Final answer:
PMI and BEAGLE have different sensitivities to window size in distributional models. PMI is based on statistical association within a specific window, while BEAGLE learns distributed word representations. The nature of the data used by the models also plays a role in their sensitivity.
Step-by-step explanation:
PMI (Pointwise Mutual Information) and BEAGLE are both distributional models used in natural language processing. They are dependent on window size, but they have different sensitivities to this parameter for several reasons.
One reason is that PMI measures the statistical association between two words based on their co-occurrence within a specific window. With a larger window size, PMI may capture more contextual information and potentially more meaningful associations between words. On the other hand, BEAGLE is a neural network-based model that learns distributed word representations. It may be less sensitive to window size because it can capture the semantic relationships between words in a continuous vector space, which is not constrained by a fixed window size.
Another reason for the different sensitivities is the nature of the data used by the models. PMI typically relies on a large text corpus, where the distribution of words and their co-occurrence patterns can vary significantly depending on the domain and the type of text data used. In contrast, BEAGLE can be trained on more specific datasets, such as domain-specific documents or even social media posts, where the context and the way words are used may differ from traditional text sources. This flexibility in training data may make BEAGLE less reliant on window size for capturing meaningful semantic relationships.