so welcome back to the second lecture of the
first week so in the last lecture we were
we was about the course introduction and this
lecture we will start seeing what do we actually
do in n l p ok so so we ended the last lecture
discussing what are the main goals of n l
p so we talked about two different course
ok so we talked about the very fundamentals
scientific goal that is can we have a very
very deep understanding of the broad language
so and the second goal that we discussed was
engineering goal that is can be designed implement
and test systems that can process natural
language and that can be used for practical
application for our day to day life and we
also said that in this course we will mainly
focus on the engineering course of n l p
so now so so we talked about the engineering
goals ok now what are these goals so let us
see with some examples so now my goals can
be very very ambitious ok so this is just
a fun example so all though so we we use google
translate every now and then so so getting
a very very good quality a perfect translation
is i will say very very ambitious goal ok
so this is snap short taken from google translate
page so if i type google is awesome ah the
transition comes out to be google [FL] if
i go from english to hindi ok so instead of
saying something very very perfect ah it come
it turns out and and says google [FL] thats
not a good translation for this term ok
so so why is that because the the systems
that we have are not perfect they they have
certain engineering solutions for towards
the region but that be not be perfect so you
will you will not get the perfect translation
every every time and that is what we should
always know in back our mind that yes they
are not the perfect systems ok similarly this
is another example so if you if you type in
google translate google is cool we will find
some in the translation google [FL] so now
this is slightly better than the last one
what is to not perfect ok why i am saying
the disease slightly better than the last
one so is so is shanth one of the translation
for the world cool ok yes for a person who
is cool you can say that ah cool can have
an meaning of shanth ok but thats not the
meaning that is intendent in this in this
sentence google is cool
so this is one of the problems that we will
also see in this course that a word might
have multiple science is how do i know that
what is the actual sense that is being use
in the sentence so this is in actual engineering
problem that has to be solved by designing
efficient algorithms ok so now so while i
was talking about some of the transition that
do not go very very well valid in google so
i am just showing is some examples that yes
so this is not not only the case with machines
even humans have made blunders when it comes
to translation ok so in this slide i am showing
you one particular slogan so that was with
pepsi so when pepsi went to china for the
first time for their campaign
so they had the slogan on come alive with
the pepsi generation ok and in china they
had to translated in to chinese and they ended
up translating it as something that meant
pepsi brings your relatives back from the
dead ok so now this looks very funny ok but
if you look at the actual english sentence
you see that you can actually come up with
a translation like that you see generation
is one to one with the relatives and alive
with one to one with back from the dead ok
so this is some sort of ah jugglery of this
words so that you again come up come up with
the very very absurd sort of translation so
this is not only with the machines even humans
have made blunders ok so yeah so this was
so previous k f c so pepsi now you can see
one with k f c so again when they went to
china so they had this slogan on finger licking
good and when the translated in to chinese
it meant we will eat your fingers off ok
so so again you can see the licking and and
all this this they have a correspondence with
the other translation so yeah so unless you
know the other language you will not be able
to translate it perfectly with just by using
it simple dictionary ok it might give you
a very very absurd translation so you have
out so yes so in their many many examples
so for example yeah this is called as hand
grenade and yeah so work in progress translated
as execution progress and if you can if you
if you know what is the meaning of execution
that i am intending here so yes so again coming
to the ambitious goals ok so if you have hard
about the the chatbots that microsoft had
ah released the tay tweets ok so so it was
taken on in less than a day ok
so why did that happen so it it was responding
to how how you humans were ah communicating
with it and very soon it happen that it was
giving very very absurd and and resist ah
tweets and it had to be taken down ok so this
is very nice tweet so tay when from humans
are super cool to full nazis in less than
twenty four hours ok so i am not at all concerned
about the future of a i so again that tells
you yes so its very very difficult to develop
a very very perfect system that that works
for open domain conversation ok so is very
so its very difficult problem to solve so
now so with so we have some goals that are
ah that are very very ambitious but they are
very other goals many other goals that are
also very practical ok for example finding
out if there is a this my queries in correct
i am trying to correct it ok so suppose in
google you type a query so world cup two thousand
fourteen ok and you missed missed out on in
r
so google who give you some sort of ah reply
that ok its are you looking for world cup
two thousand fourteen so instead of w o l
d did you mean w o r l d ok so this kind of
automatic query correction which a a problem
that you can think of solving by using n l
p very very so in very very systematic manner
ok search engines and query completion again
in search engines so if you type somewhat
like if you type a start typing a query so
they also try to predict what is the complete
query that your ah that that your planning
so if you type a query google is they will
try to complete it with what you have queried
before or what other users have queried with
with these types ok
so again so the language modelling concept
that we will discuss in this course goes behind
all these completion tasks then there is the
very very important application on information
extraction that is you have a lot of uninstructed
data in in in the sense of news report and
