8 months ago
Dec 19, 2021

NLP Natural Language Processing - The Way Into The Future!

As artificial intelligence technology advances, natural language processing (NLP) does. This area of AI deals with the ability of computers to understand human language and respond humanly.Natural language understanding is important for many applications, such as dialog


As artificial intelligence technology advances, natural language processing (NLP) does.

This area of AI deals with the ability of computers to understand human language and respond humanly.

Natural language understanding is important for many applications, such as dialogue systems, machine translation, text summarization, and question answering.

Natural Language Processing Definition:

What is Natural language processing? NLP natural language processing is a field of artificial intelligence (AI) concerned with understanding human language.

We can see rapid advancements in NLP natural language processing in recent years. Especially with products such as Apple’s Siri, Amazon’s Alexa, Google’s Now, Microsoft’s Cortana, and Facebook’s M performing language tasks.

NLP natural language processing is highly interdisciplinary, drawing on expertise from computational linguistics, formal semantics, speech recognition, machine learning, cognitive science, and artificial intelligence.

NLP Natural Language Processing Categories

The applications can be broadly divided into two categories:

1) Text processing:

Text processing of NLP includes tasks such as text classification, text chunking, named entity recognition, sentiment analysis, and machine translation; and

2) Speech processing:

Speech processing of NLP includes tasks such as speech recognition, speaker recognition, emotion recognition, and dialog systems.

NLP Natural Language Processing Examples:

Following are some examples of natural language processing tasks:

1. Text processing:

As mentioned above, it is classifying text into different categories, such as news articles, product reviews, blog posts etc.

2. Chunking:

Breaking up text into smaller fragments, such as sentences and phrases.

3. Named entity recognition:

Identifying the names of people, places, organizations, products, etc. in text.

4. Sentiment analysis:

Assessing the sentiment of text, such as whether a test is positive, negative, or neutral.

5. Speech processing:

Recognizing words, phonemes, and sentences in speech.

Is Natural Language Processing Part of Machine Learning?

Many people consider natural language processing to be a subfield of machine learning, as both involve teaching computers how to learn from data.

So, is natural language processing part of machine learning?

NLP natural language processing focuses more on sentence structure than it does on prediction and classification.

Natural language generation, however, is a subfield of machine learning, which deals with the generation of text from data.

Natural Language Processing Techniques:

Following are some NLP natural language processing techniques:

1. Machine learning:

The most common approach to natural language processing is deep learning. Other common machine learning approaches include rule-based systems, probabilistic modeling, and decision trees.

2. Statistical methods:

A lot of information extraction tasks make use of statistical models to extract contextual information from text or speech. These models are usually hand-engineered. Using natural language generation for review writing requires the construction of a model that can generate coherent paragraphs composed of sentences describing product features (like battery life, screen size, etc.). This task draws on latent Dirichlet allocation (LDA), which extracts topics from documents.

3. Clustering algorithms:

Part of speech tagging uses unsupervised clustering to group words into categories such as “noun,” “verb,” and “adjective.”

4. Cognitive science:

Researchers in artificial intelligence and cognitive science also contribute to the field of natural language processing. These include linguists who analyze how language can be modeled using formal rules, which gives rise to finite-state automata. Other notable researchers include psychologists, neuroscientists, and philosophers who look at how language is processed by the mind, giving rise to computational models of human memory

Best Tools for NLP Natural Language Processing:

There are many different tools and libraries for natural language processing, but the following are some of the most popular:

1. Text processing:

Python’s Natural Language Toolkit (NLTK) is a popular toolkit for text processing, which includes many functions for analyzing, tokenizing, and parsing text.

2. Java’s Stanford Core:

NLP is also a popular toolkit for text processing, with features such as part-of-speech tagging, chunking, and named entity recognition.

3. Speech processing:

Dragon Dictate is a popular tool for speech recognition, which can convert spoken words into text.

4. Wit.ai:

It is a popular platform for building AI applications, which includes natural language processing capabilities.

5. Siri SDK:

Siri SDK is the development kit for Apple’s Siri voice assistant, which allows developers to create apps that use Siri’s natural language processing abilities.

6. Amazon Lex:

It is a service that allows developers to integrate conversational interfaces into any app by leveraging verbal messages.

7. Cortana Analytics Suite:

It offers natural language processing, image recognition, and geospatial services, which can be used to build custom AI applications.

Natural Language Processing Best Books

Following are some books on natural language processing:

1. Speech and Language Processing:

Speech and language Proccessing is a comprehensive book covering almost all aspects of natural language processing.

2. Foundations of Statistical Natural Language Processing:

This book presents techniques and algorithms for understanding, modeling, and generating human languages. It describes state-of-the-art methodologies for building statistical models to automatically recognize words and parse sentence structures. 

3. Computational Linguistics – An Introduction:

This book covers research in computational linguistics where literary studies meet artificial intelligence. Researchers from both fields have been exploring ways to make use of computer science tools in literary text analyses. This includes machine learning for natural languages processing tasks such as sentiment classification and authorship attribution.

Natural Language Processing with Python

Python is a popular language for natural language processing, mainly because of its easy-to-use libraries such as NLTK. Other libraries include SpaCy, which is popular for text classification, sentiment analysis and named entity recognition.

Other popular NLP tasks that can be performed with python include:

– Word tokenization

– Stop word removal

– Lemmatization

– Part of speech tagging

– Chunking

– Parsing

– Semantic analysis

– Text summarization

– Opinion mining and sentiment analysis.

There is a whole pool of virtual resources that can help you get started with NLP in python. For example, the Natural Language Processing with Python tutorial on Codecademy provides a good introduction to the basics of NLP using python. 

What is more, there are several libraries for text classification, sentiment analysis, and named entity recognition. For example, the TextBlob library provides a simple interface for text processing, while the Scikit-Learn library includes a variety of machine learning algorithms for text classification and sentiment analysis.





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