Natural language processing (NLP) helps to understand human languages and develop applications. A few applications that implement NLP is Google voice search, which can help one to understand the content. Blogs, social media platforms, and web sites generate a mass amount of gigabytes everyday. Applications are developed to collect of all the generated data and analyze to adjust a future plan to accommodate users.
Natural language processing consists of five essential components; discourse integration, morphological and lexical analysis, pragmatic analysis, semantic analysis, and syntactic analysis. In addition, NLP has three types of systems which include alphabetic, logographic, and syllabic. The implementation of NLP consists of machine learning and statistical interface.
NLP and Advertisements
Do you love to travel? Or frequent great restaurants to socialize? Most likely you search your local area for the restaurants or the best cities to beaches for vacations. When you search for the criteria, advertisements are placed on those web pages. Search engines may not be the biggest implementer of NLP, but the search engines do play a big role in the development and use of natural language processing.
In the technology and business sector, there are many successful implementations of natural language processing that involve different techniques and concepts:
- Search Engines – Search engines such as Google, Yahoo, and Bing understand your interests and provide advertised related results to you.
- Social Media – Social Media platforms such as Facebook provides natural language processing to understand your interests. This is a news feed algorithm using natural language processing to provide related ads based on your interests.
- Speech Engines – Speech engines like Apple Siri that can understand what you say and prompt responses from your speech.
- Spam Filters – Spam Filters are used within Google to interpret the email content and understand what if the content is spam or legit a legit email.
- Provides the answers to questions with correctness in the natural language.
- Provides information that is necessary and to the point of the question.
- When questions are asked with information that is relevant, NLP accuracy of answers are increased.
- Answers are provided without bias to any human and are solely based on facts of the provided information.
- Produces a summary of text such as reviews, news articles, or books.
- When given text, determines which words refer to the same object.
- When given text, NLP is able to determine the tone of the writing and this is major factor in business understand. A business is able to understand by the tone of the customer in product reviews via media or product review sites.
- NLP may nto understand questions that are written in proper form and/or ambiguous.
- A proper NLP system is built to perform a single task with limited functional ability.
- Users are not able to interact with the interface and there’s no system interaction.
Python Libraries Used for NLP
Scikit-learn library is a software machine learning for Python.In addition, the Scikit-learn library provides a series of supervised and unsupervised algorithms with a Python interface and is distributed globally in academia and business use.
- IPython: Interactive console
- NumPy: n-dimensional array package
- Matplotlib: 2D and 3D plotting
- Pandas: Data analysis and structures
- SciPy: Scientific computing library
- Sympy: Symbolic mathematics
Modules for SciPy are also known as SciKits and the modules provide as a great resource for learning algorithms. Meaning there’s a focus on the quality of modules, documentation, and performance for easy use in production systems. The vision for the library is a level of robustness and support required for use in production systems. This means a deep focus on concerns such as easy of use, code quality, collaboration, documentation and performance.
The nltk Python library is the acronym for the Natural Language Toolkit in Python and is a very powerful toolkit for NLP in Python. The nltk library provided many packages in machine learning to understand the human language and learning to respond appropriately. Some of these packages include character count, lemmatization, punctuation, stemming, tokenization, and much more.