Natural language processing (NLP) is concerned with interactions between computers and human languages. NLP analyzes text to handle tasks like summarization, translation, sentiment analysis, and topic segmentation. The Natural Language Toolkit (NLTK) is a Python library that provides tools for NLP tasks like tokenization, stemming, tagging, parsing, and classification. Tokenization is the process of splitting text into tokens or chunks. Bag-of-words is an algorithm that encodes text as numeric vectors, representing word presence or absence to allow machine learning on text data. NLP has applications in areas like spam filtering, chatbots, and sentiment analysis.

1. Natural Language Processing using python(NLTK)

2. UNIVERSITY INSTITUTE OF TECHNOLOGY, BURDWAN
SEMINAR - 1
IT – 792
Academic Year : 2016-20
NATURAL LANGUAGE PROCESSING USING PYTHON
Submitted by : PRAKASH ANAND (20163016)

3. Natural Language Processing
Natural language processing (NLP) is an area of computer science and
artificial intelligence concerned with the interactions between computers
and human (natural) languages.
It is the branch of machine learning which is about
analyzing any text and handling predictive analysis. By utilizing NLP and its
components, we can organize the massive chunks of text data, perform
numerous automated tasks and solve a wide range of problems such as –
automatic summarization, machine translation , named entity recognition
,sentiment analysis, speech recognition, and topic segmentation etc.

4. Regular Expression(regex)
Regular expression are strings that have special
syntax which allows you to match patterns and find
other strings.
It is present as a built-in package called ‘re’ in
python.
Some Applications of regex:
 Find all web links in a document.
 Parse email addresses , remove/replace
unwanted characters.

7. What is NLTK?
The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and
programs for symbolic and statistical natural language processing (NLP) present in
python.
It is free and open source.
Suite of libraries for variety of academic text processing tasks:
- tokenization, stemming, tagging,
- chunking ,parsing ,classification,
- Language modeling , logical semantics.

8. Tokenization
It is a process of tokenizing or splitting a string, text into a list of
tokens. document into smaller chunks (tokens).
Some Common application include :
1. Easier to map part of speech.
2. Matching common words.
3. Separating all hashtags in a tweet.
4. Separating punctuation, period and stopwords.
5. Removing unwanted tokens.
6. Breaking out words or sentences.

9. Implementation of tokenization using python

10. Classification using NLP
Supervised classifiers can perform a wide variety of NLP tasks, including
document classification, part-of-speech tagging, sentence segmentation etc.
Example : Decision tree implementation in NLP
“Jennie ate an apple”

11. Application of NLP in Classification problems
 Spam classifier in email.
 Sentence segmentation.
 Identifying Dialogue act types.
 Recognizing Textual Entailment.

12. Vectorizing Data : Bag-Of-Words
It is the process of encoding text as integers which means numeric form to create
feature vectors so that machine learning algorithms can understand our data.
Bag of Words(BoW) is a feature extraction
algorithm describes the presence of words within the text data .It gives a result of
1 if present in the sentence and 0 if not present. It creates two-dimensional
matrix with count of each frequent occurred words are listed.

13. Application of NLP using python
It is used to identify and separate words(Text classifier).
To extract topics in a text(Topic Recognition).
Build your own fake news classifier.
Spam filtering.
Chatbots.
Sentiment Analysis.
 Machine Translation.

15. Future of NLP
 Semantic web search.
 Sentiment Analysis/ Opinion Mining.
 Machine Translation.
 Advanced speech processing application.
 Social Network analysis.
 Collective Intelligence.

16. Conclusion
NLP takes a very important role in new machine human interfaces . When
we look at Some of the products based on technologies with NLP ,we can
see that they are very advanced and useful too. We will be able to see
improved applications of NLP in the near future.

17. Thanks