How Does a Human Brain Process Received Information?

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that enables computers to understand human languages. It focuses on the interaction between the computer and the human and involves the development of algorithms that enable computers to tag, classify and interpret human language in a way that is valuable and meaningful.

How does a Human brain process information?

The human brain processes information through a complex network of specialised cells called Neurons. Neurons communicate with each other through electrical impulses and signals. When a human being receives a sensory input, such as seeing, hearing, or feeling something, their brain triggers a series of events and sends it to the sensory receptors. This latter distributes it between the different Neuron channels which will collaborate with each other to realise it. Let’s clarify this in more details. A human being decides to complete a task, e.g. to buy a pair of trainers. The brain takes this piece of information and segments it into a series of tasks and assigns each task to a channel of Neurons. Each Neuron collects the data it needs to complete its assignment and transmits the task to another Neuron. In the end, the brain collects and integrates completed tasks and transmits the information and coordinates a response.

The Goal

Sensory Input: information is received through human senses (sight, hearing, touch, taste, and smell).

Sensory Receptors: specialised cells in the sensory organs convert these stimuli into electrical signals.

Transmission of Signals: electrical signals travel along nerve fibres to specific areas of the brain.

Integration: the brain processes and integrates the information. Different parts of the brain handle different types of information (e.g., visual processing in the occipital lobe, auditory processing in the temporal lobe).

Pattern Recognition: the brain looks for patterns and associations to make sense of the information.

Memory and Learning: information is compared to existing knowledge stored in your memory, and new connections might be formed.

Decision Making: based on the processed information, the brain makes decisions and coordinates a response.

Feedback Loop: the brain continually receives feedback about the effectiveness of its response, which can lead to adjustments in future processing.

Pipelines refer to a series of processing steps applied to text data in order to perform a specific task. They are the building blocks that structure, organise and processes natural language texts in the same way a human brain does. They pipelines consist of six prime processes. Or steps: An intent That breaks the whole voice or text into small chunks, reconstructs it, analyzes, and processes it to bring us the most relevant data from the Search Engine Result Page.

The Steps that Help Computer to Understand Human Language

Natural language processing uses Language Processing Pipelines to read, pipelines Pipeline apply the human decipher and understand human languages. These pipelines consist of six prime processes. That breaks the whole voice or text into small chunks, reconstructs it, analyzes, and processes it to bring us the most relevant data from the Search Engine Result Page. The Steps that Help Computer to Understand Human Language:

Natural Language Processing Pipelines

When you call NLP on a text or voice, it converts the whole data into strings, and then the prime string undergoes multiple steps (the process called processing pipeline.) It uses trained pipelines to supervise your input data and reconstruct the whole string depending on voice tone or sentence length.

For each pipeline, the component returns to the main string. Then passes on to the next components. The capabilities and efficiencies depend upon the components, their models, and training.

NLP encompasses a wide range of tasks and applications, including:

Text Classification: This involves categorizing pieces of text into predefined categories. For example, classifying emails as spam or not spam, or sentiment analysis to determine if a piece of text expresses positive, negative, or neutral sentiment.

Named Entity Recognition (NER): This task involves identifying and classifying named entities in text into predefined categories, such as names of people, organizations, locations, dates, etc.

Machine Translation: This involves automatically translating text from one language to another. Services like Google Translate use NLP techniques.

Information Extraction: This involves extracting specific information or data from unstructured text. For example, extracting names, dates, and locations from news articles.

Question Answering Systems: These systems take a question in natural language and attempt to provide a relevant and accurate answer. Examples include chatbots and virtual assistants like Siri or Alexa.

Summarization: This involves condensing large bodies of text into shorter, coherent summaries while preserving the key information.

Speech Recognition: While not strictly a text-based NLP task, speech recognition involves converting spoken language into written text and is closely related to NLP.

Conversational Agents (Chatbots): These are systems designed to engage in natural language conversations with humans. They find applications in customer support, virtual assistants, and more.