whatever is so from where you want to identify
what are the entities of interest and what
are the various relations between these entities
ok so for example if you look at this text
so from here you can identify that russell
is a person who works on the post of president
and general manager in the company new york
times newspaper ok and he just he started
his post at this movement so this information
is available in the text data but in an un
instructed format ok
so now can i have an application or a system
that are converted in information to a very
very instructed format so likely here you
have seeing it in a set format ok so you are
finding out various persons to what company
what post they are on and did their tenure
start or end so given lot of text data can
you automatically build up such instruction
data sets of information in relations between
entities so this is the task of information
extraction and this is a very very practical
goal of n l p so in this course we will also
deal with this problem for certain lectures
that how do i start extracting relations between
entites from text data what are the different
algorithms that go behind it
then if you have hard about ah this course
so this is a recent news so in one of the
course in in professor used a chatbot as a
t a for the course ok so so what happened
in the course so the student whatever queries
the students were having so they also result
in chatbot that can try to analyze the queries
and try to give a some readymade answers ok
and it was interesting that after some training
there is the the chatbot was so good that
the students failed to notice ok so from the
article if you see that ah after sometime
when the when the chatbot learnt from the
way issues for querying and the responses
that where ideal for the queries it was giving
answers with some roughly like ninety seven
percent certainty and and yeah t a is the
actual the the human t a is for actual check
the responses first in the then they will
upload on on the photo
so this was again very very practical goal
so so you can contrast it with the open domain
chatbot that we talked about so this was very
very domain is specific chatbot so they were
they built it only for there own ah course
so domain was fixed to their course and the
kind of queries you can expect are also limited
in number two fixed to a certain domain so
building these domain a specific chartbots
or conversation agent is it practical very
very practical goal and also coming up to
to be a important application in recent is
so thinking you starting from the ah conversation
agent that can help you by some product on
an e commerce we up side instead of you having
to search everything ok can you just provide
your specifications to the chatbot and it
can search a product for you or in in the
case of any flight booking system or banking
system where you can give a queries and the
chatbot can come up with the possible reply
by looking in to the document and so on
and this is the practical application that
that can be solved using using n l p then
there is a problem of sentiment analysis again
lot of work has gone in n l p on this and
this is again a very very practical code so
from your all your tweets all your opinions
and comments that you provide in social media
can a tool find out what are various sentiments
of users and and with that you can also find
out are they some transitions and sentiments
of the users over the yes ok a lot of research
was done with recent presidential elections
and in india the the lok sabha elections a
lot of research was on on finding out what
are peoples opinions and sentiments about
various political parties and leaders ok many
of them actually came across to even predicting
who will be the winner of of the elections
and there are many many other goals so we
talked about some interesting goals like building
ah say sentiment analysis building domains
a specific conversational agents ok doing
query completi completion or auto correction
of the query but they are many other goals
like expand deduction so so so you will see
that if your using gmail or any other the
web service many of much of the emails going
to the spam folder directly without even bothering
you ok so so what it happening at the back
and so once and email comes in so the system
tries to see is it spam or not by doing again
text analysis over there and if it just spam
it is not even shown to you you in your inbox
it is join directly send to some a spam folder
so spam deduction is again a very very practical
goal in not only in your emails also on ah
so with social media even on on tweets youtube
comments even youtube videos finding out what
are its spams is again very interesting and
and challenging problem
then you have the problem on machine translation
services on the web so think about opening
a web page from some other country ok so like
from china or ja japan suppose you have you
have going to visit that country and you want
to read that that page so you can is google
translate to load that page in english or
in any other language for you ok and that
that is really really helpful so again this
is a very e practical application where n
l p is is used and find it text summarization
so given a a big news article or scientific
article can i summarize that in short and
then there are many many other applications
where n l p is actually used so remember one
of the some of the previous slides that we
saw in this ah in this lecture so we found
out n l p technology is not perfect ok
so there are many places where ah the systems
make blunders so it is not perfect but it
is still goodness of a many mnay good applications
ok so you can you can know that by so the
way you are using it in your really life so
you can a still use that in many day to day
day life applications and thats what is guiding
this field that so they are lot of applications
and that you can think of so can you come
up with ties ideas nice algorithms first soling
that problem and actually people would use
that and benefit from that ok so lot of applications
you can think off where you can helpless society
by by doing text processing and analytics
so ok so so in the in the next so in this
in this lecture we discussed what are some
of the things that we doing in n l p so next
lecture onwards we will start talking about
see why is n l p heard what makes the language
processing a difficult ask to handle ok
thank you
