Handing of significant deviations from boilerplate text

ABSTRACT

We are attempting to extract information automatically from large legal documents. SPADES is an expert system for screening pension plans submitted to the Internal Revenue Service (IRS), a task which has resisted prior automation attempts. Nearly all pension plans are prepared by plan preparation specialists. Most of a pension plan document consists of boilerplate text, which is reused by the preparer in nearly every plan. We describe techniques used for dealing with plans which essentially follow the boilerplate model for a particular preparer, but contain significant deviations. A significant deviation is found to be any extra paragraph in a new plan which was not predicted by the boilerplate model. Other deviations from the boilerplate model can be handled fairly easily. Treatment of a significant deviation is affected by whether the topic of the extra paragraph can be identified. When it can, the logical impact of the extra text may be deduced by the system, or the system may guide an IRS Agent in analyzing the extra paragraph.

REFERENCES

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.1 Applications and Expert Systems
          Subjects: Law

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL

General Terms:
Design, Experimentation, Human Factors, Performance