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Natural Language Processing(NLP) Assignment Help | Machine Learning Homework Help | Realcode4you

Updated: Jun 15, 2022

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Our Machine Learning Expert Provide NLP Assignment help & NLP homework help. Our expert are able to do your NLP homework assignments at bachelors , masters & the research level. Here you can get top quality code and report at any basic to advanced level. We are solve lots of projects and papers related to NLP and Machine Learning research paper so you can get code with more experienced expert.

In this post we will learn how Natural Language Processing(NLP) work and how to start working using Natural Language Processing(NLP).


To the outside observer, Natural Language Processing (NLP) may seem futuristic. Only around a third of smartphone owners use their personal assistants regularly (a hallmark of NLP technologies), even though 95 percent have tried them at some point, according to Creative Strategies, a consultancy. However, Natural Language Processing advances continue in leaps and bounds, as Digital Neural Networks (DNN) and Machine Learning become more intricate. Both technologies enhance NLP technologies up to 30 percent.


What does NLP mean for Big Data? NLP will change everything, from Business Reporting and Data Analytics/Synthesis to Security and Data Governance. The future has arrived.


What is Natural Language Processing (NLP)

Natural Language Processing (NLP) combines Artificial Intelligence (AI) and computational linguistics so that computers and humans can talk seamlessly. This task has proven quite complex.


Using NLP to Communicate and Summarize Complex Big Data

Business managers have a Big Data problem. They puzzle over dashboards and spreadsheets drowning in too much data and trying to compile it all together into meaningful information.

Using NLP to Turn Language into Useful Data

Question answering technology built on 200 million text pages, encyclopedias, dictionaries, thesauri, taxonomies, ontologies, and other databases has gained traction.

Natural Language Processing and the Future

Advancements in NLP have implications in Data Governance. NLP gathers copious amounts of data from users, raising important legal issues about data ownership, privacy, and security.

Natural Language Processing(NLP)

It is a field machine learning which is used to:

  • Understand,

  • Analyze,

  • Manipulate, and

  • Potentially generate human language.

NLP Topics In Which You can get help

We are providing NLP Assignment Help, Project Help, Homework Help, Coursework Help, below the NLP topics in which you can get help with an affordable prices.



Below the NLP Topics:

Machine Translation, Information Retrieval, Text Simplification, Sentiment Analysis, Text Summarization, Spam Filter, Speech Recognition, Question Answering


Machine Translation

Machine Translation (MT) is the task of automatically converting one natural language into another, preserving the meaning of the input text, and producing fluent text in the output language


Type of Machine Translation:

  1. Statistical Machine Translation or SMT

  2. Rule-based Machine Translation or RBMT

  3. Hybrid Machine Translation or HMT

  4. Neural Machine Translation or NMT


Benefits of machine translation:

Computer programs can translate a huge amount of text rapidly. Yes, the human translator does their work more accurately but they cannot match the speed of the computer.

Example Of Machine Translation: Voice/speech recognition systems, Job portal retrieving candidate details, Computer programs that read financial reports, Google Translate processes, Google-like search engines

Information Retrieval

Information retrieval (IR) may be defined as a software program that deals with the organization, storage, retrieval and evaluation of information from document repositories particularly textual information.


Steps Which Used For Information IR:

  • Data Collection

  • Data Preprocessing

  • Machine Learning Models

- Building the Classifiers: Decision Tree, Logistic Regression, Naïve Bayes, Neural Network, Random Forest, Support Vector Machine
  • Performance Evaluation

Text Simplification

Text Simplification is the task of reducing the complexity of the vocabulary and sentence structure of text while retaining its original meaning, with the goal of improving readability and understanding.

There are two methodology used for text simplification:

  • vocabulary simplification; and

  • simplification of syntactic structures


Sentiment Analysis

Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. A sentiment analysis system for text analysis combines natural language processing (NLP) and machine learning techniques to assign weighted sentiment scores to the entities, topics, themes and categories within a sentence or phrase.


Text Summarization

Text Summarization is one of those applications of Natural Language Processing (NLP) which is bound to have a huge impact on our lives. With growing digital media and ever growing publishing – who has the time to go through entire articles / documents / books to decide whether they are useful or not? Thankfully – this technology is already here.


Spam Filter In NLP

Spam emails or messages belong to the broad category of unsolicited messages received by a user. Spam can also be used in Denial of Service (DOS) or Distributed Denial of Service (DDOS) attacks.


Various techniques are employed to filter out spam messages, usually centered on content-based filtering.


Speech Recognition

Automatic speech recognition can be implemented by gathering a large pool of labeled data, training a model on that data, and then deploying the trained model to accurately label new data.


Question Answering

Question Answering is a computer science discipline within the fields of information retrieval and natural language processing, which focuses on building systems that automatically answer questions posed by humans in a natural language.


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Python: Python is the import programming language which start career as a Data Science and Machine Learning. This is first choice of professionals which learn data science. Now a day it became most popular programming language in the field of data science.

Java: Java is also used for machine learning projects but it is difficult to implement compare to Python programming language. Syntax of this programming language is complex compare to python so developer not choose this as a first choice.

R: R programming is also became most familiar with Data Science and Machine Learning expert. This is also choose by Data Science expert like a python. It is also simple like python and it provide lots of in-built libraries which make it easy to implement.

JavaScript: When we need to create advance level GUI Application related to Machine Learning and Data Science for model prediction then JavaScript used to create the front end design.

MATLAB: MATLAB is also used to predict the Data Science and Machine Learning models. Basically it used to advance level scientific calculations which is related to machine learning.

SCALA: Scala is used in Data processing, distributed computing, and web development. It powers the data engineering infrastructure of many companies. It also used by Data Scientist Developer.

