Dave Hickey and the louche tradition Contributed by Jonathan Stevenson / A clear strain in American letters celebrates the capacity of insouciant and unabashedly disreputable people to say things that matter by cutting through the flatulent.Body Language at The Painting Center Contributed by Carol Diamond / Now on display at The Painting Center, the group exhibition titled “The Body in Question,” a phrase cheekily resonant of a coroner’s report, explores the.To use content beyond the scope of this license, permission is required. Two Coats of Paint is licensed under a Creative Commons Attribution – Noncommercial-No Derivative Works 3.0 United States License. Resigned to his individualist compulsions, in the final scene he is left trudging bloodily from Leo’s silenced house, the last man barely standing – Frank’s visual epitaph. Frank isn’t cut out for servitude, either. The showstopper isn’t so much that he’s not cut out for it – though that’s certainly a major factor – as Prosky’s duplicitously avuncular Chicago gangster Leo’s effective confiscation of his money by tying it up “on the street” in order to keep Frank in his employ. He has taken an angry yet affectingly futile stab at normal life above ground, hoping to settle down with Weld’s Jessie and adopt a child. More importantly, the framing signifies that those minutes following the successful completion of his last and greatest heist constitute Frank’s lone moment of contentment.
In terms of smoking cool, it’s up there with Bogart in Casablanca, Mastroianni in La Dolce Vita, and Belmondo in Breathless. At the end of the scene, Frank, having secured the diamonds, sweaty and grimy, takes off his protective hood, sits down on a chair, removes his gloves, lights a cigarette, and almost post-coitally exhales the smoke, gently nodding to himself. The first, a little more than halfway into the movie, involves Caan’s Frank’s methodically muscular cracking of a putatively impenetrable safe via a hi-tech blowtorch and a lance-like steel rod. The denouement is bracketed by images that Mann must have intended as the movie’s most indelible ones, like something akin to paintings. To learn more, see the privacy policy.Yet in Thief style and substance come together synergistically as they never have in any of his other ten movies. Please note that Describing Words uses third party scripts (such as Google Analytics and advertisements) which use cookies. Special thanks to the contributors of the open-source mongodb which was used in this project. As you'd expect, you can click the "Sort By Usage Frequency" button to adjectives by their usage frequency for that noun. The "uniqueness" sorting is default, and thanks to my Complicated Algorithm™, it orders them by the adjectives' uniqueness to that particular noun relative to other nouns (it's actually pretty simple).
You can hover over an item for a second and the frequency score should pop up. The blueness of the results represents their relative frequency. If anyone wants to do further research into this, let me know and I can give you a lot more data (for example, there are about 25000 different entries for "woman" - too many to show here). In fact, "beautiful" is possibly the most widely used adjective for women in all of the world's literature, which is quite in line with the general unidimensional representation of women in many other media forms.
On an inital quick analysis it seems that authors of fiction are at least 4x more likely to describe women (as opposed to men) with beauty-related terms (regarding their weight, features and general attractiveness). Hopefully it's more than just a novelty and some people will actually find it useful for their writing and brainstorming, but one neat little thing to try is to compare two nouns which are similar, but different in some significant way - for example, gender is interesting: " woman" versus " man" and " boy" versus " girl". The parser simply looks through each book and pulls out the various descriptions of nouns. Project Gutenberg was the initial corpus, but the parser got greedier and greedier and I ended up feeding it somewhere around 100 gigabytes of text files - mostly fiction, including many contemporary works. Eventually I realised that there's a much better way of doing this: parse books! While playing around with word vectors and the " HasProperty" API of conceptnet, I had a bit of fun trying to get the adjectives which commonly describe a word.
The idea for the Describing Words engine came when I was building the engine for Related Words (it's like a thesaurus, but gives you a much broader set of related words, rather than just synonyms).