Global Communication With Machine Translation - A Comprehensive Review of Text and Voice
Introduction
In Chapter I we studied that natural language processing (NLP) is an area of artificial intelligence (AI) that:
Focuses on enabling computers to understand, interpret, and generate human language.
NLP involves the development of algorithms and models that can process and analyze natural language data, such as written text, speech, and even gestures.
NLP encompasses a wide variety of applications, including:
Machine translation.
Sentiment analysis.
Chatbots and virtual assistants.
Text summary and language modeling.
In this chapter, we will focus on machine translation.
Machine translation.
Machine translation in natural language processing (NLP) is the process of automatically translating text or speech from one language to another using computer algorithms:
It is a subfield of computational linguistics and artificial intelligence whose goal is to facilitate communication between people who speak different languages.
It consists of developing algorithms and models capable of analyzing and understanding input texts in one language and generating output texts in another.
It involves not only linguistic analysis and understanding but also the ability to capture the nuances and complexities of human language accurately.
Machine translation (MT) can be classified according to a number of factors, such as:
MT by input type (text or speech).
MT by the direction of translation (for example, from English to French or from French to English).
MT by granularity (word, phrase, or sentence).
Machine text or voice translation
Automatic text translation consists of using software algorithms to translate written text from one language to another without human intervention.
Speech machine translation, on the other hand, uses software algorithms to automatically translate spoken language from one language to another, without human intervention. Algorithms used:
Text Classification:
Text classification in NLP refers to the process of assigning labels or categories to a given text. In text classification, a machine learning model is trained on a dataset of previously labeled text and then used to predict the category or label of a new unlabeled text.
Text classification is one of the most common NLP tasks and is used in a variety of applications:
News classification:
Classification of customer comments.
Classification of legal documents.
Classification of opinions in social networks.
Example. How to classify two documents:
Doc. 1. Cepal:
The coronavirus disease pandemic (COVID-19) has caused an unprecedented crisis worldwide. In the field of education, this emergency has led to the massive closure of face-to-face activities in educational institutions in the Americas.
Doc. 2. OPS:
Due to COVID-19 infections in the Americas, the director of the Pan American Health Organization (PAHO) called on countries to ensure the protection of healthcare workers.
Step 1.- extract and prepare the text:
To extract the text, we can use a technique called web scraping, there are many libraries to apply this tool, such as BeautifulSoup in Python.
Then we must preprocess the text, remove punctuation, transform it to lowercase, and remove frequent words used in the language such as prepositions, and articles.
Doc. 1. Cepal: (Pandemic disease pandemic coronavirus covid-19 educational emergency face-to-face activities educational institutions America)
Doc. 2. OPS: (Covid-19 infections America director Pan American Health Organization called on countries to ensure the protection of health care workers)
Step 2.- The text becomes a numerical representation:
Since algorithms are mathematical equations that understand numbers, we represent the text with numbers.
One of the simplest models is the so-called bag of words (BoW) or list of words that exist in the whole text.
Step 3.- Classify:
From the numerical representation we are ready to classify our documents by topics.
One technique is topic modeling. This is an unsupervised machine learning technique.
Another technique used is called Non-Negative Matrix Factorization or NMF, which consists of a matrix decomposition with the property that the matrices do not have negative elements.
Continuing with the example:
If a person does a search with the words "pandemic, disease, America", a search engine like Google will select document one.
Named Entity Recognition (NER):
Used to identify and classify entities in a text, such as names of people, organizations, places, dates, quantities, among others.
For example, in a text that talks about a person named "John Doe", NER could identify that "John Doe" is a person's name and label it as such. The website nlpcloud.com illustrates the following table:
People in blue.
Dates in red.
Events in green.
Organizations in black.
Sentiment analysis:
Sentiment analysis in NLP is a technique used to determine the emotional polarity (positive, negative, or neutral) of a text.
It uses machine learning algorithms to identify keywords in a text and evaluate its emotional tone.