PySpark: It used to handle the Big Data related task. When data is too long then right choice to implement PySpark. It also support the SQL so it make easy to execute the query.

Hive/HADOOP: It also used by Big Data expert to implement big data related task. Now a day most of industries which is working over past decades then data of these industries is too long. To handle these data Hive/HADOOP is the right choice of developer.

Tkinter: This is the python tool which used to create GUI applications. It also used to create Data Science and Machine Learning Application.

Django/Flask: These are the python Framework which is used to create GUI Applications. If you are looking to hire expert which can do your machine learning web application then you can choose these frameworks.


Machine Learning NLP Tutors Provide Help and Assist You In

Here the some machine learning topics in which our expert provide assist you:

  • Classification Trees

  • Optimization Methods

  • Optimization methods

  • Naive Bayes Theorem

  • K Means clustering

  • Decision Tree

  • Natural Language Processing

  • Hidden Markov Models

  • Random Forests

  • Kernel PCA

  • Gradient Boosting

  • Kernel Ridge Regression

  • Factor Analysis Bias and Variance

  • Probabilistic Modeling

  • Deep Learning

  • Artificial Neural Networks

  • Clustering Algorithms

  • Predictive Modeling

  • Hypothesis Space

  • Hierarchical Clustering

  • Instance based Learning

  • Graphical Models and Factor Graphs

  • Ensemble Learning

  • WEKA implementations

  • Machine Learning Project

  • Concept Learning

  • Unsupervised Learning

  • Machine Learning Problems

  • Machine Learning Techniques

  • Supervised Learning

  • Reinforcement Learning


Important Key Applications Of ML NLP

Here the some machine learning Applications:

  • Video Games

  • Automatic Speech Recognition

  • Genomics

  • Computer Vision

  • Mobile Advertising

  • System Biology

  • Language Processing

  • Natural Language Processing

  • Proteomics

  • Face Detection

  • Bioinformatics

  • Text mining

  • Image Recognition

  • CRM Technologies

  • Microarrays

  • Pattern Recognition

  • Image Recognition

  • Neural Networks

  • Bayesian Network

  • DNA sequence analysis

  • Data Mining

  • Protein sequence analysis

  • Cognitive Services

  • Automatic Game Playing

  • Predicting functional structures

  • Predictive Learning

  • Object Classification in Photographs

  • Reinforcement Learning

  • Automatic Machine Translation

  • Metabolic and regulatory networks


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How we will work with NLP?

First need to install NLTK is we want to work with NLTK, here pip command to insall the NLTK:


Installing it using pip with piping symbol in jypyter notebook:

!pip install nltk

then after this import and download the nltk:

import nltk
nltk.download()

Now use pandas to read dataset:

import pandas as pd
df = pd.read_csv("file.csv")
df.head()

Now Pre-processing the data:

Below the some steps which need to data pre-processing:

Remove punctuation, Tokenization, Remove stopwords, Preprocessing Data: Stemming, Preprocessing Data: Lemmatizing, Vectorizing Data, Vectorizing Data: Bag-Of-Words, Vectorizing Data: N-Grams, Vectorizing Data: TF-IDF, Feature Engineering: Feature Creation, And last building a machine learning model: And get the result with accuracy


Sample NLP Code Implementation

!pip install nltk

#1: import NLTK
import nltk

#3:importing Twitter Sample
from nltk.corpus import twitter_samples

#4: Files presend in twitter_samples
twitter_samples.fileids()
output: ['negative_tweets.json', 'positive_tweets.json', 'tweets.20150430-223406.json']

#5: download swadesh corpora
nltk.download('swadesh')

#6:Print all file presend in gutenberg
nltk.download('gutenberg')

files = nltk.corpus.gutenberg.fileids()
print(files)

#7: printing content of shakespeare-macbeth.txt
path = nltk.data.find('corpora/gutenberg/shakespeare-macbeth.txt')
raw = open(path, 'rU').read()
raw
#8: Saving Content of shakespeare-macbeth.txt inot macbeth.txt
with open("macbeth.txt", "w") as f1:
    for line in raw:
        f1.write(line)

#9: counting number of article
file = open("macbeth.txt", "r")
data = file.read()

occurrences = data.count("a")
occurrences
Output: 5313

#9: removing article
infile = "macbeth.txt"
outfile = "without_article.txt"

delete_list = ["a", "an", "the", "A", "An", "The"]
with open(infile) as fin, open(outfile, "w+") as fout:
    for line in fin:
        for word in delete_list:
            line = line.replace(word, "")
        fout.write(line)

#10: removing puctuation
punctuations = '''!()-[]{};:'"\,<>./?@#$%^&*_~'''
 
no_punc = ""
for char in data:
   if char not in punctuations:
       no_punc = no_punc + char
 
print("Punctuation Free String: ",no_punc)

#11: download names corpora
nltk.download('names')

#12 total number of words in names corpora
nltk.corpus.names.fileids()
Output: ['female.txt', 'male.txt']

path_female = nltk.data.find('corpora/names/female.txt')
path_male = nltk.data.find('corpora/names/male.txt')
raw_female = open(path_female, 'rU').read()
raw_male = open(path_male, 'rU').read()
raw_male
raw_female
#count word in female.txt
wordlist = raw_female.split()

wordfreq = []
count_female= 0
for w in wordlist:
    wordfreq.append(wordlist.count(w))
    count_female = count_female+1
print(count_female)

Output: 5004

#count word in male.txt
wordlist_male = raw_male.split()

wordfreq_male = []
count_male= 0
for w in wordlist_male:
    wordfreq_male.append(wordlist_male.count(w))
    count_male = count_male+1
print(count_male)

Output: 2943



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