Word Representations with Character N-grams Quiz

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What is the main limitation of most word embedding techniques mentioned in the text?

They lack parameter sharing

They ignore the internal structure of words (correct)

They are not efficient on large corpora

They do not consider morphologically rich languages

How did Mikolov et al. (2013b) propose to learn continuous representations of words?

By incorporating morphological features (correct)

Using feed-forward neural networks

By sharing parameters among words

By predicting words based on context

What is a distinguishing feature of morphologically rich languages like Turkish and Finnish?

They contain many word forms that occur rarely (correct)

They have very simple grammatical structures

They lack inflectional forms for verbs

They do not benefit from character level information

What did Alexandrescu and Kirchhoff (2006) introduce to improve modeling of rare words?

Factored neural language models Signup and view all the answers

In contrast to some other methods, what does the approach proposed in the text not rely on for deriving word representations?

Morphological decomposition of words Signup and view all the answers

What is a limitation of popular models that learn word representations?

They ignore the morphology of words. Signup and view all the answers

In the proposed approach based on the skipgram model, how are words represented?

As the sum of character n-gram representations. Signup and view all the answers

What advantage does the new method have in training models on large corpora quickly?

It represents words using character n-grams. Signup and view all the answers

How are word representations evaluated in the study mentioned?

By comparing to morphological word representations. Signup and view all the answers

What is the main historical source of continuous representations of words in natural language processing?

Rumelhart et al., 1988 Signup and view all the answers

Word Representations with Character N-grams Quiz

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Questions and Answers

What is the main limitation of most word embedding techniques mentioned in the text?

How did Mikolov et al. (2013b) propose to learn continuous representations of words?

What is a distinguishing feature of morphologically rich languages like Turkish and Finnish?

What did Alexandrescu and Kirchhoff (2006) introduce to improve modeling of rare words?

In contrast to some other methods, what does the approach proposed in the text not rely on for deriving word representations?

What is a limitation of popular models that learn word representations?

In the proposed approach based on the skipgram model, how are words represented?

What advantage does the new method have in training models on large corpora quickly?

How are word representations evaluated in the study mentioned?

What is the main historical source of continuous representations of words in natural language processing?

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