Beijing, China

Shouxun Lin

USPTO Granted Patents = 2 

Average Co-Inventor Count = 7.0

ph-index = 1

Forward Citations = 5(Granted Patents)


Company Filing History:


Years Active: 2013-2019

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2 patents (USPTO):Explore Patents

Title: Shouxun Lin: Innovator in Statistical Machine Translation

Introduction

Shouxun Lin is a prominent inventor based in Beijing, China. He has made significant contributions to the field of statistical machine translation, holding two patents that showcase his innovative approach to language processing.

Latest Patents

One of his latest patents is titled "Statistical machine translation method using dependency forest." This invention relates to the use of multiple dependency trees in tree-based statistical machine translation. It proposes a dependency forest to effectively process these trees, improving translation capabilities by generating a translation rule and a dependency language model. This model is applied when converting source language text to target language text, enhancing the overall translation quality.

Another notable patent is the "Apparatus and method for decoding using joint tokenization and translation." This disclosure presents a joint decoding apparatus and method that integrates tokenization and translation processes. By generating all available candidate tokens and reducing translation errors, this method aims to achieve optimal translation results through simultaneous decoding of input character sequences.

Career Highlights

Shouxun Lin has worked with notable companies such as Sk Planet Co., Ltd. and Eleven Street Co., Ltd. His experience in these organizations has contributed to his expertise in machine translation technologies.

Collaborations

Some of his coworkers include Young Sook Hwang and Sang-Bum Kim, who have collaborated with him on various projects in the field of machine translation.

Conclusion

Shouxun Lin's innovative patents and career achievements highlight his significant role in advancing statistical machine translation. His work continues to influence the development of language processing technologies.

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