Natural Language Processing in Python (NLP)
What is NLP?
Natural language processing (NLP) is about developing applications and services that are able to understand human languages. Some Practical examples of NLP are speech recognition for eg: google voice search, understanding what the content is about or sentiment analysis etc.
Benefits of NLP
- As all of you know, there are millions of gigabytes every day are generated by blogs, social websites, and web pages.
- There are many companies gathering all of these data for understanding users and their passions and give these reports to the companies to adjust their plans.
- Suppose a person loves traveling and is regularly searching for a holiday destination, the searches made by the user is used to provide him with relative advertisements by online hotel and flight booking apps.
- You comprehend what, web indexes are by all account not the only usage of common language preparing (NLP) and there are a ton of magnificent executions out there.
Challenges in NLP
- Tokenization (breaking text into smaller pieces)
- Stopword Removal (Filtering non-important words)
- N-Grams (Grouping related words)
- Parts-of-Speech (Identifying characteristics of a language)
- Stemming (Removing endings of words)
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NLP Implementations
These are some of the successful implementations of natural language processing (NLP):
- Search engines such as Google, Yahoo, etc. The Google search engine understands that you are a technology expert, so it shows you results related to you
- Social networking sites such as Facebook news. The news feed algorithm understands your interests through natural language processing and displays related ads and publications more likely than other publications
- Voice engines such as Apple Siri
- Spam filters such as Google spam filters. It is not just the usual spam filtering, now spam filters understand what is in the content of the email and see if it is spam or not.
What are the techniques used in NLP with Python?
Syntax
Syntax refers to the arrangement of words in a sentence so that they have grammatical meaning In NLP, the analysis is used to evaluate how natural language aligns with grammatical rules Computer algorithms are used to apply grammar rules to a group of words and extract meaning from them. Here are some syntax techniques that can be used:
- Lemmatization: this means reducing the various flexed forms of a word in a single way for easier analysis
- Morphological segmentation: it involves dividing words into individual units called morphemes
- Word segmentation: consists of dividing a large part of the continuous text into units different
- Marking on the part of the speech: it implies identifying the part of the speech of each word
- Analysis: it implies analyzing the given sentence
- Jumping sentence: it implies placing the limits of the sentence in a large text
- Derivation: it consists of cutting the flexed words In its root form.
Semantics
Semantics refers to the meaning transmitted by a text. Semantic analysis is one of the difficult aspects of natural language processing that has not yet been fully resolved. It involves the application of computer algorithms to understand the meaning and interpretation of words and how sentences are structured. These are some techniques in semantic analysis.
- Name Entity Recognition (NER): This involves determining the parts of the text that can be identified and classified into predefined groups. Examples of such groups include names of people and place names
- Disambiguation of the meaning of the word: it implies giving meaning to a word according to the context
- Natural language generation: it implies the use of databases to derive semantic intentions and convert them into human language
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Wrapping up
Natural language processing plays a fundamental role in supporting machine-human interactions. As more research is done in this field, we hope to see more discoveries that will make the machines smarter to recognize and understand human language
NLTK (Natural Language Toolkit) in Python
| NLP Library | Description |
| NLTK | This is one of the most useful NLP libraries and mothers of all. |
| spaCy | This is a fully optimized and highly accurate library, widely used for deep learning. |
| PyNLPl | PyNLPI was also known as ‘Pineapple’ and is compatible with Python. It provides an analyzer for many data formats such as FoLiA / Giza / Moses / ARPA / Timbl / CQL. |
| Stanford CoreNLP Python | For the client-server architecture, this is a good library in NLTK. This is written in JAVA, but it provides modularity for use in Python. |
| TextBlob | This is an NLP library that works in Pyhton2 and python3. This is used for textual data processing and mainly provides all kinds of operations in the form of an API. |