Final answer:
To estimate the statistical model and determine the probability of the sequence 'noun, verb, noun', one must calculate individual and conditional probabilities based on the frequency data from the corpus. The choice between methods iii) and iv) likely depends on the intended application (empirical versus theoretical) and which method better represents actual language usage or dataset characteristics.
Step-by-step explanation:
Probability and Statistical Model
To estimate the statistical model given the frequency data, one needs to calculate the probability of each event, such as P(det), P(noun), P(verb), P(noun | verb) (probability of a noun following a verb), P(noun | det) (probability of a noun following a determiner), P(verb | noun) (probability of a verb following a noun), and P(det | verb) (probability of a determiner following a verb). The corpus comprises 70 parts of speeches. To determine the probability of the sequence 'noun, verb, noun', one would multiply the respective conditional probabilities:
P('noun, verb, noun') = P(noun) × P(verb | noun) × P(noun | verb)
The choice between iii) and iv) will depend on the details provided in those points, which may suggest different methods of approximation or calculation. Generally speaking, the choice of method will be judged based on whether it better approximates the actual use of language as indicated by the frequency data or if it has better mathematical or computational properties.
For example, iii) might suggest an empirical method and iv) a theoretical method, where you would choose the one that gives a better representation of the language used in the corpus or is more suitable for the dataset's properties.