We present a theoretical and empirical investigation of the statistical behaviour of the words in a text produced by human language. To this aim, we analyse the word distribution of various texts of Italian language selected from a specific literary corpus. We firstly generalise a theoretical framework elaborated by ourselves to identify 'quantum mechanical statistics' in large-size texts. Then, we show that, in all analysed texts, words distribute according to 'Bose--Einstein statistics' and show significant deviations from 'Maxwell--Boltzmann statistics'. Next, we introduce an effect of 'word randomization' which instead indicates that the difference between the two statistical models is not as pronounced as in the original cases. These results confirm the empirical patterns obtained in texts of English language and strongly indicate that identical words tend to 'clump together' as a consequence of their meaning, which can be explained as an effect of 'quantum entanglement' produced through a phenomenon of 'contextual updating'. More, word randomization can be seen as the linguistic-conceptual equivalent of an increase of temperature which destroys 'coherence' and makes classical statistics prevail over quantum statistics. Some insights into the origin of quantum statistics in physics are finally provided.

A lot of work has been done to address privacy issues Lyu et al. (2020); Anil et al. (2021); Dupuy et al. With the widespread application of differential privacy (2022); Li et al. (2021) to train language models using in text protection, however, the current text differential privacy (DP)Dwork et al. (2006), which is cleaning mechanism based on metric local differential considered the standard for privacy-preserving computing.

Paraphrasing is the task of expressing an essential idea or meaning in different words. But how different should the words be in order to be considered an acceptable paraphrase? And can we exclusively use automated metrics to evaluate the quality of a paraphrase? We attempt to answer these questions by conducting experiments on a German data set and performing automatic and expert linguistic evaluation.

Mind-reading technology can now transcribe people's thoughts in real-time based on the blood flow in their brain. A study put three people in MRI machines and got them to listen to stories. For the first time, researchers claim, they produced a rolling text of people's thoughts, and not just single words or sentences, without using a brain implant. The mind-reading technology did not exactly replicate the stories, but captured the main points. The breakthrough raises concerns about'mental privacy' as it could be the first step in being able to eavesdrop on others' thoughts.

In this Machine Learning tutorial we will learn about How to Handle Fake News with Machine Learning. In today's fast-paced digital world, spreading fake news has become a significant concern. With the increasing ease of access to social media platforms and other online sources of information, it has become more challenging to distinguish between real and fake news. In this project-based article, we will learn how to build a machine-learning model to detect fake news accurately. This article was published as a part of the Data Science Blogathon.

You're probably aware of AI's increasing ability to analyze and synthesize human language, such as the recent controversy over whether a Google chatbot is, in fact, sentient (Google claims -- and I'm inclined to believe -- that the chatbot is just very, very good at recognizing and replicating speech patterns). Since AI is so skilled at analyzing language, I wondered whether it could detect changes in language over time. Could it differentiate between texts written in, say, the 12th century and the 18th century? As it turns out, it can! To build this model, I used natural language processing, the branch of machine learning dedicated to (you guessed it!)

Deep Learning, particularly Natural Language Processing (NLP), has been gathering a huge interest nowadays. Some time ago, there was an NLP competition on Kaggle called Quora Question insincerity challenge. The competition is a text classification problem and it becomes easier to understand after working through the competition, as well as by going through the invaluable kernels put up by the Kaggle experts. First, let's start by explaining a little more about the text classification problem in the competition. Text classification is a common task in natural language processing, which transforms a sequence of a text of indefinite length into a category of text.

Day by day the number of machine learning models is increasing at a pace. With this increasing rate, it is hard for beginners to choose an effective model to perform Natural Language Understanding (NLU) and Natural Language Generation (NLG) mechanisms. Researchers across the globe are working around the clock to achieve more progress in artificial intelligence to build agile and intuitive sequence-to-sequence learning models. And in recent times transformers are one such model which gained more prominence in the field of machine learning to perform speech-to-text activities. The wide availability of other sequence-to-sequence learning models like RNNs, LSTMs, and GRU always raises a challenge for beginners when they think about transformers.

Before we go any further into text classification, we need a way to represent words numerically in a vocabulary. Because most of our ML models require numbers, not text. One way to achieve this goal is by using the one-hot encoding of word vectors, but this is not the right choice. Given the structure of one-hot encoded vectors, the similarity is always going to be 0 between different words. Word2Vec overcomes the above difficulties by providing us with a fixed-length (usually much smaller than the vocabulary size) vector representation of words.

There is a lot of text present around us. We see it in books, articles, comments, and newspapers. It would be really wise to use this text and convert it into a form that could be easily understood by machine learning and deep learning algorithms. As a result, they would take the processed text and give predictions for different use cases. Natural language processing (NLP) refers to converting natural text into a form that could be used for machine learning purposes.