Top 20 NLP Interview Questions for Mid-Level Professionals

Natural
language
processing
has
become
one
of
the
in-demand
skills
for
AI
jobs.
It
is
a
blend
of
computer
science,

AI
and
linguistics
that
bridges
the
communication
gap
between
humans
and
machines.
NLP
focuses
on
designing
systems
that
can
understand
and
process
natural
language
data.
You
can
find
multiple
examples
of
systems
using
NLP
in
your
everyday
lives.
The
growing
demand
for
NLP
experts
has
increased
the
curiosity
to
learn
top
NLP
interview
questions
for
NLP
jobs.
With
prior
awareness
of
NLP
interview
questions
and
their
answers,
you
can
appear
for
every
interview
with
confidence.
Let
us
learn
the
most
important
NLP
interview
questions
for
mid-level
professionals.

Significance
of
Learning
NLP
Interview
Questions

You
might
have
some
doubts
before
learning
NLP
interview
questions.
Most
of
the
doubts
on
your
mind
now
will
likely
point
at
the
reasons
to
learn
interview
questions
for
NLP
jobs.
The
best
way
to
find
the
answers
to
such
doubts
involves
learning
about
the
significance
of
natural
language
processing. 

It
is
important
to
know
that
NLP
is
the
driving
force
behind
applications
such
as
language
translation
services,

chatbots
and
sentiment
analysis
apps.
NLP
plays
a
crucial
role
in
enhancing
customer
service,
automation
of
routine
tasks
and
extracting
insights
from
unstructured
data. 

Learning
the
important
questions
for
NLP
job
interviews
can
help
you
prepare
for
a
promising
career
path
in
the
future
of
technology.
You
will
not
only
improve
your
NLP
knowledge
but
also
discover
the
ideal
approaches
to
answer
interview
questions.

Take
your
first
step
towards
learning
about
artificial
intelligence
through AI
Flashcards

Discovering
Important
Mid-Level
NLP
Interview
Questions

Anyone
can
pursue
a
career
in
NLP
with
the
right
training
and
guidance.
You
can
use
advanced
NLP
interview
questions
as
references
to
test
your
knowledge
of
natural
language
processing.
Beginners
can
answer
fundamental
level
interview
questions
on
NLP
with
confidence.
However,
you
will
need
something
more
to
secure
better
jobs
as
NLP
experts.
The
following
NLP
interview
questions
for
mid-level
professionals
will
help
you
test
your
capabilities
before
appearing
in
an
NLP
interview.

1.
Can
you
mention
some
sources
to
obtain
data
for
NLP
projects?

You
can
obtain
data
for
NLP
projects
from
multiple
sources.
The
most
notable
data
sources
are
public
datasets
such
as
Google
Datasets.
Another
promising
source
of
data
for
NLP
projects
is
data
scraping
in
which
you
can
scrape
data
from
different
websites.
You
will
not
get
structured
data
through
web
scraping. 

2.
How
does
data
augmentation
work
in
NLP
projects?

The
list
of
NLP
questions
and
answers
for
mid-level
professionals
will
include
entries
that
will
test
your
practical
knowledge.
Data
augmentation
is
a
useful
technique
to
prepare
datasets
for
NLP
projects
from
existing
datasets.
It
primarily
involves
using
language
properties
to
create
text
that
has
the
same
syntax
as
the
source
text
data.
You
can
implement
data
augmentation
in
NLP
projects
through
techniques
such
as
entity
replacement,
back
translation,
adding
noise
and
replacing
synonyms.

3.
Do
you
know
the
meaning
of
TF-IDF
in
natural
language
processing?

TF-IDF
in
NLP
stands
for
Term
Frequency-
Inverse
Document
Frequency.
It
is
a
useful
tool
to
find
the
significance
of
a
specific
word
as
compared
to
other
words
in
the
corpus.
TF-IDF
serves
as
the
preferred
scoring
metric
for
summarization
and
information
retrieval
tasks.
It
ensures
conversion
of
words
into
vectors
followed
by
adding
semantic
information
to
generate
weighted
unusual
words
that
you
can
use
in
different

NLP
applications.

