Tag: Language

Navigating Nabokov: A Companion Glossary for “Lolita” | April 08 2026, 11:24

I have finally finished the book The Reader’s Glossary – essentially a 5200-word dictionary for “Lolita” by Nabokov, but organized not alphabetically, like regular dictionaries, but in order of the occurrence of complex words, divided by chapters and indicating the context of the word or phrase. The website – readersglossary dot com (see the first comment). It is expected to be used, among other things, as a companion book while reading the original. Yes, it’s twice as thick 🙂

The dictionary turned out quite thick – 600-700 pages. It is available in four languages – Russian, English, French, and German. Moreover, the translations (RU, FR, DE) or clarifications (in ENG) are not abstract but contextual, taking into account how Nabokov himself translated the fragment from English (“Lolita” was first written in English, then translated into Russian).

On my website, there are huge fragments of these dictionaries RU, FR, DE, EN available for review (each about 1/3 of the total volume).

There is also a full-fledged interactive dictionary on the site, where you can enter a word and see its translation or explanation. The dictionary mainly contains complex words, but we know that complexity has its own definition for everyone, so all words are divided into three categories and highlighted with different frames. Probably for a well-read Anglophone, the first category (dotted) is completely useless (about 50% of the dictionary), for the less-read, maybe 20% are useless. But I decided not to cut it further, because the book is not only for Anglophones but also for those for whom English is a second language, and there those dotted frames are very handy.

Overall, I did this “for myself and friends,” just for fun, not as a commercial project. Therefore, I am quite sober in understanding that it has a super niche audience, and if even once a week someone finds it useful, it’s already nice.

Although it was something like a hobby, the book took a lot of time. To achieve what I did, I developed a dozen applications/scripts, a couple of which have their own interactive UI, in which I spent many hours over two months of work. And of course, I learned a lot in the process, which is actually the main fun of it.

So, come to the website – readersglossary dot com. Link in the comments

P.S. In Russian – only as a PDF for now. Amazon doesn’t allow selling books in Russian, only in a small number of European languages in addition to English. The French and German versions of the dictionary will be released on Amazon about a week from now.

Navigating the Lexical Complexity of Nabokov’s “Lolita” | April 02 2026, 15:56

I’ve finished the first version of a dictionary-style book on Nabokov’s “Lolita”. The chart shows how the complexity of vocabulary is distributed across the pages of the book. The lower chart averages 25 sentences, displaying the number of complex words on the vertical axis, with colors indicating their complexity/rarity (purple – the most complex, red – less complex, yellow – even less so). But I have already removed two levels, and overall, for a foreigner, all five levels are challenging. In the book, level 3 is marked with a dashed line, level 4 with a simple frame, and level 5 with a double frame. Currently, there are 5794 words, of which 541 are fifth level, 1070 are fourth, 1883 are third, 1393 are second, and 54 are first (the simplest ones). Considering that the first version ended up being 1148 pages, the dictionary will need to be significantly streamlined by removing what can be dispensed with. This mainly pertains to the first and second levels, and some from the third and fourth. The rarity of words is calculated in three ways: through LLM, and through two lists of word frequencies in the English language corpus (300K words).

Not all words are complex. For instance, in the sentence “With the ebb of lust, an ashen sense of awfulness, abetted by the realistic drabness of a gray neuralgic day, crept over me and hummed within my temples.” someone well-acquainted with English might not know the words ebb, abet, drabness, while everything else is familiar, but lower the requirements for the reader, and the dictionary might not be very useful for such cases.

Or consider the sentence:

Homo pollex of science, with all its many sub-species and forms; the modest soldier, spic and span, quietly waiting, quietly conscious of khaki’s viatric appeal; the schoolboy wishing to go two blocks; the killer wishing to go two thousand miles; the mysterious, nervous, elderly gent, with brand-new suitcase and clipped mustache; a trio of optimistic Mexicans; the college student displaying the grime of vacational outdoor work as proudly as the name of the famous college arching across the front of his sweatshirt; the desperate lady whose battery has just died on her; the clean-cut, glossy-haired, shifty-eyed, white-faced young beasts in loud shirts and coats, vigorously, almost priapically thrusting out tense thumbs to tempt lone women or sadsack salesmen with fancy cravings.

My browser even highlights four words here.