NLP relies on a combination of linguistics, computer science, and machine learning techniques. It often involves the use of machine learning models, particularly deep learning models like recurrent neural networks (RNNs) and transformers, which are highly effective at processing sequential data like language.

The applications of NLP are vast and have a significant impact on various industries including healthcare, finance, customer service, marketing, and more. NLP is a rapidly evolving field with ongoing research to improve the capabilities and applications of language processing systems.Sentence Segmentation

When you have the paragraph(s) to approach, the best way to proceed is to go with one sentence at a time. It reduces the complexity and simplifies the process, even gets you the most accurate results. Computers never understand language the way humans do, but they can always do a lot if you approach them in the right way.

For example, consider the above paragraph. Then, your next step would be breaking the paragraph into single sentences.

When you have the paragraph(s) to approach, the best way to proceed is to go with one sentence at a time.

It reduces the complexity and simplifies the process, even gets you the most accurate results.

Computers never understand language the way humans do, but they can always do a lot if you approach them in the right way.

# Import the nltk library for NLP processes

import nltk

# Variable that stores the whole paragraph

text = “…”

# Tokenize paragraph into sentences

sentences = nltk.sent_tokenize(text)

# Print out sentences

for sentence in sentences:

print(sentence)

When you have paragraph(s) to approach, the best way to proceed is to go with one sentence at a time.

It reduces the complexity and simplifies the process, even gets you the most accurate results.

Computers never understand language the way humans do, but they can always do a lot if you approach them in the right way.

Word Tokenization

Tokenization is the process of breaking a phrase, sentence, paragraph, or entire documents into the smallest unit, such as individual words or terms. And each of these small units is known as tokens.

These tokens could be words, numbers, or punctuation marks. Based on the word’s boundary – ending point of the word. Or the beginning of the next word. It is also the first step for stemming and lemmatization.

This process is crucial because the meaning of the word gets easily interpreted through analyzing the words present in the text.

Let’s take an example:

That dog is a husky breed.

When you tokenize the whole sentence, the answer you get is [‘That’, ‘dog’, ‘is’, a, ‘husky’, ‘breed’].

There are numerous ways you can do this, but we can use this tokenized form to:

Count the number of words in the sentence.

Also, you can measure the frequency of the repeated words.

Natural Language Toolkit (NLTK) is a Python library for symbolic and statistical NLP.

Output:

[‘That dog is a husky breed.’, ‘They are intelligent and independent.’]

Parts of Speech Parssing

In a part of the speech, we have to consider each token. And then, try to figure out different parts of the speech – whether the tokens belong to nouns, pronouns, verbs, adjectives, and so on.

All these help to know which sentence we all are talking about.

Let’s knock out some quick vocabulary:

Corpus: Body of text, singular. Corpora are the plural of this.

Lexicon: Words and their meanings.

Token: Each “entity” that is a part of whatever was split up based on rules.

Output:

[(‘Everything’, ‘NN’), (‘is’, ‘VBZ’),

(‘all’, ‘DT’),(‘about’, ‘IN’),

(‘money’, ‘NN’), (‘.’, ‘.’)]

Lemmatization

English is also one of the languages where we can use various forms of base words. When working on the computer, it can understand that these words are used for the same concepts when there are multiple words in the sentences having the same base words. The process is what we call lemmatization in NLP.

It goes to the root level to find out the base form of all the available words. They have ordinary rules to handle the words, and most of us are unaware of them.

Stop Words

When you finish the lemmatization, the next step is to identify each word in the sentence. English has a lot of filler words that don’t add any meaning but weakens the sentence. It’s always better to omit them because they appear more frequently in the sentence.

Most data scientists remove these words before running into further analysis. The basic algorithms to identify the stop words by checking a list of known stop words as there is no standard rule for stop words.

One example that will help you understand identifying stop words better is:

Output:

Tokenize Texts With Stop Words:

[‘Oh’, ‘man’,’,’ ‘this’, ‘is’, ‘pretty’, ‘cool’, ‘.’, ‘We’, ‘will’, ‘do’, ‘more’, ‘such’, ’things’, ‘.’]

