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Semantic Language Prediction Models

Technology Benefits
- Achieve significantly better translation quality measured by the BLEU score- Improves readability of the translation
Technology Application
- Machine translation- Speech recognition programs/applications - Information retrieval
Detailed Technology Description
None
Supplementary Information
Inventor: Wang, Shaojun | Tan, Ming
Priority Number: US20130325436A1
IPC Current: G06F001727 | G10L001518
US Class: 704009 | 704E15018
Assignee Applicant: Wright State University,Dayton
Title: Large Scale Distributed Syntactic, Semantic and Lexical Language Models
Usefulness: Large Scale Distributed Syntactic, Semantic and Lexical Language Models
Novelty: Composite language models for machine translation, speech recognition and information retrieval, has composite word predictor to predict next word, and approximate expectation-maximization and follow-up expectation-maximization algorithm
Industry
ICT/Telecom
Sub Category
Software/Application
*Abstract
The inventors have created an algorithm which builds powerful language models to capture and predict natural language. The invention has a variety of real world applications involving machine translation, speech recognition software and information/ database retrieval systems. The improvements provided by the invention are done without sacrificing strong assumptions or intractable model assumptions. A working language model is available via the Ohio Supercomputer Center.
*Principal Investigator

Name: Ming Tan

Department:


Name: Shaojun Wang

Department:

Country/Region
USA

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