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Large-scale, parallel automatic patent annotation
| Content Provider | ACM Digital Library |
|---|---|
| Author | Tablan, Valentin Agatonovic, Milan Roberts, Ian Li, Yaoyong Bontcheva, Kalina Cunningham, Hamish Aswani, Niraj Heitz, Thomas |
| 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. |
| Starting Page | 1 |
| Ending Page | 8 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781605582566 |
| DOI | 10.1145/1458572.1458574 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2008-10-30 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Patent enrichment Large-scale Gate Information extraction Parallel |
| Content Type | Text |
| Resource Type | Article |