Tokenize Texts Without Stop Words:

[‘Oh’, ‘man’, ’,’ ‘pretty’, ‘cool’, ‘.’, ‘We’, ’things’, ‘.’]

Dependency Parsing

Parsing is divided into three prime categories further. And each class is different from the others. They are part of speech tagging, dependency parsing, and constituency phrasing.

The Part-Of-Speech (POS) is mainly for assigning different labels. It is what we call POS tags. These tags say about part of the speech of the words in a sentence. Whereas the dependency phrasing case: analyzes the grammatical structure of the sentence. Based on the dependencies in the words of the sentences.

Whereas in constituency parsing: the sentence breakdown into sub-phrases. And these belong to a specific category like noun phrase (NP) and verb phrase (VP).

Final Thoughts

In this blog, you learned briefly about how NLP pipelines help computers understand human languages using various NLP processes.

Starting from NLP, what are language processing pipelines, how NLP makes communication easier between humans? And six insiders involved in NLP Pipelines.

The six steps involved in NLP pipelines are – sentence segmentation, word tokenization, part of speech for each token. Text lemmatization, identifying stop words, and dependency parsing.

Bio: Ram Tavva is Senior Data Scientist, Director at ExcelR Solutions.

Related:

6 NLP Techniques Every Data Scientist Should Know

Using NLP to improve your Resume

Hugging Face Transformers Package – What Is It and How To Use It

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Natural Language Processing Key Terms, Explained

N-gram Language Modeling in Natural Language Processing

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How to Start Using Natural Language Processing With PyTorchParts of speech (POS) tagging is the  process of assigning a word in a text as  corresponding to a part of speech based on its  definition and its relationship with adjacent and  related words in a phrase, sentence, or paragraph. POS tagging falls into two distinctive groups: rulebased and stochastic. In this paper, a rule-based POS tagger is developed for the English language using Lex and Yacc. The tagger utilizes a small set of simple rules along with a small dictionary to generate sequences of tokens

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that enables computers to understand human languages. It focuses on the interaction between the computer and the human and involves the development of algorithms that enable computers to tag, classify and interpret human language in a way that is valuable and meaningful.

How does a Human brain process information?

The human brain processes information through a complex network of specialised cells called Neurons. Neurons communicate with each other through electrical impulses and signals. When a human being receives a sensory input, such as seeing, hearing, or feeling something, their brain triggers a series of events and sends it to the sensory receptors. This latter distributes it between the different Neuron channels which will collaborate with each other to realise it. Let’s clarify this in more details. A human being decides to complete a task, e.g. to buy a pair of trainers. The brain takes this piece of information and segments it into a series of tasks and assigns each task to a channel of Neurons. Each Neuron collects the data it needs to complete its assignment and transmits the task to another Neuron. In the end, the brain collects and integrates completed tasks and transmits the information and coordinates a response.

The Goal

Sensory Input: information is received through human senses (sight, hearing, touch, taste, and smell).

Sensory Receptors: specialised cells in the sensory organs convert these stimuli into electrical signals.

Transmission of Signals: electrical signals travel along nerve fibres to specific areas of the brain.

Integration: the brain processes and integrates the information. Different parts of the brain handle different types of information (e.g., visual processing in the occipital lobe, auditory processing in the temporal lobe).

Pattern Recognition: the brain looks for patterns and associations to make sense of the information.

Memory and Learning: information is compared to existing knowledge stored in your memory, and new connections might be formed.

Decision Making: based on the processed information, the brain makes decisions and coordinates a response.

Feedback Loop: the brain continually receives feedback about the effectiveness of its response, which can lead to adjustments in future processing.

Pipelines refer to a series of processing steps applied to text data in order to perform a specific task. They are the building blocks that structure, organise and processes natural language texts in the same way a human brain does. They pipelines consist of six prime processes. Or steps: An intent That breaks the whole voice or text into small chunks, reconstructs it, analyzes, and processes it to bring us the most relevant data from the Search Engine Result Page.

