Real pain for my sham friends, real tricks for my meh friends, and finding more like this with NLP

source: L

Susie Dent has an ever growing Twitter following of 1,1 million unique word lovers to whom she shares her daily word of the day. Word search engine Unscramblerer.com went through Susie Dent’s whole Twitter history and analyzed what are the most liked, shared and commented words of the day she has posted.

ingordigiousness, recrudescence, sequaciousness, ...

Some thoughts on the art of the newspaper lede.

A mixed metaphor walks into a bar, seeing the handwriting on the wall but hoping to nip it in the bud.

A misplaced modifier walks into a bar owned by a man with a glass eye named Ralph.

Just like many other mysteries, our mystery of the phantom reference ultimately had a very simple explanation: sloppy writing and sloppy quality control. An academic incentive system that makes publication in Web of Science listed conference proceedings popular invokes the law of big numbers. Thus the actual number of mistakes rose to be high enough to be noticeable, even though the mistake was only committed by a tiny fraction of the authors.

This is a column about curse words, and the deployment of curse words in sports. Don’t worry: I’m not going to use a curse word here. At least none of the really good ones. I might use a drat, a rats, a Fudgesicles, or a phooey, or, if I get really agitated—and this is just a warning to the kids at home, curled up reading a print newspaper, as kids do—a gadzooks. But I’m not going to say $*#$@!. Or %&#*!, *#$#@, or #*$!(@%. And definitely not #$*#@*^!.

The North American Scrabble Players Association (NASPA) seems poised to remove hundreds of offensive slurs from tournament-level Scrabble play.

Words that are “used to cause offense on scatological, prurient, profane or other grounds” are not under discussion this time around. NASPA publishes an obfuscated, anagrammed list of which offensive words fall into each category.

source: ars

The person who types “lol” is never actually laughing; the person who types I’M SCREAMING is silently dabbing at a screen. In the same way, the person who is perpetually shocked and outraged and brimming with righteous fury is almost always lying to themselves. They’re as affectless as the rest of us: play-acting, downloading synthetic emotions, and then passing them on.

source: jwz

Because Unicode contains such a large number of characters and incorporates the varied writing systems of the world, incorrect usage can expose programs or systems to possible security attacks. This is especially important as more and more products are internationalized. This document describes some of the security considerations that programmers, system analysts, standards developers, and users should take into account, and provides specific recommendations to reduce the risk of problems.

A large number of problems as well.

source: solar

Though we have presented quite a bit of informal and recent use, our earliest written use of welp goes back over 70 years. It shows up in a scholarly article on two of welp’s linguistic cousins: yep and nope. Well gained that final -p as part of a normal process of articular: the lips come together to stop the sound of well and prepare for the next sound, and some hear that stoppage as a -p. This means it is very common in speech. One linguist went so far as to say that anyone who didn’t know what welp meant was probably an alien.

A San Diego federal judge Friday dismissed a $10 million defamation lawsuit filed by the owners and operators of San Diego-based One America News Network against MSNBC and political commentator Rachel Maddow. Last summer, the liberal host told her viewers that the Trump-friendly conservative network “really literally is paid Russian propaganda.”

But data breach notifications have become an all-too-regular exercise in crisis communications. These notices increasingly try to deflect blame, obfuscate important details and omit important facts. After all, it’s in a company’s best interest to keep the stock markets happy, investors satisfied and regulators off their backs. Why would it want to say anything to the contrary?

source: white

I went through some of the examples of metaphors in Metaphors To Live By and grepped all the man pages on my computer for them.

There is a widespread public misperception, particularly among the New Age sector, that the Chinese word for “crisis” is composed of elements that signify “danger” and “opportunity.” I first encountered this curious specimen of alleged oriental wisdom about ten years ago at an altitude of 35,000 feet sitting next to an American executive. He was intently studying a bound volume that had adopted this notorious formulation as the basic premise of its method for making increased profits even when the market is falling. At that moment, I didn’t have the heart to disappoint my gullible neighbor who was blissfully imbibing what he assumed were the gems of Far Eastern sagacity enshrined within the pages of his workbook. Now, however, the damage from this kind of pseudo-profundity has reached such gross proportions that I feel obliged, as a responsible Sinologist, to take counteraction.

The loose skin at the joint of one’s elbow

But curiously, arrant and errant are the historically the same word, with an interesting and tangled history.

Lots of little facts organized in various ways.

source: grugq

It pains me to admit it, but this one even confuses me.

Rendering text, how hard could it be? As it turns out, incredibly hard! To my knowledge, literally no system renders text “perfectly”. It’s all best-effort, although some efforts are more important than others.

I lost it at multicolored ligatures.

source: L

This is a really important paper for anyone working with language or generative models, and just in general for anyone interested in understanding some of the broader implications and possible unintended consequences of deep learning. There’s also a lovely sense of the human drama accompanying the discoveries that just creeps through around the edges.

Disclosure of secrets is of particular concern in neural network models that classify or predict sequences of natural language text… even if sensitive or private training data text is very rare, one should assume that well-trained models have paid attention to its precise details…. The users of such models may discover— either by accident or on purpose— that entering certain text prefixes causes the models to output surprisingly revealing text completions.