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CertNexus AIP-210 Exam - Topic 5 Question 63 Discussion

When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?
B) Bag of bigrams (2 letter pairs)
A) Bag of words model with TF-IDF
C) Word2Vec algorithm
D) Clustering similar words and representing words by group membership

CertNexus AIP-210 Exam - Topic 5 Question 63 Discussion

Actual exam question for CertNexus's AIP-210 exam
Question #: 63
Topic #: 5
[All AIP-210 Questions]

When working with textual data and trying to classify text into different languages, which approach to representing features makes the most sense?

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Suggested Answer: B

A bag of bigrams (2 letter pairs) is an approach to representing features for textual data that involves counting the frequency of each pair of adjacent letters in a text. For example, the word ''hello'' would be represented as {''he'': 1, ''el'': 1, ''ll'': 1, ''lo'': 1}. A bag of bigrams can capture some information about the spelling and structure of words, which can be useful for identifying the language of a text. For example, some languages have more common bigrams than others, such as ''th'' in English or ''ch'' in German .


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I practiced a question similar to this, and I feel like clustering words might help, but it seems less direct than using TF-IDF.
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Eugene
5 days ago
I think the Word2Vec algorithm could be a strong choice since it captures semantic meaning, but I need to double-check its effectiveness for this specific task.
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Filiberto
10 days ago
I remember studying the Bag of Words model with TF-IDF, but I'm not sure if it's the best for language classification.
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