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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:
Statistical Machine Translation or SMT
Rule-based Machine Translation or RBMT
Hybrid Machine Translation or HMT
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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Get Help In Supervised Learning
Supervised learning as the name specifies shows the presence of a superviser as an educator. Essentially supervised learning is a learning wherein we teach or train the machine utilizing information in which the target lables are present. algorithms include: Support Vector Machines, Linear Regression, Logistic Regression, Naive Bayes, Linear Discriminanat Analysis, Neural Networks.
For more details about Supervised Learning read our blogs:
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Other Programming Languages Used For Data Scientist
There are many other programming languages and Tools which used to do Data Science and Machine Learning Task. Here we can discussing these programming languages in which our expert also providing the Data Science and Machine Learning programming Help:
​
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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