The Steps that Help Computer to Understand Human Language

Natural language processing uses Language Processing Pipelines to read, pipelines Pipeline apply the human decipher and understand human languages. These pipelines consist of six prime processes. That breaks the whole voice or text into small chunks, reconstructs it, analyzes, and processes it to bring us the most relevant data from the Search Engine Result Page. The Steps that Help Computer to Understand Human Language:

Natural Language Processing Pipelines

When you call NLP on a text or voice, it converts the whole data into strings, and then the prime string undergoes multiple steps (the process called processing pipeline.) It uses trained pipelines to supervise your input data and reconstruct the whole string depending on voice tone or sentence length.

For each pipeline, the component returns to the main string. Then passes on to the next components. The capabilities and efficiencies depend upon the components, their models, and training.

NLP encompasses a wide range of tasks and applications, including:

Text Classification: This involves categorizing pieces of text into predefined categories. For example, classifying emails as spam or not spam, or sentiment analysis to determine if a piece of text expresses positive, negative, or neutral sentiment.

Named Entity Recognition (NER): This task involves identifying and classifying named entities in text into predefined categories, such as names of people, organizations, locations, dates, etc.

Machine Translation: This involves automatically translating text from one language to another. Services like Google Translate use NLP techniques.

Information Extraction: This involves extracting specific information or data from unstructured text. For example, extracting names, dates, and locations from news articles.

Question Answering Systems: These systems take a question in natural language and attempt to provide a relevant and accurate answer. Examples include chatbots and virtual assistants like Siri or Alexa.

Summarization: This involves condensing large bodies of text into shorter, coherent summaries while preserving the key information.

Speech Recognition: While not strictly a text-based NLP task, speech recognition involves converting spoken language into written text and is closely related to NLP.

Conversational Agents (Chatbots): These are systems designed to engage in natural language conversations with humans. They find applications in customer support, virtual assistants, and more.

NLP relies on a combination of linguistics, computer science, and machine learning techniques. It often involves the use of machine learning models, particularly deep learning models like recurrent neural networks (RNNs) and transformers, which are highly effective at processing sequential data like language.

The applications of NLP are vast and have a significant impact on various industries including healthcare, finance, customer service, marketing, and more. NLP is a rapidly evolving field with ongoing research to improve the capabilities and applications of language processing systems.Sentence Segmentation

When you have the paragraph(s) to approach, the best way to proceed is to go with one sentence at a time. It reduces the complexity and simplifies the process, even gets you the most accurate results. Computers never understand language the way humans do, but they can always do a lot if you approach them in the right way.

For example, consider the above paragraph. Then, your next step would be breaking the paragraph into single sentences.

When you have the paragraph(s) to approach, the best way to proceed is to go with one sentence at a time.

It reduces the complexity and simplifies the process, even gets you the most accurate results.

Computers never understand language the way humans do, but they can always do a lot if you approach them in the right way.

# Import the nltk library for NLP processes

import nltk

# Variable that stores the whole paragraph

text = “…”

# Tokenize paragraph into sentences

sentences = nltk.sent_tokenize(text)

# Print out sentences

for sentence in sentences:

print(sentence)

When you have paragraph(s) to approach, the best way to proceed is to go with one sentence at a time.

It reduces the complexity and simplifies the process, even gets you the most accurate results.

Computers never understand language the way humans do, but they can always do a lot if you approach them in the right way.

Word Tokenization

Tokenization is the process of breaking a phrase, sentence, paragraph, or entire documents into the smallest unit, such as individual words or terms. And each of these small units is known as tokens.

These tokens could be words, numbers, or punctuation marks. Based on the word’s boundary – ending point of the word. Or the beginning of the next word. It is also the first step for stemming and lemmatization.

This process is crucial because the meaning of the word gets easily interpreted through analyzing the words present in the text.

Let’s take an example:

That dog is a husky breed.

When you tokenize the whole sentence, the answer you get is [‘That’, ‘dog’, ‘is’, a, ‘husky’, ‘breed’].

There are numerous ways you can do this, but we can use this tokenized form to:

Count the number of words in the sentence.

