NLP-Questions Class 10 Ai
NLP-Questions Class 10 Ai
NLP-Questions Class 10 Ai
1. What is a Chabot?
A chatbot is a computer program that's designed to simulate human conversation through
voice commands or text chats or both. Eg: Mitsuku Bot, Jabberwacky etc. A chatbot is also
known as an artificial conversational entity (ACE), chat robot, talk bot, chatterbot or
chatterbox.
9. What are the types of data used for Natural Language Processing applications?
Natural Language Processing takes in the data of Natural Languages in the form of written
words and spoken words which humans use in their daily lives and operates on this.
Stemming: Stemming is a rudimentary rule-based process of stripping the affixes (“ing”, “ly”,
“es”, “s” etc) from a word. Stemming is a process of reducing words to their word stem, base
or root form (for example, books — book, looked — look).
Lemmatization: Lemmatization, on the other hand, is an organized & step by step procedure
of obtaining the root form of the word, it makes use of vocabulary (dictionary importance of
words) and morphological analysis (word structure and grammar relations).
The aim of lemmatization, like stemming, is to reduce inflectional forms to a common base
form. As opposed to stemming, lemmatization does not simply chop off inflections. Instead it
uses lexical knowledge bases to get the correct base forms of words.
13. Which words in a corpus have the highest values and which ones have the least?
Stop words like - and, this, is, the, etc. have highest values in a corpus. But these words do
not talk about the corpus at all. Hence, these are termed as stopwords and are mostly
removed at the pre-processing stage only. Rare or valuable words occur the least but add
the most importance to the corpus. Hence, when we look at the text, we take frequent and
rare words into consideration.
14. Does the vocabulary of a corpus remain the same before and after text normalization?
Why?
No, the vocabulary of a corpus does not remain the same before and after text
normalization. Reasons are –
● In normalization the text is normalized through various steps and is lowered to minimum
vocabulary since the machine does not require grammatically correct statements but the
essence of it.
● In normalization Stop words, Special Characters and Numbers are removed.
● In stemming the affixes of words are removed and the words are converted to their base
form.
So, after normalization, we get the reduced vocabulary.
15. What is the significance of converting the text into a common case?
In Text Normalization, we undergo several steps to normalize the text to a lower level. After
the removal of stop words, we convert the whole text into a similar case, preferably lower
case. This ensures that the case-sensitivity of the machine does not consider same words
as different just because of different cases.
TFIDF stands for Term frequency–inverse document frequency. It is a numerical statistic that
is intended to reflect how important a word is to a document in a collection or corpus.
20. What are stop words? Explain with the help of examples.
“Stop words” are the most common words in a language like “the”, “a”, “on”, “is”, “all”. These
words do not carry important meaning and are usually removed from texts.
consider the text, "Students are requested to make a model for science fair."
In the above statement, ' are, to, a , for ' do not carry meaning for a computer to understand
the sentence. These are stopwords and can be removed.
It is possible to remove stop words using Natural Language Toolkit (NLTK), a suite of
libraries and programs for symbolic and statistical natural language processing.
21. Classify each of the images according to how well the model’s output matches the data
samples:
Here, the dashed line is model’s output while the crosses are actual data samples.
● In the first figure, the model’s output does not match the true function at all. Hence the
model is said to be under fitting and its accuracy is lower.
● In the second case, model performance is trying to cover all the data samples even if they
are out of alignment to the true function. This model is said to be over fitting and this too has
a lower accuracy
● In the third one, the model’s performance matches well with the true function which states
that the model has optimum accuracy and the model is called a perfect fit.
22. Explain how AI can play a role in sentiment analysis of human beings?
The goal of sentiment analysis is to identify sentiment among several posts or even in the
same post where emotion is not always explicitly expressed. Companies use Natural
Language Processing applications, such as sentiment analysis, to identify opinions and
sentiment online to help them understand what customers think about their products and
services (i.e., “I love the new iPhone” and, a few lines later “But sometimes it doesn’t work
well” where the person is still talking about the iPhone) and overall . Beyond determining
simple polarity, sentiment analysis understands sentiment in context to help better
understand what’s behind an expressed opinion, which can be extremely relevant in
understanding and driving purchasing decisions.
