Which RNN network structure would best fit a text analysis task, where the occurences of references to a specific kind of event is searched for?

For a text analysis task where the occurrences of references to a specific kind of event are searched for, a Long Short-Term Memory (LSTM) network would be a good fit.

Here's why, step by step:

1. Sequential Data: Text is a form of sequential data. Each word in a sentence depends on the words before it. Recurrent Neural Networks (RNNs) are designed to work with sequential data.

2. Long-term Dependencies: In many text analysis tasks, understanding the context requires maintaining long-term dependencies. For example, a reference to an event might be made at the beginning of a paragraph and not mentioned again until the end. Standard RNNs struggle with maintaining these long-term dependencies due to the vanishing gradient problem.

3. LSTM Networks: LSTM networks are a type of RNN that are designed to remember long-term dependencies. They do this through a series of "gates" that control the flow of information into and out of the memory of the network. This makes them well-suited to tasks that require understanding the context over long stretches of text.

4. Event Detection: In the case of searching for occurrences of a specific event, an LSTM can be trained to recognize the patterns of words and phrases that indicate the event. Once trained, the LSTM can then scan through new text and identify these patterns, effectively detecting the event.

So, in summary, an LSTM network would be a good fit for this task due to its ability to handle sequential data and maintain long-term dependencies, which are both key aspects of text analysis tasks.