Natural Language Processing Questions
latent semantic analysis (LSA) - answer One of the foundational methods in topic
modeling. The core idea is to take a matrix of what we have — documents and terms —
and decompose it into a separate document-topic matrix and a topic-term matrix.
(Part of speech) POS tagging - answer Labeling words with their appropriate POS (i.e
nouns, verbs, etc...)
Chunking - answer Chunking is a process of extracting phrases from unstructured text.
Instead of just simple tokens which may not represent the actual meaning of the text, its
advisable to use phrases such as "South Africa" as a single word instead of 'South' and
'Africa' separate words.
Chunking works on top of POS tagging, it uses pos-tags as input and provides chunks
as output. Similar to POS tags, there are a standard set of Chunk tags like Noun
Phrase(NP), Verb Phrase (VP), etc. Chunking is very important when you want to
extract information from text such as Locations, Person Names etc. In NLP called
Named Entity Recognition.
Natural Language Toolkit (NLTK) - answer A suite of libraries and programs for
symbolic and statistical NLP for English written in the Python programming language.
word sense disambiguation - answer An open problem in NLP regarding the ambiguity
which arises due to different meaning of words in different context.
parsing - answer In this sense, syntactic analysis or parsing may be defined as the
process of analyzing the strings of symbols in natural language conforming to the rules
of formal grammar.
crash blossom - answerA newspaper headline in which some of the words have more
than one possible meaning so that it can be understood in more than one way, often
humorously. This type of ambiguity makes NLP difficult.
word sense ambiguity - answerWord sense ambiguity means a single word or. sentence
is interpreted differently by different users. The main reason for this is that a single.
word has more than one meaning (exact meaning. depends on the context).
regular expression - answerA formal language for specifying text strings. For example
lets say we are looking for woodchucks in a text document. Woodchucks can be
expressed in a few different ways, for example: Woodchuck, Woodchucks, woodchuck,
woodchucks.
latent semantic analysis (LSA) - answer One of the foundational methods in topic
modeling. The core idea is to take a matrix of what we have — documents and terms —
and decompose it into a separate document-topic matrix and a topic-term matrix.
(Part of speech) POS tagging - answer Labeling words with their appropriate POS (i.e
nouns, verbs, etc...)
Chunking - answer Chunking is a process of extracting phrases from unstructured text.
Instead of just simple tokens which may not represent the actual meaning of the text, its
advisable to use phrases such as "South Africa" as a single word instead of 'South' and
'Africa' separate words.
Chunking works on top of POS tagging, it uses pos-tags as input and provides chunks
as output. Similar to POS tags, there are a standard set of Chunk tags like Noun
Phrase(NP), Verb Phrase (VP), etc. Chunking is very important when you want to
extract information from text such as Locations, Person Names etc. In NLP called
Named Entity Recognition.
Natural Language Toolkit (NLTK) - answer A suite of libraries and programs for
symbolic and statistical NLP for English written in the Python programming language.
word sense disambiguation - answer An open problem in NLP regarding the ambiguity
which arises due to different meaning of words in different context.
parsing - answer In this sense, syntactic analysis or parsing may be defined as the
process of analyzing the strings of symbols in natural language conforming to the rules
of formal grammar.
crash blossom - answerA newspaper headline in which some of the words have more
than one possible meaning so that it can be understood in more than one way, often
humorously. This type of ambiguity makes NLP difficult.
word sense ambiguity - answerWord sense ambiguity means a single word or. sentence
is interpreted differently by different users. The main reason for this is that a single.
word has more than one meaning (exact meaning. depends on the context).
regular expression - answerA formal language for specifying text strings. For example
lets say we are looking for woodchucks in a text document. Woodchucks can be
expressed in a few different ways, for example: Woodchuck, Woodchucks, woodchuck,
woodchucks.
