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Dive deep into the linguistic wonders that make NLP tick. It's like learning the ABCs but for making computers understand human lingo!
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Welcome back.
0:00
In this video we'll explore the critical
concepts that formed the backbone of NLP
0:01
from morphological and syntactic analysis
to semantic and discourse analysis.
0:06
Additionally, we'll cover the fundamental
techniques that help machines make sense
0:12
of text including tokenization, Stop
Word Removal, stemming lemmatization and
0:17
part of speech tagging.
0:21
By the end of this video, you'll have a
solid grasp of the building blocks of NLP
0:22
and how they're shaping a world where
machines can understand human language.
0:26
The first four concepts presented in
the video, morphological analysis,
0:31
syntactic analysis, semantic analysis,
and discourse analysis.
0:35
Are primarily focused on understanding and
0:39
interpreting human language at
increasing levels of complexity.
0:41
Each of these concepts is hierarchical,
0:45
with one building upon the understanding
developed at the previous level.
0:48
At the most basic level, morphological
analysis examines the structure of words.
0:52
It looks at how words are formed and
identifies the building blocks called
0:58
morphemes, which are the smallest
units of meaning.
1:01
This includes understanding prefixes,
suffixes and root words,
1:05
which helps in recognizing different
word forms and their meanings.
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Moving up, syntactic analysis is about
dissecting the structure of sentences.
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It involves applying grammatical rules to
assemble words into meaningful phrases and
1:16
sentences.
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This is where we analyze how words come
together to create sentences ensuring
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the correct interpretation of each
component in its specific context.
1:25
Then we have semantic analysis
where the focus shifts to meaning.
1:30
This layer is concerned with
how sentences convey meaning,
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interpreting the nuances and
concepts behind the words.
1:37
It deals with the significance
of words and phrases in context,
1:40
aiming to understand the literal and
figurative meanings within the text.
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Finally, at the discourse level,
1:48
we consider the larger
context of language use.
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Discourse analysis goes beyond
individual sentences to look at
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how language functions across stretches of
text and in different social interactions.
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It's about understanding how sentences
relate to each other to convey a coherent
2:01
message.
2:05
Considering factors like the speaker's
intent and the listener's interpretation.
2:06
With a foundational understanding
of language established,
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let's explore the specific techniques
used in NLP to prepare and
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refine text data for
computational processing.
2:18
Up first, we have tokenization.
2:21
Tokenization is the foundational step in
NLP where text is segmented into tokens,
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meaningful pieces that can be words,
phrases or symbols.
2:29
This process is akin to separating
a sentence into individual words,
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providing a clear structure for
computers to analyze.
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Tokenization paves the way for
complex tasks like sentiment analysis,
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machine translation and voice recognition.
2:45
Next, we have Stop Word Removal.
2:48
Stop words are the frequent fillers
in language that add little to
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the substantive content words such as,
and, the, and a.
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In NLP, stop word removal is like
sifting the grain from the chaff.
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It helps algorithms focus
on the meaningful content,
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streamlining data processing and enhancing
the performance of language models.
3:06
Another technique used in natural language
processing is Stemming and Lemmatization.
3:10
Stemming is the process of cutting
down words to their base or root form,
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simplifying the complex
variations we find in languages.
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Lemmatization takes this a step further,
considering the context and
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converting words to their canonical forms.
3:29
These techniques are crucial in unifying
the many faces of words, allowing for
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more accurate search results and
data analysis.
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Furthermore, the last technique we'll
go over is part-of-Speech Tagging.
3:40
Part-of-Speech-Tagging is the process
of classifying words into their
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grammatical categories, identifying
them as nouns, verbs, adjectives, etc.
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This is like assigning roles to
actors on the stage of a sentence.
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It's an essential process that helps in
understanding the grammatical structure of
4:00
sentences, which is vital for accurate
translation, question-answering systems,
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and even generating language.
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And that's it for this video.
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Now that we understand more about how NLP
works, join me in the next video where
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I'll discuss some of the tasks that
natural language processing is used for.
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I'll see you in the next one.
4:21
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