4.
How
do
you
use
bag-of-words
model
in
NLP?

The
Bag-of-Words
or
BoW
model
is
a
common
method
for
representation
of
text
data
in
NLP
tasks.
The
model
converts
text
into
vector
of
word
frequencies
without
including
word
order
and
grammar.
Every
word
in
the
text
corpus
turns
into
a
feature
and
the
vector
represents
the
number
of
times
a
word
appears
in
the
document.
BoW
model
is
useful
in
text
clustering
and
classification
tasks. 

5.
What
is
word
sense
disambiguation
in
NLP?

The
most
common
natural
language
processing
interview
questions
for
mid-level
professionals
can
also
include
references
to
word
sense
disambiguation.
It
is
the
process
to
determine
the
sense
in
which
a
word
has
been
used
in
a
specific
context.
Word
sense
disambiguation
is
useful
in
NLP
as
words
can
have
multiple
meanings.
The
significance
of
word
sense
disambiguation
is
clearly
visible
in
tasks
such
as
information
retrieval,
text
analysis
and
machine
translation.

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6.
What
is
syntactic
parsing?

Syntactic
parsing
is
a
method
for

syntax
analysis
that
involves
assessment
of
the
grammatical
structure
in
a
sentence.
The
analysis
helps
in
recognizing
the
syntactic
relationship
between
words
to
generate
a
dependency
graph
or
parse
tree.
Syntactic
parsing
is
a
useful
technique
for
tasks
such
as
information
extraction
and
machine
translation. 

7.
Do
you
know
about
the
role
of
autoencoders
in
NLP?

Autoencoders
are
networks
that
help
in
learning
the
vector
representation
of
an
input
in
compressed
form.
It
follows
an
unsupervised
learning
approach
as
you
don’t
need
labels
for
autoencoders.
The
primary
purpose
of
autoencoders
in
NLP
tasks
revolves
around
learning
the
mapping
function
directly
from
the
input.

8.
Can
you
explain
the
concept
of
Latent
Semantic
Indexing?

The
best
NLP
interview
questions
for
mid-level
NLP
job
roles
will
also
focus
on
concepts
like
Latent
Semantic
Indexing
or
LSI.
It
is
a
mathematical
method
to
improve
accuracy
in
information
retrieval
tasks.
The
method
helps
in
discovering
hidden
relationships
between
words
by
creating
a
collection
of
different
concepts
related
to
the
terms
in
a
phrase.

9.
What
is
the
utility
of
ensemble
methods
in
NLP
projects?

Ensemble
methods
help
in
obtaining
an
output
or
making
predictions
by
combining
different
independent
models.
The
primary
utility
of
ensemble
methods
in
NLP
projects
revolves
around
overcoming
drawbacks
such
as

bias,
noise
and
variance.
You
can
create
an
ensemble
method
by
combining
different
models
such
as
logistic
regression,
random
forest
and
SVM. 

10.
What
do
you
know
about
pragmatic
analysis
in
NLP?

Pragmatic
analysis
helps
in
obtaining
knowledge
from
the
outside
world
or
outside
the
context
of
the
questions
or
documents.
You
might
find
many
parts
of
natural
language
that
need
real-world
knowledge
for
interpretation.
Pragmatic
analysis
helps
in
such
cases
by
focusing
on
the
description
and
providing
another
interpretation
of
its
actual
meaning.

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11.
Does
perplexity
matter
in
NLP
projects?

Yes,
perplexity
is
an
important
metric
for
determining
the
effectiveness
of
language
models
used
in
NLP
projects.
You
can
represent
perplexity
as
a
mathematical
function
representing
the
likelihood
of
a
language
model
describing
a
test
sample.
With
more
perplexity,
language
models
convey
lesser
information.

12.
Do
you
know
anything
about
co-reference
resolution?

Your
search
for
top
NLP
interview
questions
will
also
test
your
knowledge
of
concepts
like
co-reference
resolution.
It
is
a
natural
language
processing
task
that
focuses
on
identification
of
all
expressions
in
a
text
that
represent
the
same
entity.
The
primary
goal
of
co-reference
resolution
revolves
around
identifying
whether
words
and
phrases
in
a
sentence
represent
the
same
things
in
the
real
world.

