Improving Automated Patent Claim Parsing: Dataset, System, and Experiments
This addresses parsing challenges for patent professionals and researchers, though it's incremental as it adapts existing methods rather than introducing new paradigms.
The authors tackled poor performance of off-the-shelf NLP software on patent claims by proposing forced part-of-speech tag correction and creating a public corpus via Amazon Mechanical Turk, achieving improved parsing performance and demonstrating utility in automated patent subject classification.
Off-the-shelf natural language processing software performs poorly when parsing patent claims owing to their use of irregular language relative to the corpora built from news articles and the web typically utilized to train this software. Stopping short of the extensive and expensive process of accumulating a large enough dataset to completely retrain parsers for patent claims, a method of adapting existing natural language processing software towards patent claims via forced part of speech tag correction is proposed. An Amazon Mechanical Turk collection campaign organized to generate a public corpus to train such an improved claim parsing system is discussed, identifying lessons learned during the campaign that can be of use in future NLP dataset collection campaigns with AMT. Experiments utilizing this corpus and other patent claim sets measure the parsing performance improvement garnered via the claim parsing system. Finally, the utility of the improved claim parsing system within other patent processing applications is demonstrated via experiments showing improved automated patent subject classification when the new claim parsing system is utilized to generate the features.