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consider some additional examples:
• You can bank on Ravi to finish the project on time. (bank here means rely on)
• I need to go to the bank to deposit money. (here bank means a financial institution)
• She likes to rock in her chair while reading. (move)
• The band rocked the concert last night! (perform)
• You can store files on a cloud. (online storage)
• A cloud covers the sky. (weather phenomenon)
It might only take you a moment to understand these different meanings, but an AI system could struggle with classifying
these elements correctly without a comprehensive understanding of language nuances and context.
Dealing with Classification Problems
Classification can be more challenging for an AI system than simply identifying tokens because so much of classification
depends on the context within the sentence.
Compare:
• I read a book about space. (outer space in universe)
• I need space to work. (personal space)
In both cases, the word “space” is used, but it has different meanings based on the context. An AI system must associate
the word with the correct context: outer space or personal space.
How does an AI system deal with this problem? Here are some ways:
AI systems use machine learning techniques, such as supervised learning.
Dealing with Classification Problems
The AI system learns patterns and links between words, sentences, and their
meanings through a massive dataset of language usage and classification.
The AI system improves classification accuracy over time by modifying
internal settings depending on observed patterns.
To overcome the level of uncertainity and error, well-designed AI systems not only
provide a response but also a confidence value, which indicates the system's degree
of assurance in its classification.
Leveraging Linguistics and Computer Science 373

