Large-scale, parallel automatic patent annotation

ABSTRACT

When researching new product ideas or filing new patents, inventors need to retrieve all relevant pre-existing know-how and/or to exploit and enforce patents in their technological domain. However, this process is hindered by lack of richer metadata, which if present, would allow more powerful concept-based search to complement the current keyword-based approach. This paper presents our approach to automatic patent enrichment, tested in large-scale, parallel experiments on USPTO and EPO documents. It starts by defining the metadata annotation task and examines its challenges. The text analysis tools are presented next, including details on automatic annotation of sections, references and measurements. The key challenges encountered were dealing with ambiguities and errors in the data; creation and maintenance of large, domain-independent dictionaries; and building an efficient, robust patent analysis pipeline, capable of dealing with terabytes of data. The accuracy of automatically created metadata is evaluated against a human-annotated gold standard, with results of over 90% on most annotation types.

REFERENCES

• 1: N. Aswani, V. Tablan, K. Bontcheva, and H. Cunningham. Indexing and Querying Linguistic Metadata and Document Content. In Proceedings of Fifth International Conference on Recent Advances in Natural Language Processing (RANLP2005), Borovets, Bulgaria, 2005.
• 2: Daniel M. Bikel , Richard Schwartz , Ralph M. Weischedel, An Algorithm that Learns What‘s in a Name, Machine Learning, v.34 n.1-3, p.211-231, Feb. 1999 [doi>10.1023/A:1007558221122]
• 3: Nancy Chinchor, MUC-4 evaluation metrics, Proceedings of the 4th conference on Message understanding, June 16-18, 1992, McLean, Virginia [doi>10.3115/1072064.1072067]
• 4: H. Cunningham. Information Extraction, Automatic. Encyclopedia of Language and Linguistics, 2nd Edition, pages 665--677, 2005.
• 5: H. Cunningham, D. Maynard, K. Bontcheva, and V. Tablan. GATE: A Framework and Graphical Development Environment for Robust NLP Tools and Applications. In Proceedings of the 40th Anniversary Meeting of the Association for Computational Linguistics (ACL'02), 2002.
• 6: H. Cunningham, D. Maynard, K. Bontcheva, V. Tablan, and C. Ursu. The GATE User Guide. http://gate.ac.uk/, 2002.
• 7: D. Day, P. Robinson, M. Vilain, and A. Yeh. MITRE: Description of the Alembic System Used for MUC-7. In Proceedings of the Seventh Message Understanding Conference (MUC-7), 1998.
• 8: M. Dean, G. Schreiber, S. Bechhofer, F. van Harmelen, J. Hendler, I. Horrocks, D. L. McGuinness, P. F. Patel-Schneider, and L. A. Stein. OWL web ontology language reference. W3C recommendation, W3C, Feb 2004. http://www.w3.org/TR/owl-ref/.
• 9: D. Hull, S. Ait-Mokhatar, M. Chuat, A. Eisele, E. Gaussier, G. Grefenstette, P. Isabelle, C. Samuelsson, and F. Segond. Language technologies and patent search and classification. World Patent Information, 23:265--268, 2001.
• 10: A. Kiryakov. OWLIM: balancing between scalable repository and light-weight reasoner. In Proc. of WWW2006, Edinburgh, Scotland, 2006.
• 11: Y. Li, K. Bontcheva, and H. Cunningham. SVM Based Learning System For Information Extraction. In M. N. J. Winkler and N. Lawerence, editors, Deterministic and Statistical Methods in Machine Learning, LNAI 3635, pages 319--339. Springer Verlag, 2005.
• 12: D. Maynard, K. Bontcheva, and H. Cunningham. Towards a semantic extraction of Named Entities. In Recent Advances in Natural Language Processing, Bulgaria, 2003.
• 13: D. Maynard, V. Tablan, C. Ursu, H. Cunningham, and Y. Wilks. Named Entity Recognition from Diverse Text Types. In Recent Advances in Natural Language Processing 2001 Conference, pages 257--274, Tzigov Chark, Bulgaria, 2001.
• 14: Borislav Popov , Atanas Kiryakov , Damyan Ognyanoff , Dimitar Manov , Angel Kirilov, KIM – a semantic platform for information extraction and retrieval, Natural Language Engineering, v.10 n.3-4, p.375-392, September 2004 [doi>10.1017/S135132490400347X]
• 15: C. J. Van Rijsbergen, Information Retrieval, Butterworth-Heinemann, Newton, MA, 1979
• 16: L. Wanner, R. Baeza-Yates, S. Brugmann, J. Codina, B. Diallo, E. Escorsa, M. Giereth, Y. Kompatsiaris, S. Papadopoulos, E. Pianta, G. Piella, I. Puhlmann, G. Rao, M. Rotard, P. Schoester, L. Serafini, and V. Zervaki. Towards Content-oriented Patent Document Processing. World Patent Information, 30(1):21--33, 2008.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Linguistic processing

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

General Terms:
Experimentation, Measurement, Performance

Keywords:
GATE, information extraction, large-scale, parallel, patent enrichment