Natural Language Processing Quiz

Identify an application of Natural Language Processing (NLP).

• Voice assistant

• Image segmentation

• Circuit simulation

• Network routing

Distinguish between natural language and formal language in terms of their typical use.

• Natural language is used for human communication, while formal language is used in mathematics and programming.

• Both natural and formal languages are only used by computers.

• Formal language includes spoken and written communication between humans.

• Natural language is used for creating algorithms and data structures.

A student is building a smart reply feature for an email application. The model needs to predict the next word by analyzing the current and previous word in the sentence. Choose the appropriate N-Gram model to meet this requirement.

• Bigram Model

• Unigram Model

• Decision Tree Classifier

• Support Vector Machine

A language model developed for autocomplete is producing incomplete or blank suggestions because certain n-gram combinations are missing from the training data. Choose the appropriate technique that assigns non-zero probabilities to these unseen word sequences.

• Add-One (Laplace) Smoothing

• Data Normalization

• One-Hot Encoding

• TF-IDF Weighting

A developer is evaluating the poor performance of a transformer model fine-tuned for next sentence prediction. On inspecting the input encoding, they discover that Type IDs for both sentences are incorrectly set to 1: Input: [CLS] The weather is nice today. [SEP] Let’s go for a walk. [SEP] Type IDs: 1 1 1 1 1 1 1 1 1 1 1 1 1 Analyse the impact of this Type ID configuration on model behavior.

• The model may incorrectly treat both sentences as part of the same segment, making it difficult to learn inter-sentence relationships.

• The model will apply a bidirectional attention mask, causing generation errors.

• The positional embeddings will overwrite the segment embeddings during fine-tuning.

• The model will use zero padding instead of segment embeddings, resulting in loss of context.

A developer is preparing input for a transformer model to perform a next sentence prediction task. The input contains two sentences: Input: [CLS] AI is evolving fast [SEP] It impacts many industries [SEP] Calculate the token structure and identify the correct Position IDs and Type IDs for this sequence.

• Position IDs: 0 1 2 3 4 5 6 7 8 9; Type IDs: 0 0 0 0 0 1 1 1 1 1

• Position IDs: 1 2 3 4 5 6 7 8 9 10; Type IDs: all 0

• Position IDs: 0 1 2 3 4 5 6 7 8 9; Type IDs: 1 1 1 1 1 0 0 0 0 0

• Position IDs: 0 1 2 3 4 5 6 7 8 9; Type IDs: 0 0 0 0 0 0 1 1 1 0

A student applies a 1-skip-2-gram model to the sentence: "Transformers understand complex patterns" They generate the following skip-gram pairs: (Transformers, understand), (understand, complex), (complex, patterns) Select the skip-gram output and identify the error in the student's approach.

• The student missed valid skip pairs like (Transformers, complex) and (understand, patterns), which should be included in a 1-skip model.

• The student incorrectly included pairs more than 2 positions apart, violating the n-gram rule.

• The student included punctuation tokens that should have been filtered out.

• The student used a bidirectional model instead of a unidirectional skip-gram model.

Name the category of models that integrate NLP methods with a structured knowledge base to perform reasoning in specialized domains like medicine or law.

• Knowledge-based models

• Statistical models

• N-Gram models

• Recurrent neural networks

Identify the programming language most commonly used for Natural Language Processing (NLP) tasks.

• Python

• Java

• C++

• R

Recall the deep learning libraries commonly used for developing NLP models.

• TensorFlow and PyTorch

• NumPy and SciPy

• Hadoop and Spark

• Eclipse and NetBeans