NLP

Natural Language Processing (NLP) is a branch of artificial intelligence that allows computers to interact with human language. This involves understanding and composing both written text (for example, a book, a tweet, or a website) and spoken language (for example, a telephone conversation or a podcast).

One of the main goals of NLP is to enable a computer to understand the complexity of language. Human language involves many complexities such as grammatical rules, slang, local idioms, dependence of meaning on context, and constant changes in language. NLP uses a variety of techniques and algorithms to understand and process these complexities.

NLP has many different applications. Among them:

Text analysis: This is used to analyze documents or other text. For example, a company can analyze customer reviews and see which words are frequently used in those comments to determine overall customer satisfaction.

Language translation: NLP is used to translate text from one language into another language. Google Translate is an example of this.

Speech recognition: NLP is used to convert speech into text. This is important for applications such as voice assistants (e.g. Siri or Alexa) or voice typing programs.

Sentiment analysis: This is used to determine the overall emotional tone in a text. For example, a company can analyze what is being said about their brand on Twitter and determine whether those comments are generally positive or negative.

Chatbots and virtual assistants: NLP enables a chatbot or virtual assistant to understand human language and generate responses in natural language.

These and other applications of NLP enable computers to better understand human language and use it more effectively. This allows computers and humans to communicate more naturally and effectively.

Sparse Matrix (Intuitive Matrix): A sparse matrix is a matrix with a majority of zeros. Such matrices often appear in large data sets and especially in areas such as natural language processing. Efficient storage and processing of sparse matrices is important to save memory and computational resources. Because storing zero values is generally unnecessary and calculations made on these values usually do not change the result.

Spelling Marks: Spelling marks are symbols used to determine sentence structure and meaning in written language. Signs such as periods, commas, exclamation marks, question marks, apostrophes, semicolons, and colons fall into this category. Spelling marks often play an important role in natural language processing (NLP) studies. Because these signs can determine the meaning and tone of the sentence. But sometimes, especially when cleaning or preprocessing text data, these marks are removed or replaced.

Preprocessing of orthographic marks

In Natural Language Processing (NLP) projects, data often goes through a series of pre-processing steps. These steps aim to make the data more suitable for analysis or modelling. Preprocessing of orthographic marks is usually one of these steps, and there are generally two main approaches:

Removing Spelling Marks: This approach is often used in tasks such as text classification and sentiment analysis. Here, spellings usually do not affect the meaning much and can sometimes degrade the model's performance. In Python, this is usually done with the "punctuation" property and "translate" method of the "string" module. Here is an example:
import string

text = "Hello, how are you? I'm fine!"
text = text.translate(str.maketrans('', '', string.punctuation))
This code removes all spelling marks from the text.

Using Spellmarks as Tokens: This approach is often used to understand and render text (for example, a chatbot or text rendering model). Here, spellings are important because they determine the structure and tone of the sentence. In this case, spellings are generally considered a token in their own right. This is usually done using a tokenization tool (e.g. NLTK, Spacy).
Which approach to use depends on the requirements of a particular task and the nature of the data.

Big and small letter (case normalization)

In Natural Language Processing (NLP) projects, case normalization is often performed when processing text data. This means converting all text to lowercase. This step allows the model to recognize different spellings of the same word (e.g. "Hello", "HELLO", "hello") as the same word.

In Python, you can use the lower() function to convert a string to lowercase. Here is an example:

text = "Hello, How are you?"
text = text.lower()
print(text)

When you run this code, the output is "hello, how are you?" It will happen.

In some cases, preserving capital letters may be important - for example, in cases such as names or abbreviations. But generally, for NLP tasks such as text classification or sentiment analysis, it is best practice to convert all text to lowercase. This makes the model more general and flexible.

Stop Words

Stop Words are the most frequently used words in a language. Generally, these words contribute little to the overall meaning of a text and are therefore often omitted in text processing and Natural Language Processing (NLP) tasks. Examples of stop words in English include words such as "the", "is", "at", "which", and "on".

Removing stop words makes the data more manageable and helps identify important words. This is especially useful in NLP tasks such as text classification, keyword extraction, and sentiment analysis.

In Python, the NLTK (Natural Language Toolkit) library provides a list of stop words for a number of languages. Here is an example:

from nltk.corpus import stopwords

stop_words = set(stopwords.words('english'))

text = "This is a sample sentence."
text_tokens = text.split()

filtered_text = [word for word in text_tokens if word not in stop_words]

print(filtered_text)

This code extracts stop words from the text and returns a list of words that are not stop words.

Stemmer

Stemming is a widely used technique in the field of Natural Language Processing (NLP). This technique aims to find the root or root form of a word. For example, the roots of the words "running", "runs" and "ran" are "run".

Stemming is often used in NLP tasks such as text classification, sentiment analysis and similar. This allows the model to recognize different words with the same root as the same word.

In Python, the NLTK (Natural Language Toolkit) library includes popular stemming algorithms such as Porter and Lancaster. Here is an example:

from nltk.stem import PorterStemmer

stemmer = PorterStemmer()

words = ["program", "programs", "programer", "programing", "programers"]

stemmed_words = [stemmer.stem(word) for word in words]

print(stemmed_words)

This code finds the root of each word and returns a list of its root forms.

One disadvantage of stemming is that it can sometimes produce stems that are not real words. For example, the root of the word "running" may be "run", while the root of the word "argument" may be "argu". In this case, another technique called lemmatization may produce better results. Lemmatization finds the root form of a real word using grammatical analysis of the word.

CountVectorizer

CountVectorizer is a widely used technique in text mining and natural language processing (NLP) tasks. This technique converts a text document or a collection of text documents (a corpus) into a word count matrix. Each row represents a document and each column represents a word in the document. The value in each cell represents the frequency of a particular word in a particular document.

CountVectorizer is used specifically for NLP tasks such as text classification and clustering. This allows the model to understand text in a numerical format, since machine learning models generally cannot process text directly.

In Python, the scikit-learn library provides the CountVectorizer class. Here is an example:

from sklearn.feature_extraction.text import CountVectorizer

corpus = [
     'This is the first document.',
     'This document is the second document.',
     'And this is the third one.',
     'Is this the first document?',
]

vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus)

print(vectorizer.get_feature_names())
print(X.toarray())

This code creates the word count vector of each document and prints the frequency of each word.