Sticos are developing a bot called @else (http://else.sticos.no) to help Human Resources (HR) departments be more efficient in their work. A lot of their time is consumed by reoccurring questions from their employees and managers. For years HR software have tried to tackle this problem by using a personnel manual. However, the use of it is sparse. The problem lies in accessibility and the fact that is easier to ask the question directly and get a qualified answer to your problem on the fly.
Sticos develops and sells personnel manuals. All rules and answers to employees questions lies in the texts from the personnel manuals. Our customers can also write their own specific rules in the system.
An example correlation is: "An employee can have up to 10 days paid leave in order to take care of his sick kids under the age of 12. However, if the employee has more than 2 children under the age of twelve, he gets 5 days extra. A total of 15 days."
The correlations here are:
• one kid under 12 years : 10 days paid leave
• two kids under 12 years: 10 days paid leave
• three or more kids under 12 years: 15 days paid leave
The project can focus on one or more of the following aspects:
1. Grammatical analytics and statistical classification have different strength and weaknesses. Where IBM and Googles language understanding originates from those different approaches. Today Google, IBM and others uses a hybrid combination of both to understand text better. What is a proper combination of these techniques to understand a Norwegian text?
2. In order to reduce work and secure consistency we want @else to use the personnel manuals when answering questions from employees. What is the best way to achieve this?