13.
What
do
you
know
about
the
GRU
model?

GRU
or
Gated
Recurrent
Unit
model
is
a
variant
of
recurrent
neural
network
architecture
used
across
a
wide
range
of
NLP
tasks.
It
aims
at
resolving
the
problem
of
vanishing
gradient
alongside
capturing
the
long-term
dependencies
in
sequential
data.
The
gating
mechanisms
used
in
GRU
make
it
almost
similar
to
LSTM
networks
albeit
with
a
simpler
architecture
that
uses
fewer
gates. 

14.
How
can
you
use
masked
language
modeling?

Masked
language
modeling
is
a
trusted
and
effective
NLP
technique
to
obtain
output
from
a
contaminated
input.
You
can
leverage
the
masked
language
modeling
approach
to
build
expertise
in
deep
representations
for
downstream
tasks.
Masked
language
modeling
can
help
in
predicting
words
based
on
presence
of
other
words
in
a
text.

15.
How
can
you
manage
noisy
text
data
in
NLP
projects?

The
advanced
NLP
interview
questions
for
mid-tier
NLP
engineers
will
also
evaluate
your
effectiveness
in
managing
datasets
for
NLP
projects.
You
can
handle
noisy
text
data
in
NLP
projects
by
using
different
preprocessing
steps
for
cleaning
and
preparing
the
data.
Some
of
the
common
preprocessing
techniques
include
normalization
of
text,
removing
misspellings
and
filtering
non-textual
elements
such
as
HTML
tags.

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16.
What
is
the
best
way
to
measure
the
performance
of
NLP
models?

The
best
approach
to
measure
the
performance
of
NLP
models
involves
the
use
of
notable
metrics.
Some
of
the
most
common
metrics
used
for
NLP
models
include
accuracy,
F1-score,
precision
and
recall.
You
can
choose
the
ideal
metric
according
to
the
task
that
the
model
has
to
perform. 

17.
Have
you
faced
any
challenges
in
processing
large-scale
text
data?

Yes,
you
might
encounter
many
challenges
in
processing
large-scale
text
data
such
as
memory
limitations
and
computational
restrictions.
Another
prominent
challenge
for
processing
large-scale
text
data
is
the
requirement
of
efficient
mechanisms
for
data
storage
and
retrieval.
You
can
solve
them
by
using
distributed
computing
frameworks
and
cloud-based
services
to
expand
the
scale
of
processing
capabilities.

18.
Can
you
describe
the
importance
of
lexical
analysis
in
NLP?

Your
preparation
for
natural
language
processing
interview
questions
will
be
incomplete
without
focusing
on
lexical
analysis.
It
involves
conversion
of
a
sequence
of
characters
into
a
token
sequence
that
helps
in
identification
and
classification
of
individual
words
in
the
text.
Lexical
analysis
serves
a
vital
role
in
more
complex
NLP
tasks
by
offering
a
more
structured
representation
of
text.

19.
What
are
the
useful
techniques
to
handle
out-of-vocabulary
words?

Out-of-vocabulary
words
are
one
of
the
common
challenges
in
implementation
of
language
models.
You
can
deal
with
them
by
using
different
strategies
such
as
open
vocabulary
approaches,
subword
tokenization
or
special
tokens.
It
is
important
to
know
that
the
ideal
strategy
for
dealing
with
out-of-vocabulary
words
depends
on
the
specific
application.                 

20.
How
is
attention
mechanism
relevant
for
NLP
projects?

Attention
mechanism
in
neural
networks
serves
as
a
valuable
technique
for
focusing
on
specific
elements
of
the
input
during
generating
an
output.
Attention
mechanisms
are
useful
in
NLP
projects
that
involve
long
sequences
in
which
traditional
methods
can
ignore
important
information. 

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Final
Thoughts 

The
NLP
questions
and
answers
highlighted
in
this
discussion
can
help
you
prepare
for
NLP
job
interviews.
You
can
notice
that
the
questions
focus
on
technical
concepts
and
practical
ways
for
using
different
concepts
and
tools
in

NLP
projects.
Discover
the
best
resources
to
enhance
your
NLP
expertise
and
prepare
for
interview
questions
right
now.