Diapositive 1

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INIST Machine-aided indexing Abdelmajid
Khayari Stéphane Schneider INIST/CNRS France NFA
IS Forum New York. April 22, 2005
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• INIST
• Institute for Scientific
• and Technical Information
• A service of the French CNRS.
• Activities collection, analysis and
  dissemination of the results and findings of
  worldwide research.
• Fields covered science, technology, medicine,
  humanities and social sciences.
• Leading scientific and technical document
  supplier in France.
• Producer of multilingual, multidisciplinary
  bibliographic databases, PASCAL, FRANCIS and
  ISD covering the core worldwide scientific
  literature.

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INIST Machine-Aided Indexing. NFAIS forum. New
York. April 22, 2005.
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• INIST (continued)
• Provider of customized services to the
  scientific
• community (portals, current awareness,
  training, etc.)
• Partner in open access initiatives.
• Research partner in the NTIC community

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INIST Machine-Aided Indexing. NFAIS forum. New
York. April 22, 2005.
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• Aims and scopes
• Introduction of a part of automation in the
  indexing
• process
• To which extent can the process be automated ?
• Which approach is suitable ?
• What are the prerequisites ?
• Evaluation of the final result
• Is there support from the indexers ?
• Does it meet the expectations of the
• indexers ?
• Are the results acceptable ?

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INIST Machine-Aided Indexing. NFAIS forum. New
York. April 22, 2005.
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• Current indexing practices
• About 70 internal and external specialized
  indexers.
• Documents in diverse languages (main English,
• French, Spanish, German).
• Semi-manual allocation of descriptors and
• classification codes.
• Use of controlled vocabularies and
  classification
• schemes completed with free key-words.
• Multilingual descriptors.

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INIST Machine-Aided Indexing. NFAIS forum. New
York. April 22, 2005.
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• Workstation
• Development of a home-made platform accessed by
• INIST indexers since year 2000 via our intranet.
• Set up of indexing programs and fine tuning.
• Collaborative work on terminology resources.
• About 2000 input records processed each night.
• Use of fully automated indexing for periodicals
  that
• are not manually analyzed (May, 2004).

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INIST Machine-Aided Indexing. NFAIS forum. New
York. April 22, 2005.
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• Two indexing programs
• Lexical method. Uses equivalence rules gathered
  in
• subject terminological resources to assign
• descriptors and classification codes to
  documents.
• Statistical approach (lexical collocation). A
  two-stage
• process
• Training stage a corpus of human-indexed
• citations is processed to create association
• dictionaries.
• Indexing stage using the association
  dictionaries,
• controlled vocabulary descriptors and
• classification codes are assigned to the
• incoming documents.

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INIST Machine-Aided Indexing. NFAIS forum. New
York. April 22, 2005.
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• Lexical method
• Text processing of bibliographical records
  (titles abstracts author keywords)
• Parsing text to phrases and phrases to words.
• Lemmatization.
• Matching with subject terminology resources
• Searching for terms that correspond to
  descriptors and classification codes.
• Searching intervals for compound terms (2 to 6
  words).
• Ordering of candidate key-words and
  classification codes semantic categories are
  used to construct the indexing grid.

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INIST Machine-Aided Indexing. NFAIS forum. New
York. April 22, 2005.
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• Lexical method (continued)
• Generation of additional descriptors and codes
  each keyword or code may trigger another one
  using association rules in a cascade-like manner
  
• Rat -gt Animal / Acropulpitis -gt Finger.
• Pointing task -gt Manual task -gt Motor control
• Pointing task Vision -gt Visuomotor integration
• Ranking of keywords and codes.
• Filtering of the assigned elements number of
  occurrences for each category is used as a
  filter to set the desired number of candidate
  descriptors.

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INIST Machine-Aided Indexing. NFAIS forum. New
York. April 22, 2005.
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• Lexical method (continued)
• Check-up by human indexers candidate
  descriptors and codes are validated and
  completed.
• Continuous feedback on terminological resources
  is operated in parallel by introducing new
  equivalence rules, new data (synonyms) in
  existing rules or deleting noise-producing
  rules.
• Feedback on the indexing program (changing
  parameters and terminology resources
  combination).

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INIST Machine-Aided Indexing. NFAIS forum. New
York. April 22, 2005.
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• Initial version
• Deployment of a basic version using the 2
  methods.
• A semi-automated process is used to construct
  the subject terminology resources.
• Bibliographic databases are used to extract
  corpora dealing with a specific subject (i.e.
  pain)
• Corpora are processed to extract a ranked list
  of descriptors which is run against the
  controlled vacabulary to extract synonyms,
  translations, semantic categories, etc.
• This core thematic dictionary is enriched with
  new concepts and new data.
• During an iterative indexing and re-indexing
  process, the performance of the dictionary is
  improved.

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INIST Machine-Aided Indexing. NFAIS forum. New
York. April 22, 2005.
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• Initial version (continued)
• Sharing of thematic resources - Someone has the
  dictionary of diseases or of geographical names
  I need.
• Access to full-text articles (OCRized and
  directly from publishers).
• Direct feedback to administrators/developers
  incorporated.
• Evaluation of performance on each citation or
  collection of citations. Final indexing is
  compared to the initial one as proposed by the
  program.

INIST Machine-Aided Indexing. NFAIS forum. New
York. April 22, 2005.
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• Evaluation
• Indexers support for MAI was not easy to obtain
  
• Important psychological reluctance at the
  beginning
• (the machine will never be able to perform a
  highly
• intellectual task abstraction).
• The crucial need to formalize the specialist
• knowledge is becoming well understood.
• Many concerns about fully automated indexing
  since
• the standard scale is the human produced
  indexing
• (i.e. a candidate descriptor which is not
  inaccurate
• per se will be considered as wrong since the
  human
• indexer did not include it in the final record.)

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INIST Machine-Aided Indexing. NFAIS forum. New
York. April 22, 2005.
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• Evaluation (continued)
• The lexical method is predominantly used by
  indexers.
• The statistical one is used mainly for
  determination
• of classification codes.
• Stats are obtained by comparing machine indexing
  
• with the final record after human revision
• Performance is proportional to the degree of
• improvement of terminology resources (in pilot
• subject fields up to 80 accurate candidate
• descriptors can be obtained).
• Unsuccessful machine-indexing triggers feedback
  on
• computer programs and on terminology
• resources

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INIST Machine-Aided Indexing. NFAIS forum. New
York. April 22, 2005.
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• Evaluation (continued)
• During the deployment phase, time-saving is not
• always achieved because feedback on
• terminology resources is time-consuming.
• Nevertheless, benefits are real in terms of
• Indexing consistency (less intra- and
  inter-individual
• variations)
• Indexors expertise and knowledge acquired
  during
• the abstraction-indexing processes are integrated
  
• into an organization resource (knowledge
• capitalization and sharing).

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INIST Machine-Aided Indexing. NFAIS forum. New
York. April 22, 2005.
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• Future trends
• Indexing programs improvement
• Improvement of textual pattern extraction
  (genes, etc.)
• Introduction of advanced natural language
  processing
• Extraction of concepts.
• Extraction of relationships between concepts.
• Improvement of citation pre-classification in
  order to
• be able to assign the right combination of
• subject resources.
• Constitution of a unified terminology database

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INIST Machine-Aided Indexing. NFAIS forum. New
York. April 22, 2005.