documents in the 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).
• This can be represented as a curve.

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Example

• Let the answer set be 0,1,2,3,4,5,6,7,8,9 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, precision) is
  (10,100), for the third (20,66), for the
  sixth (30,50), for the tenth (40,40), and for
  the last (50,33).

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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.
• average precision at seen document
• R-precision

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R-precision

• This is a pretty 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 40.
• An average can be calculated for a number of
  queries.

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average precision at seen document

• To find it, sum all the precision level for each
  new relevant document discovered by the user and
  divide by the total number of relevant documents
  for the query.
• In our example, it is (100665040 33)/557.8
• This measure favors retrieval methods that get
  the relevant documents to the top.

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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.
• They focus on one query only. No serious user
  works like this.
• There are some other measures, but that is more
  for an advanced course in IR.

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Looking at database structure

• Up until now, we have looked at commands that
  take a full-text view of the database.
• Such commands can be executed for every database.
• If we want to make more precise queries, we have
  to take account of database structure.

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bluesheet

• Each database name is linked to a blueish pop-up
  window called the blue sheet for the database.
• This is called the bluesheet.
• It contains the details of the database.

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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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basic vs additional index

• the basic index
• has information that is relevant to the
  substantive contents of the data
• usually is indexed by word, i.e. connectors are
  required
• the additional index
• has data that is not relevant to the substantive
  matter
• usually indexed by phrase, i.e. connectors are
  not required

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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.
  when phrases are indexed you don't need proximity
  indicators

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search in basic index

• a field in the basic index is queried through
  term/IN, where term is a search term and IN is a
  field indicator
• Thomas calls this a appending indicator
• several field indicators can be ORed by giving a
  comma separated list
• for example mate/ti,de searches for mate in the
  title or descriptor fields

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limiters and sorting

• Some databases allow to restrict the search using
  limiters. For example
• /ABS require abstract present
• /ENG English language publication
• Some fields are sortable with the sort command,
  i.e. records can be sorted by the values in the
  fields. Example sort s1/all/ti.
• Such features are database specific.

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additional indices

• additional indices lists those terms that can
  lead a query. Often, these are phrase indexed.
• Such fields a queried by prefix INterm where IN
  is the field abbreviator and term is the search
  term
• Thomas calls this a pre-pending indicator

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expanding queries

• names have to be entered as they appear in the
  database.
• The "expand" command can be used to see varieties
  of spelling of a value
• It has to be used in conjunction with a field
  identifier, example
• expand aucruz, b?
• expand aubarrueco?
• to search for misspellings of José Manuel
  Barrueco Cruz

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expanding queries II

• search produces results of the form
• Ref Items Index-term
• Ref is a reference number
• Items is the number of items where the index term
  appears
• Index-term is the index term
• "s Ref" searches for the reference term.

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expand topics

• You can also expand a topic in a database to see
  what index terms are available that start with
  the term. Example b 155 e cold
• If you expand an entry in the expansion list
  again, you can see a list of related terms to
  the term, if such a list is available.

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Example

• How many domain names are currently registered in
  Novosibirsk, Russia?
• Hint use domain name database file 225.
• Note that this database also covers non-current
  domains.

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ranking

• The rank command can be use to show the most
  frequent values of a phrase indexed field in a
  search set.
• Example
• rank au s1
• shows the most frequent authors
• rank de s1
• shows most frequent descriptors
• read the screens following rank command for
  instructions.

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example

• Who wrote on interest rates and growth rates. Use
  EconLit b 139
• s interest(n)rate? and growth(n)rate?
• rank au s1
• You can then set some authors you are interested
  in, 1-5 for example
• exit to leave rank, confirm with yes.
• exs to search for those authors.

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topic searches

• Often we want to know what literature is
  available on a certain topic.
• Many times authors do not use obvious words that
  occur to the searcher.
• Using descriptors can be very helpful.
• Conduct a search
• Look for descriptors
• Use those in other searches

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Initial file selection

• On the main menu, go to the database menu.
• After the principle menu, you get a search box
• There you can enter full-text queries for all the
  databases
• You can then select the database you want
• And get to the begin databases stage.

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database categories

• In order to help people to find databases
  (files), DIALOG have grouped databases by
  categories.
• categories are listed at http//library.dialog.com
  /bluesheets/html/blo.html
• 'b category' will select databases from the
  category category at the start.
• 'sf category' selects files belonging to a
  category category at other times.

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add/repeat

• add number, number
• adds databases by files to the last query
• example "add 297" to see what the bible says
  about it
• repeat
• repeats previous query with database added

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to find publications

• Sometimes, you want to find out if a certain
  publication, say, a serial, is available on
  Dialog
• http//library.dialog.com/bluesheets/
• has a search box specifically for journal
  data.

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http//openlib.org/home/krichel

• Thank you for your attention!