English to Russian Tech/Engineering Computers (general)

Из книги об использовании ИИ в маркетинге.

Alerting

We scan the Internet for signals and this way, we are informed about eco- nomic changes in companies. Any mention of companies is analysed and the impact they have is evaluated and whether they reveal a positive or a negative development. A rapidly increasing number of complaints to a non- responsive customer service can be an indication of internal problems within the company. News, blogs, social media and the website are a highly topical source of information about the condition and development of a company. Scheduled relocations, structural changes, expansion strategies or profit announcements, for example, are quickly visible and are a sign of a positive or negative development of a company. On the basis of these “early signals”, statements can be made about how probable a company will react to being addressed at the current point in time.

Alerting openly scans the Internet and crawls cyclically websites and social media channels for content snippets containing information about a company. These snippets are the potential alerts that are filtered and aggregated according to significance down the line. In the first step, the probability of a company being mentioned in the given text is determined. Sequence learners, which make a decision based on the lexical similarity and the context of the word as to whether the mention refers to a company or not, are used for this purpose.

In the second step, a deep learner decides whether the validated snippets on a company trigger an alert or whether they are a part of daily background noise. To this end, a model is trained on the basis of historic text data and corresponding share developments, to recognise correlations between snippets and the development on the stock market. __The time lag between alert and real change “lag”__ is automatically learned by the system. Recurrent neural networks, in comparison with other approaches on the basis of a “sliding time window” in combination with a classical regression, do not have the limitation of the finite number of input values.

Тут смысл фразы в том, что система автоматически узнает о том, насколько предупреждение об изменениях отстает по времени от фактического момента, когда эти изменения происходят?

Спасибо.