A trainable system for the extraction of meaning from text

ABSTRACT

This project is developing a trainable system that can extract meaning from texts in different domains (example: various Internet news-groups). The system does partial parsing based on a large dictionary containing approximately 150,000 words. The system assists the user in extracting a semantic network representation for each member of a set of training articles contained in some large database. Based on the user's training, the system forms statistical tables, a knowledge base, and a set of rules mirroring the user's actions. The system then generalizes these rules. Using statistically based semantic classification, the system applies these rules to new articles from the database for automatically building semantic networks.

REFERENCES

• 1: {1} J. Allen. "Statistical Methods". Natural Language Understanding, pages 195-204, 1995.
• 2: Chidanand Apté , Fred Damerau , Sholom M. Weiss, Automated learning of decision rules for text categorization, ACM Transactions on Information Systems (TOIS), v.12 n.3, p.233-251, July 1994 [doi>10.1145/183422.183423]
• 3: Jerry R. Hobbs , Douglas Appelt , John Bear , David Israel , Megumi Kameyama , Mabry Tyson, FASTUS: a system for extracting information from text, Proceedings of the workshop on Human Language Technology, March 21-24, 1993, Princeton, New Jersey [doi>10.3115/1075671.1075701]
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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.4 Knowledge Representation Formalisms and Methods
          Subjects: Representations (procedural and rule-based); Semantic networks
      I.2.6 Learning
          Subjects: Knowledge acquisition
      I.2.8 Problem Solving, Control Methods, and Search
          Subjects: Dynamic programming

General Terms:
Algorithms, Design, Experimentation, Languages, Measurement, Theory