Traditional machine learning approaches
While rule-based approaches provide very fine-grained and specific information about language, there are some drawbacks to these approaches, which has motivated the development of alternatives. There are two major drawbacks:
- Developing the rules used in rule-based approaches can be a laborious process. Rule development can either be done by experts directly writing rules based on their knowledge of the language or, more commonly, the rules can be derived from examples of text that have been annotated with a correct analysis. Both of these approaches can be expensive and time-consuming.
- Rules are not likely to be universally applicable to every text that the system encounters. The experts who developed the rules might have overlooked some cases, the annotated data might not have examples of every case, and speakers can make errors such as false starts, which need to be analyzed although they aren’t covered by any rule. Written...