It can be useful in a variety of contexts, such as analyzing product reviews, social media comments, customer feedback and user comments.
Sentiment analysis can be used to determine the public perception of a specific product, service, or topic.
The website scielo.cl illustrates the following diagram:
Voice recognition:
A technique that allows computers to interpret and process the human voice, it is achieved by transcribing the spoken voice into text, which allows computers to understand and analyze the language spoken by humans.
It uses audio signal processing algorithms and machine learning models, which can be trained to recognize patterns in the way people speak and produce words.
It is used in a variety of applications, such as transcribing meetings, generating video captions, controlling smart home devices using voice commands, and real-time translation of spoken languages.
According to planetachatbot.com, the activation method ensures that the AI responds when its name is spoken.
Text Summary:
A technique that allows computers to automatically summarize the content of a text or document. The objective of text summarization is to extract the essential information from the text and present it in a more concise form.
Algorithms are used to identify keywords, eliminating redundant information.
It is used in news summaries, executive summaries, and key points of legal contracts.
ITM_Colombia explains the extractive approaches of the algorithm based on the similarity matrix:
Theme modeling:
Refers to the task of identifying and extracting the main topics from a set of documents or a corpus of text, in order to more effectively classify, categorize or summarize the information contained therein.
It uses clustering techniques to identify the main topics covered in the documents and assign labels or keywords to them.
For example, if you have a set of articles on politics, thematic modeling might identify topics such as "technologies," "sports," "entertainment," or "human rights".
The aprendemachinelearning.com site illustrates topic modeling:
GPT chat and automatic text and voice translations
Do automatic translations between several languages, including Spanish to English and vice versa. If you have any specific text, you would like me to translate, feel free to provide it to me and I will do my best to provide an accurate translation.
Big Data Applications:
Sentiment Analysis: By translating social media posts, customer reviews and other forms of user-generated content, companies can better understand how their target audience perceives their products or services. This can help them identify areas for improvement and make data-driven decisions.
Multilingual chatbots: By integrating machine translation into chatbots, companies can offer multilingual customer support without the need for human translators. This can help them save costs and provide quick responses to their customers.
Data aggregation: With machine translation, it is easier to aggregate data from diverse sources in different languages. This can help companies collect and analyze large volumes of data from around the world, providing valuable insight into global trends and consumer behavior.
Localization: Machine translation can also help companies localize their content for different regions and languages. This can help them expand their global reach and access new markets.
Applications in the financial industry:
Multilingual customer service: With machine translation, financial institutions can offer multilingual customer service without the need for human translators. This can help them provide quick responses to their customers and improve customer satisfaction.
International Transactions: Machine translation can help financial institutions translate important documents, such as contracts and agreements, for international transactions. This can help them expand their global reach and do business with customers in different countries.
Compliance: Machine translation can also help financial institutions comply with regulations that require them to provide information in different languages. This can help them avoid legal problems and penalties.
Market Analysis: With machine translation, financial institutions can analyze news and other information from around the world, providing valuable insight into global financial trends and market behavior.
Fraud detection: Machine translation can help financial institutions detect and prevent fraud through real-time analysis of multilingual data, such as social media posts and customer feedback.
Applications in the real estate industry:
Multilingual Property Listings: With machine translation, real estate agents can list properties in multiple languages, making it easier to attract buyers from different parts of the world.
Multilingual Customer Service: Machine translation can help real estate agents provide multilingual customer service without the need for human translators. This can help them improve customer satisfaction and close deals faster.
Market Analysis: With machine translation, real estate agents can analyze market data from different regions and countries, providing valuable information on global trends and investment opportunities.
Property Management: Machine translation can help property managers communicate with tenants and landlords in different languages, making it easier to manage properties in multicultural communities.
Real Estate Investing: Machine translation can help investors analyze real estate investment information in different languages, enabling them to make informed decisions about potential opportunities around the world.
Closing remark:
Overall, machine translation can help various industries process and analyze large amounts of multilingual data, leading to better insights, improved customer satisfaction and more informed decisions.