23. Why are human languages complicated for a computer to understand? Explain.
The communications made by the machines are very basic and simple. Human
communication is complex. There are multiple characteristics of the human language that
might be easy for a human to understand but extremely difficult for a computer to
understand.
Some of the challenges in understanding human language are:
Arrangement of the words and meaning - There are rules in human language. There are
nouns, verbs, adverbs, adjectives. A word can be a noun at one time and an adjective some
other time. This can create difficulty while processing by computers.
Different syntax, same semantics: 2+3 = 3+2 Here the way these statements are written is
different, but their meanings are the same that is 5. Different semantics, same syntax: 2/3
(Python 2.7) ≠ 2/3 (Python 3) Here the statements written have the same syntax but their
meanings are different. In Python 2.7, this statement would result in 1 while in Python 3, it
would give an output of 1.5.
Multiple Meanings of a word - In natural language, it is important to understand that a word
can have multiple meanings and the meanings fit into the statement according to the context
of it.
Perfect Syntax, no Meaning - Sometimes, a statement can have a perfectly correct syntax
but it does not mean anything. In Human language, a perfect balance of syntax and
semantics is important for better understanding.
These are some of the challenges we might have to face if we try to teach computers how to
understand and interact in human language.
24. What are the steps of text Normalization? Explain them in brief.
In Text Normalization, we undertake several steps to normalize the text to a lower level.
Sentence Segmentation - Under sentence segmentation, the whole corpus is divided into
sentences. Each sentence is taken as a different data so now the whole corpus gets
reduced to sentences.
Tokenisation- After segmenting the sentences, each sentence is then further divided into
tokens. Tokens is a term used for any word or number or special character occurring in a
sentence. Under tokenisation, every word, number and special character is considered
separately and each of them is now a separate token.
Removing Stop words, Special Characters and Numbers - In this step, the tokens which are
not necessary are removed from the token list.
Converting text to a common case -After the stop words removal, we convert the whole text
into a similar case, preferably lower case. This ensures that the case-sensitivity of the
machine does not consider same words as different just because of different cases.
Stemming - In this step, the remaining words are reduced to their root words. In other words,
stemming is the process in which the affixes of words are removed and the words are
converted to their base form.
Lemmatization -In lemmatization, the word we get after affix removal (also known as lemma)
is a meaningful one.
With this we have normalized our text to tokens which are the simplest form of words
present in the corpus.
25. Normalize the given text and comment on the vocabulary before and after the
normalization:
Raj and Vijay are best friends. They play together with other friends. Raj likes to play football
but Vijay prefers to play online games. Raj wants to be a footballer. Vijay wants to become
an online gamer.
Tokenization
Raj And Vijay Are Best Friends . They
Play Together With Other Friends . Raj Likes
To Play Football But Vijay Prefers To Play
Online Games . Raj Wants To Be A
Footballe . Vijay Wants To Become An Online
r
gamer .
Removing Stop words, Special Characters and Numbers:
In this step, the tokens which are not necessary are removed from the token list. So, the
words and, are, to,a, an, . (Punctuation) will be removed.
Stemming:
In this step, the remaining words are reduced to their root words. In other words, stemming
is the process in which the affixes of words are removed and the words are converted to
their base form.
Given text :
Raj and Vijay are best friends. They play together with other friends. Raj likes to play football
but Vijay prefers to play online games. Raj wants to be a footballer. Vijay wants to become
an online gamer.
Normalized text:
Raj Vijay best friends they play together with other friends Raj like play football Vijay prefer
online games Raj want be footballer Vijay want become online gamer
27. Through a step-by-step process, calculate TFIDF for the given corpus and mention the
word(s) having highest value.
The formula of TFIDF for any word W becomes: TFIDF(W) = TF(W) * log (IDF(W))