  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.
