Natural Language Processing Final
Exam
What Is Natural Language Processing? - answer Natural language processing, in its
simplest form, is the ability for a computer/system to truly understand human language
and process it in the same way that a human does.
What is Entity recognition? - answer It is the process used to classify multiple entities
found in a text in predefined categories, such as a person, objects, location,
organizations, dates, events, etc. Word vector refers to the mapping of the words or
phrases from vocabulary to a vector of real numbers.
The process of automatically assigning parts-of-speech to words in text is called? -
answer part-of-speech tagging, POS tagging, or just tagging.
Tokenization - answer It is the segmentation of a text into basic units—or tokens—such
as words and punctuation. Tokenization based on whitespace is inadequate for many
applications because it bundles punctuation together with words. NLTK provides an off-
the-shelf tokenizer nltk.word_tokenize().
Regular expressions - answer They are a powerful and flexible method of specifying
patterns. Once we have imported the re module, we can use re.findall() to find all
substrings in a string that match a pattern.
Lemmatization - answer It is a process that maps the various forms of a word (such as
appeared, appears) to the canonical or citation form of the word, also known as the
lexeme or lemma (e.g., appear).
Frequency distribution - answerIt is a collection of items along with their frequency
counts (e.g., the words of a text and their frequency of appearance).
RNNs and LSTM networks have applications in diverse fields, including: - answer
Chatbots, Sequential pattern identification, Image/handwriting detection, Video and
audio classification, Sentiment analysis, Time series modeling in finance.
Examples of stopwords: - answerThere, in, such, for, and, it, a, an, the.
A technique used to extract the base forms of the words by removing affixes from them
is called? - answerStemming
, Which type of information is given by the "Bag of Words" algorithm? - answerIt
describes the occurrence of words within a document, or in other words, a vocabulary of
known words.
LSTMs - answerIn LSTMs, the rules that govern the information stored in the state
(memory) are trained neural nets themselves—therein lies the magic. They can be
trained to learn what to remember, while at the same time the rest of the recurrent net
learns to predict the target label!
The Global Vectors for Word Representation, or GloVe, algorithm - answerIt is an
extension to the Word2Vec method for efficiently learning word vectors, developed by
Pennington, et al. at Stanford.
What Are Word Embeddings? - answerA word embedding is a learned representation
for text where words that have the same meaning have a similar representation. It is this
approach to representing words and documents that may be considered one of the key
breakthroughs of deep learning on challenging natural language processing problems.
What is Machine Translation? - answerMachine translation is the task of automatically
converting source text in one language to text in another language. In a machine
translation task, the input already consists of a sequence of symbols in some language,
and the computer program must convert this into a sequence of symbols in another
language.
Sequence-to-sequence - answerSequence-to-sequence networks can be built with a
modular, reusable encoder-decoder architecture. The encoder model generates a
thought vector, a dense, fixed-dimension vector representation of the information in a
variable-length input sequence. A decoder can use thought vectors to predict (generate)
output sequences, including the replies of a chatbot.
WordNet - answerIt is a semantically oriented dictionary of English, consisting of
synonym sets—or synsets—and organized into a network
Number of words - answerIt is the frequency of words in each document of the corpus.
The process of grouping different forms of the same word together is known as: -
answerLemmatization
Encoding techniques used during text processing: - answerBag of Words, TF-IDF,
Word2Vec
What is a Corpus? - answerIt is a large and structured set of texts that can be read by
machines to produce in a natural communicative setting.
True Statement: - answerFor a word having high TFIDF value, the word will have a high
term frequency with less document frequency.
Exam
What Is Natural Language Processing? - answer Natural language processing, in its
simplest form, is the ability for a computer/system to truly understand human language
and process it in the same way that a human does.
What is Entity recognition? - answer It is the process used to classify multiple entities
found in a text in predefined categories, such as a person, objects, location,
organizations, dates, events, etc. Word vector refers to the mapping of the words or
phrases from vocabulary to a vector of real numbers.
The process of automatically assigning parts-of-speech to words in text is called? -
answer part-of-speech tagging, POS tagging, or just tagging.
Tokenization - answer It is the segmentation of a text into basic units—or tokens—such
as words and punctuation. Tokenization based on whitespace is inadequate for many
applications because it bundles punctuation together with words. NLTK provides an off-
the-shelf tokenizer nltk.word_tokenize().
Regular expressions - answer They are a powerful and flexible method of specifying
patterns. Once we have imported the re module, we can use re.findall() to find all
substrings in a string that match a pattern.
Lemmatization - answer It is a process that maps the various forms of a word (such as
appeared, appears) to the canonical or citation form of the word, also known as the
lexeme or lemma (e.g., appear).
Frequency distribution - answerIt is a collection of items along with their frequency
counts (e.g., the words of a text and their frequency of appearance).
RNNs and LSTM networks have applications in diverse fields, including: - answer
Chatbots, Sequential pattern identification, Image/handwriting detection, Video and
audio classification, Sentiment analysis, Time series modeling in finance.
Examples of stopwords: - answerThere, in, such, for, and, it, a, an, the.
A technique used to extract the base forms of the words by removing affixes from them
is called? - answerStemming
, Which type of information is given by the "Bag of Words" algorithm? - answerIt
describes the occurrence of words within a document, or in other words, a vocabulary of
known words.
LSTMs - answerIn LSTMs, the rules that govern the information stored in the state
(memory) are trained neural nets themselves—therein lies the magic. They can be
trained to learn what to remember, while at the same time the rest of the recurrent net
learns to predict the target label!
The Global Vectors for Word Representation, or GloVe, algorithm - answerIt is an
extension to the Word2Vec method for efficiently learning word vectors, developed by
Pennington, et al. at Stanford.
What Are Word Embeddings? - answerA word embedding is a learned representation
for text where words that have the same meaning have a similar representation. It is this
approach to representing words and documents that may be considered one of the key
breakthroughs of deep learning on challenging natural language processing problems.
What is Machine Translation? - answerMachine translation is the task of automatically
converting source text in one language to text in another language. In a machine
translation task, the input already consists of a sequence of symbols in some language,
and the computer program must convert this into a sequence of symbols in another
language.
Sequence-to-sequence - answerSequence-to-sequence networks can be built with a
modular, reusable encoder-decoder architecture. The encoder model generates a
thought vector, a dense, fixed-dimension vector representation of the information in a
variable-length input sequence. A decoder can use thought vectors to predict (generate)
output sequences, including the replies of a chatbot.
WordNet - answerIt is a semantically oriented dictionary of English, consisting of
synonym sets—or synsets—and organized into a network
Number of words - answerIt is the frequency of words in each document of the corpus.
The process of grouping different forms of the same word together is known as: -
answerLemmatization
Encoding techniques used during text processing: - answerBag of Words, TF-IDF,
Word2Vec
What is a Corpus? - answerIt is a large and structured set of texts that can be read by
machines to produce in a natural communicative setting.
True Statement: - answerFor a word having high TFIDF value, the word will have a high
term frequency with less document frequency.
