Chang-Ning Huang

Title: Innovations of Chang-Ning Huang

Introduction

Chang-Ning Huang is a prominent inventor based in Beijing, China. He has made significant contributions to the field of natural language processing and has been awarded 8 patents for his innovative work. His inventions focus on improving the efficiency and accuracy of language processing systems.

Latest Patents

One of his latest patents is titled "Method and system for retrieving confirming sentences." This invention provides a method, computer-readable medium, and system that retrieves confirming sentences from a sentence database in response to a query. A search engine is utilized to define indexing units based on the query, which includes both lemma from the query and extended indexing units associated with it. The search engine retrieves multiple sentences from the database using these defined indexing units as search parameters. It then determines the similarity between each retrieved sentence and the query, ranking them based on the determined similarities.

Another notable patent is the "Standardized natural language chunking utility." This method supports robust natural language processing by chunking a corpus according to a draft chunking specification. It automatically flags chunk inconsistencies in the corpus for resolution and provides a chunking utility that resolves some of these flagged inconsistencies. This utility ensures a single, consistent global chunking standard, which is particularly beneficial for non-alphabetic languages like Chinese.

Career Highlights

Chang-Ning Huang has worked with Microsoft Technology Licensing, LLC, where he applied his expertise in natural language processing. His work has significantly impacted the development of language processing technologies.

Collaborations

He has collaborated with notable professionals in the field, including Ming Zhou and Jianfeng Gao. Their combined efforts have contributed to advancements in language processing systems.

Conclusion

Chang-Ning Huang's innovative patents and contributions to natural language processing demonstrate his expertise and commitment to advancing technology in this field. His work continues to influence the development of more efficient language processing systems.