Also, you can measure the frequency of the repeated words.

Natural Language Toolkit (NLTK) is a Python library for symbolic and statistical NLP.

Output:

[‘That dog is a husky breed.’, ‘They are intelligent and independent.’]

Parts of Speech Parssing

In a part of the speech, we have to consider each token. And then, try to figure out different parts of the speech – whether the tokens belong to nouns, pronouns, verbs, adjectives, and so on.

All these help to know which sentence we all are talking about.

Let’s knock out some quick vocabulary:

Corpus: Body of text, singular. Corpora are the plural of this.

Lexicon: Words and their meanings.

Token: Each “entity” that is a part of whatever was split up based on rules.

Output:

[(‘Everything’, ‘NN’), (‘is’, ‘VBZ’),

(‘all’, ‘DT’),(‘about’, ‘IN’),

(‘money’, ‘NN’), (‘.’, ‘.’)]

Lemmatization

English is also one of the languages where we can use various forms of base words. When working on the computer, it can understand that these words are used for the same concepts when there are multiple words in the sentences having the same base words. The process is what we call lemmatization in NLP.

It goes to the root level to find out the base form of all the available words. They have ordinary rules to handle the words, and most of us are unaware of them.

Stop Words

When you finish the lemmatization, the next step is to identify each word in the sentence. English has a lot of filler words that don’t add any meaning but weakens the sentence. It’s always better to omit them because they appear more frequently in the sentence.

Most data scientists remove these words before running into further analysis. The basic algorithms to identify the stop words by checking a list of known stop words as there is no standard rule for stop words.

One example that will help you understand identifying stop words better is:

Output:

Tokenize Texts With Stop Words:

[‘Oh’, ‘man’,’,’ ‘this’, ‘is’, ‘pretty’, ‘cool’, ‘.’, ‘We’, ‘will’, ‘do’, ‘more’, ‘such’, ’things’, ‘.’]

Tokenize Texts Without Stop Words:

[‘Oh’, ‘man’, ’,’ ‘pretty’, ‘cool’, ‘.’, ‘We’, ’things’, ‘.’]

Dependency Parsing

Parsing is divided into three prime categories further. And each class is different from the others. They are part of speech tagging, dependency parsing, and constituency phrasing.

The Part-Of-Speech (POS) is mainly for assigning different labels. It is what we call POS tags. These tags say about part of the speech of the words in a sentence. Whereas the dependency phrasing case: analyzes the grammatical structure of the sentence. Based on the dependencies in the words of the sentences.

Whereas in constituency parsing: the sentence breakdown into sub-phrases. And these belong to a specific category like noun phrase (NP) and verb phrase (VP).

Final Thoughts

In this blog, you learned briefly about how NLP pipelines help computers understand human languages using various NLP processes.

Starting from NLP, what are language processing pipelines, how NLP makes communication easier between humans? And six insiders involved in NLP Pipelines.

The six steps involved in NLP pipelines are – sentence segmentation, word tokenization, part of speech for each token. Text lemmatization, identifying stop words, and dependency parsing.

Bio: Ram Tavva is Senior Data Scientist, Director at ExcelR Solutions.

Related:

6 NLP Techniques Every Data Scientist Should Know

Using NLP to improve your Resume

Hugging Face Transformers Package – What Is It and How To Use It

FacebookTwitterLinkedInRedditEmailShare

More On This Topic

Natural Language Processing Key Terms, Explained

N-gram Language Modeling in Natural Language Processing

Natural Language Processing with spaCy

Applying Natural Language Processing in Healthcare

Linear Algebra for Natural Language Processing

How to Start Using Natural Language Processing With PyTorchParts of speech (POS) tagging is the  process of assigning a word in a text as  corresponding to a part of speech based on its  definition and its relationship with adjacent and  related words in a phrase, sentence, or paragraph. POS tagging falls into two distinctive groups: rulebased and stochastic. In this paper, a rule-based POS tagger is developed for the English language using Lex and Yacc. The tagger utilizes a small set of simple rules along with a small dictionary to generate sequences of tokens

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