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MLP Quiz
MLP Quiz
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1
A probabilistic context free grammar...
...considers lexical information
...assumes dependence between constituents
...works well for free word order languages
...has probabilities added to its rules
2
What is a common ambiguity in syntax?
Coordination
Subject verb agreement
Topicalisation
PP-attachment
3
Data in machine learning settings are usually split into:
Training, Validation, Test
Unsupervised Training, Supervised Validation, Test
Training, Development, Test
Easy Training, Difficult Training, Test
4
A dependency parser provides the following output:
Relations between heads and its dependents
POS tags
f-structure
Constituency structure
5
The Porter Stemmer...
...is a machine learning algorithm
...gives a detailed morphological analysis
...contains a lexicon
...removes affixes (mainly suffixes) based on rules
6
What are properties of tf-idf (term frequency - inverse document frequency)?
Dense vectors that are based on occurrence statistics
Sparse vectors that are trained
Sparse vectors that are based on occurrence statistics
Dense vectors that are trained
7
Which pair is true?
f-structure - dependency
n-grams - sequence of n words (or items in general)
Finite State Morphology Analyser - Detailed morphological analysis
Context free grammar - hierarchical structure
8
What is a language model?
The probabilities in a language model are based on a corpus
A model to compute the probability of a sentence or sequence of words
Model to compute the probability of an upcoming word
A very beautiful language
9
Two words are similar...
...when they're located close to each other in the embedding space
...when their cosine similarity is close to 1
...when they appear in different environments
...when their cosine similarity is close to 0
10
What does the following First Order Logic expression mean?
The driving test did not fail Daniel.
Daniel failed his driving test.
Daniel did not fail his driving test.
Daniel shouldn't be allowed to drive.
11
The following Finite State Network can parse...
apple
ales
aples
apppppppple
12
Which of the following strings are described by this regular expression?
WoooowTacosss
WoTacos
WWooooooTaco
WoooooooowTaco
13
Common issues in tokenizers are:
Multi word expressions
Punctuation
Languages with white spaces
Special characters like quotation marks
14
What are approaches to do language generation?
Natural language generation and generation as an inverse of parsing
Hidden Markov Generation and text prediction with large language models
Natural language generation and text prediction with large language models
Generation as an inverse of parsing and Hidden Markov Generation
15
What does this denote in regular expressions?
The letter "w"
Any digit
Alphanumeric characters and the underscore
Not the letter "w"
