This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision | Next revision Both sides next revision | ||
|
teaching:mfe:ia [2018/04/19 17:06] stuetzle |
teaching:mfe:ia [2018/04/19 17:07] stuetzle [Text Categorisation and quality control through automatic language processing] |
||
|---|---|---|---|
| Line 212: | Line 212: | ||
| ===== Text Categorisation and quality control through automatic language processing ===== | ===== Text Categorisation and quality control through automatic language processing ===== | ||
| - | This MS thesis is developed in collaboration with the Energy Efficiency in Industrial Processes (EEIP) company. EEIP is a global industry information network. As part of their activities, they disseminate case studies to various network groups. The goal of the project is to develop an automatic language processing algorithm capable to evaluate the quality (accept / reject) of the proposed case studies and to allocate them to single/multiple categories. Testing and training the algorithm is a key part as it not only requires development and testing of concepts such as how to evaluate quality or definition of requirements for multiple category allocation but the project also has | + | This thesis is developed in collaboration with the Energy Efficiency in Industrial Processes (EEIP) company. EEIP is a global industry information network. As part of their activities, they disseminate case studies to various network groups. The goal of the project is to develop an automatic language processing algorithm capable to evaluate the quality (accept / reject) of the proposed case studies and to allocate them to single/multiple categories. Testing and training the algorithm is a key part as it not only requires development and testing of concepts such as how to evaluate quality or definition of requirements for multiple category allocation but the project also has |
| to be developed in a limited data environment (+/- 1000 case studies as training set). | to be developed in a limited data environment (+/- 1000 case studies as training set). | ||