We propose a NLP methodology for analyzing patent claims that combines symbolic grammar formalisms with dataintensive methods while enhancing analysis robustness. The output of our analyzer is a shallow interlingual representation that captures both the structure and content of a claim text. The methodology can be used in any patent-related application, such as machine translation, improving readability of patent claims, information retrieval, extraction, summarization, generation, etc. The methodology should be universal in the sense that it could be applied to any language, other parts of patent documentation and text as such.
Sheremetyeva REF proposes similar approach to capture both the structure and lexical content of claims from US patent documents.
Natural Language Analysis Of Patent Claims
Workshop On Patent Corpus Processing
Computer Science