NLU vs NLG: NLP Explained
Key Points
- NLP (natural language processing) is the umbrella term for computer techniques that let machines read, understand, and generate human language, encompassing both NLU (understanding) and NLG (generation).
- NLU focuses on syntactic and semantic analysis to infer meaning from unstructured text, such as disambiguating the word “current” as a noun in “Alice is swimming against the current” versus an adjective in “The current version of the file is in the cloud.”
- NLG is the complementary process where a system produces coherent language, exemplified by the speaker writing a story to illustrate text generation.
- Core NLP methods that enable NLU and NLG include tokenization, stemming, lemmatization, and named entity recognition, all powered by deep‑learning models.
- Practical applications of NLP range from language translation to conversational chatbots, turning raw human language into actionable, machine‑readable formats.
Full Transcript
# NLU vs NLG: NLP Explained **Source:** [https://www.youtube.com/watch?v=1I6bQ12VxV0](https://www.youtube.com/watch?v=1I6bQ12VxV0) **Duration:** 00:06:49 ## Summary - NLP (natural language processing) is the umbrella term for computer techniques that let machines read, understand, and generate human language, encompassing both NLU (understanding) and NLG (generation). - NLU focuses on syntactic and semantic analysis to infer meaning from unstructured text, such as disambiguating the word “current” as a noun in “Alice is swimming against the current” versus an adjective in “The current version of the file is in the cloud.” - NLG is the complementary process where a system produces coherent language, exemplified by the speaker writing a story to illustrate text generation. - Core NLP methods that enable NLU and NLG include tokenization, stemming, lemmatization, and named entity recognition, all powered by deep‑learning models. - Practical applications of NLP range from language translation to conversational chatbots, turning raw human language into actionable, machine‑readable formats. ## Sections - [00:00:00](https://www.youtube.com/watch?v=1I6bQ12VxV0&t=0s) **Distinguishing NLP, NLU, and NLG** - The speaker explains that natural language processing encompasses computer‑based tasks, with natural language understanding and generation as its two main sub‑areas, and outlines key techniques like tokenization, stemming, lemmatization and named‑entity recognition. ## Full Transcript
natural language
processing
natural language
understanding
and natural language
generation
n l p
n l u
n l g
what's the difference
allow me to demonstrate
now look what i just did there is well
by writing a story
i performed an example of
natural language generation and if
you're now peering at the screen reading
it or even just and trying to understand
and make sense of what i'm saying right
now
you're participating in natural language
understanding and together you and i are
both performing subsets of the overall
collective of
natural language
processing so
n l
u
and n l
g
they're both subsets of n
l
p
but we're missing one quite important
point here the natural language
processing stuff that we're interested
in today is performed by
computers not humans so when we use
these terms what do we really mean and
how can these models be put to work well
nlp enables computers to understand
human language in both written and
verbal forms using deep learning
techniques to complete tasks typical
examples for that are things like
language translation or conducting a
conversation in a chat bot
now it does this through the
identification of named entities which
is a process called named entity
recognition
and identification of word patterns
using methods like tokenization stemming
and lemmatization and i've covered some
of this in a previous video about nlp so
we won't go over that in detail here
let's focus instead on these two things
natural language understanding and
natural language generation
so natural language understanding
uses syntactic and semantic analysis of
text and speech to determine the meaning
of a sentence
unlike structured computer code our
unstructured messy human language has
all sorts of nuances that nlu needs to
account for so let's take a look at a
couple of examples i'm going to cover
some sentences here so like alice
is swimming
against the current
this is a sentence that we could feed
into an nlu algorithm and ask it to
really make sense of it
another example
the current
version
of the file
is in the cloud
so that's two sentence examples let's
take a closer look at trying to make
some sense of these so we've got the
word current here in this first sentence
the word current is a noun and that's
preceded by a verb the verb here is
swimming together that provides
additional context to the reader
allowing us to conclude that we are
referring to the flow of water in the
ocean when we talk about current in this
situation in the second example here's
the word current
and this time it's an adjective and the
noun it describes
is version
so that denotes that we've got multiple
iterations of a report and here current
is implying that we have the most
up-to-date status of the file
so two completely different meanings for
current
and understanding the relationships
between words and phrases is what nlu is
really all about and enables us to
derive the intended meaning of a
sentence
now while nlu is all about improving a
computer's reading comprehension
nlg or natural language generation
focuses on enabling computers to write
it's the process of producing a human
language text response based on some
data input
nlg applications need to consider
language rules based on morphology
lexicons syntax and semantics to make
choices on how to
phrase responses appropriately
now nlg typically consists of three
stages so if we look at nlg
the first stage is text
planning
and text planning formulates the orders
and the content in a logical manner
similarly we have sentence
planning
and sentence planning considers things
like punctuation and text flow and
breaks out the content into paragraphs
and sentences and then the third stage
is called
realization
and realization ensures we're playing
correctly by the rules of grammar that
for example we know that the past tense
of the verb run
is
actually ran
and not
runned
yeah that's that's not right
so nlg is enabled by a variety of
machine learning models to perform this
stuff and that includes things like
hidden markov chains recurrent neural
networks and transformers
look natural language processing and its
subsets nlu and nlg have numerous
practical applications from healthcare
diagnosis to online customer service
oh and another
way you can use these is in
hey lightboard videos in fact i asked an
nlg algorithm to write me a sentence to
conclude this talk
and it said
natural language processing is amazing
and has many practical applications
like me
thanks nlp algorithm
if you have any questions please drop us
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thanks for watching