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TechRadar spoke to Dr Matthew Voice, an Assistant Professor in Applied Linguistics at the UK's University of Warwick, to find out the science behind the struggle to deduce Wordle Puzzle #256. "[In your live blog] you've already talked about _ATCH as a kind of trap. This is an example of an n-gram, i.e. a group of letters of a length (n) that commonly cluster together. So this is an n-gram with a length of four letters: a quadrigram," Professor Voice tells us. "Using [this] Project Gutenberg data, it's interesting to note that _ATCH isn't listed as one of the most common quadrigrams in English overall, but the [same] data considers words of all lengths, rather than just the five letters Wordle is limited to. I don't know of any corpus exclusively composed of common 5 letter words, but it might be the case that _ATCH happens to be particularly productive for that length." "The other thing to mention," Professor Voice adds, "would be that the quadrigram _ATCH is made up of smaller n-grams, like the bigram AT, which is extremely common in English. So we're seeing a lot of common building blocks in one word, which means that sorting individual letters might not be narrowing down people's guesses as much as it would with other words."

Article talks about teenage girls' linguistic innovation. While some people may decry "creaky voice"/vocal fry, uptalk, and the use of "like" as a discourse particle, the author argues that "Language is inherently unstable. It's in a constant state of flux, made and remade-stretched, altered, broken down and rearranged-by its speakers every day. Rather than a sign of corruption and disorder, this is language in its full vitality-a living, evolving organism... When it comes to language, the rules of natural selection apply: Evolve or perish."

Lingua Franca, a blog from The Chronicle of Higher Education, discusses various issues related to language and applied linguistics, including poetry, style, grammar, dialects, words.

This webpage, associated with a 2005 PBS 3-hour program of the same name, addresses several Words R Us Related issues, including African American English, perspectives on written & spoken English, regional dialects, Spanish & Chicano English, communicative choices & linguistic style, prescriptionist vs. descriptionist philosophies towards language, etymology, and slang. It also has hyperlinks to various credible academic sources for applied linguistics.

A new study conducted by the University of Pennsylvania has shown that children as young as two understand basic grammar rules when they first learn to speak and are not simply imitating adults. The study also applied the same statistical analysis on data from one of the most famous animal language-acquisition experiments -- Project Nim -- and showed that Nim Chimpsky, a chimpanzee who was taught sign language over the course of many years, never grasped rules like those in a two-year-old's grammar. "When you compare what children should say if they follow grammar against what children do say, you find it to almost indistinguishable," Professor of Linguistics Charles Yang said. "If you simulate the expected diversity when a child is only repeating what adults say, it produces a diversity much lower than what children actually say." As a comparison, Yang applied the same predictive models to the set of Nim Chimpsky's signed phrases, the only data set of spontaneous animal language usage publicly available. He found further evidence for what many scientists, including Nim's own trainers, have contended about Nim: that the sequences of signs Nim put together did not follow from rules like those in human language. Nim's signs show significantly lower diversity than what is expected under a systematic grammar and were similar to the level expected with memorization. This suggests that true language learning is -- so far -- a uniquely human trait, and that it is present very early in development.

Just a few decades ago, many linguists thought the human brain had evolved a special module for language . It seemed plausible that our brains have some unique structure or system. After all, no animal can use language the way people can. However, in the 1990s, scientists began testing the language-module theory using "functional" MRI technology that let them watch the brain respond to words. And what they saw didn't look like a module, says Benjamin Bergen, a researcher at the University of California, San Diego, and author of the book _Louder Than Words_. "They found something totally surprising," Bergen says. "It's not just certain specific little regions in the brain, regions dedicated to language, that were lighting up. It was kind of a whole-brain type of process." The brain appears to be taking words, which are just arbitrary symbols, and translating them into things we can see or hear or do; language processing, rather than being a singular module, is "a highly distributed system" encompassing many areas of the brain. Our sensory experiences can also be applied to imagining novel concepts like "flying pigs". Our sensory capacities, ancestral features shared with our primate relatives, have been co-opted for more recent purposes, namely words and language. Bergen comments, "What evolution has done is to build a new machine, a capacity for language, something that nothing else in the known universe can do," he says. "And it's done so using the spare parts that it had lying around in the old primate brain."

Novelist Kurt Vonnegut once opined, "There is no reason why the simple shapes of stories can't be fed into computers. They are beautiful shapes." Thanks to new text-mining techniques, this has now been done. Researchers at the University of Vermont's Computational Story Lab have analysed over 1,700 English novels to reveal six basic story types - you could call them archetypes - that form the building blocks for more complex stories. They are: 1. Rags to riches - a steady rise from bad to good fortune 2. Riches to rags - a fall from good to bad, a tragedy 3. Icarus - a rise then a fall in fortune 4. Oedipus - a fall, a rise then a fall again 5. Cinderella - rise, fall, rise 6. Man in a hole - fall, rise The researchers used sentiment analysis to get the data - a statistical technique often used by marketeers to analyse social media posts in which each word is allocated a particular 'sentiment score', based on crowdsourced data. Depending on the lexicon chosen, a word can be categorised as positive (happy) or negative (sad), or it can be associated with one or more of eight more subtle emotions, including fear, joy, surprise and anticipation. For example, the word 'happy' is positive, and associated with joy, trust and anticipation. The word 'abolish' is negative and associated with anger. Do sentiment analysis on all the words in a novel, poem or play and plot the results against time, and it's possible to see how the mood changes over the course of the text, revealing a kind of emotional narrative. While not a perfect tool - it looks at words in isolation, ignoring context - it can be surprisingly insightful when applied to larger chunks of text

Language Log is an applied linguistics blog founded by U.Penn's Mark Liberman. This blog post concerns the phenomenon of character amnesia, which is becoming more prevalent in E. Asian countries like China and Japan, which have character-based languages. Basically, character amnesia is the phenomenon of forgetting how to write words, due to the increase in keyboard input of characters, word processing, online correspondence and composition, predictive text technology, and the decline of handwritten documents. This particular article focuses on Japanese kanji amnesia. The bottom of the article contains links to previous discussions of this topic.

Due to the increase in language-learning technology, learners of second languages can get exposure to the new language that weren't possible before, making it easier to learn a new language.