TASK DEFINITION: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
PROBLEM: Secondly, a well-known problem of uneven yet unknown distribution of word senses is alleviated by modifying a naïve Bayesian classifier. Thanks to this correction, the classifier is no longer biased towards senses that have more training data. Finally, the aggregated feature values corresponding to target word senses are used to build a naïve Bayesian classifier adjusted to a situation of unknown a priori probabilities.

SOLUTION: How do they deal with unknown distribution senses?

PROBLEM: Advanced neural architectures based on character input (CNNs, BPE, etc) are supposed to be able to learn how to handle spelling and morphology variations themselves, even for languages with rich morphology: `just add more layers!'. Contextualised embedding models follow this tradition: as a rule, they are trained on raw text collections, with minimal linguistic pre-processing. Below, we show that this is not entirely true.

SOLUTION: Why is lemmatization not necessary in English?

PROBLEM: A total of 2,50,000 tweets over a period of August 31st, 2015 to August 25th,2016 on Microsoft are extracted from twitter API BIBREF15 . The news on twitter about Microsoft and tweets regarding the product releases were also included. Stock opening and closing prices of Microsoft from August 31st, 2015 to August 25th, 2016 are obtained from Yahoo! Finance BIBREF16 .

SOLUTION:
What dataset is used to train the model?