In the first chapter, three papers on analogy-based methods describe how machine learning techniques are applied to different problems in order to learn regularities from a set of given examples and then to generalise from those for dealing with new data. The general conclusion is that good performance has to be paid for by higher computational costs than other machine- learning algorithms, and that the computer groups linguistic entities according to other features than humans would do. It is probably not very surprising that, for a computer, surface characteristics such as character sequences seem more interesting than the implicit morphological features that are realised with those characters. However, since no claim is made on the cognitive adequacy of these methods, this does not lessen the good classification results achieved. The two main solutions chosen in the articles in the respective section are the use of symbolic pre-processing modules to extract a set of features from the input data, and using recurrent networks.
Feature extraction reduces the data to a small set of parameters relevant to the investigation.
This can, for example, involve pragmatic and syntactic features used in a hybrid abstract generation system. While most neural networks require the whole input pattern to be present, recurrent networks allow a sequential processing mode, where a part of the pattern is presented at each step, and while the pattern is shifted at the next time slice, the previous pattern is fed back into the system through an internal feedback-loop. This way a trace of past steps is still present, and temporal events can be captured. While neural networks are mainly used in pattern recognition or classification tasks, the papers in this section describe attempts to utilise them for linguistic tasks like parsing, or recognition of higher level features such as semantic relationships. The next chapter on corpus-based methods, as with the following one on machine translation, does not quite fit into the editors' grouping.