I have definitions of words in English, German, French, and Russian. I’ve encountered the issue that different words from the text are considered complex in different languages, yet they are unified for me. So, I’ll have to mark, for example, French words in the English text separately, so they are not included in the French version, since there, the reader knows, for instance, what quel mot means.

Overall, this weekend I’ll be manually removing about half, and then I can make the cover and list it on Amazon.

Exploring the Multifaceted Uses of “Oblong” in English and Russian | March 17 2026, 13:50

Sometimes in English, there are very unusual words that are very difficult to translate into Russian. Here, for example, is the word oblong. As an adjective, it translates as “elongated, oblong,” but in the book, both uses are nouns. Often oblong refers to a face – that is, close to an oval, but oblong is a broader concept that describes any figure having an elongated appearance. My mom bought an oblong tablecloth for her new table.

As a noun, it is also used, and quite frequently (though less so than as an adjective). As a noun, oblong means “a rectangular object or flat figure with unequal adjacent sides.” Rulers are considered elongated items (oblongs). Laptops, tablets, and flat-screen TVs are oblongs of different sizes. A rectangle can be defined as oblong; however, not all elongated figures are rectangles. The same face, for example. Additionally, in mathematics, an oblong number is what in Russian is called a rectangular number (the product of two consecutive numbers. For example, 12). In general, it’s utterly baffling.

The word has been alive since the 15th century, by the way. So, in my book, it appears twice, and both times as nouns. In the first case, Nabokov translated it as “corner,” and in the second – “a small oblong of smooth silver” as “a little piece.”

Exploring Multilingual Vocabulary in Nabokov’s Works with Apple Books | March 15 2026, 23:20

Man, it’s really convenient. Just sitting here reading.

The usage pattern is as follows: I hold the phone in my hands. There, in apple books, this and that book. You see an unfamiliar word – it will likely be in the word list of the chapter. The definition takes into account the translation by Nabokov himself. Then you look a couple words ahead, put the phone down, continue reading. You encounter those words, and they are still in your short-term memory, and hooray, you understand. During a break, you load the next couple of words into your brain. You have to hold the phone and flip through, each page contains 4-5 definitions.

Now, every word has definitions in English (interpretation), French, and German. Consequently, I can publish four books.

Overall, my level of English matches what my app predicts about which words will be challenging. But someday I’ll need the same for French, and it will require an assessment of the difficulty level for each word because even some basic words will be unclear to me. I’m not sure that a book with basic words will be handy. With rare ones – definitely handy.

Crafting Nabokov’s Dictionary: A Multilingual Lexical Journey | March 15 2026, 18:30

I’m reading Nabokov and decided to take a break to create a convenient app “Nabokov’s Dictionary” and am considering selling it on Amazon as a book. Essentially, it looks like this (see screenshot) – definitions of complex words in English, Russian, German, and French, in the same order they appear in the original book.

Would you buy such a book?

To accurately make their definitions, I also wrote an aligner – a program that matches sentences and paragraphs in English with their translations (Nabokovian) into Russian. And when a word’s definition is created, it uses not only the knowledge of LLM but also the Russian translation by the author. It’s worth separately discussing how the algorithm works (I invented it myself because everything I found online did not work as I needed). It first finds long sentences and matches the longest sentences with their pair through cosine similarity of embedding vectors created through the multilingual e5 model. These sentences become anchors. Then, assuming that for long sentences the error is almost excluded, the longest sentence between anchors is found, and everything repeats recursively. There are many situations where a sentence in Russian has no equivalent in English and vice versa, where a sentence is split into two, or conversely two are merged into one. The algorithm handles this as best as it can. The result is quite a good quality of alignment. To such an extent, that errors in alignment can hardly be found (but they are likely still there). Either way, it is only needed for the context for translating words, even if there are rare errors, it’s not a big deal.

Would you buy such a book?

The Curious Etymology of the Turkey: Naming Perceptions Across Languages | March 09 2026, 21:36

I wondered why turkey is called turkey here and what it’s called in Turkey. In Turkey, it’s called hindi – turkey! Decided to see what it’s called in India. Haha, in Hindi, it’s called Turkish (टर्की). Let’s see in other languages. Portuguese – Peru. That means, for them, it’s Peruvian. In Spanish – pavo, which refers to peacock 🙂 “pavone” in Italian – peacock. In French – dinde, because this bird came from the West Indies (America). Comes from poule d’Inde – “hen from India/West Indies”. Greek – “Γαλοπούλα” “French bird”.

Exploring Redundancy in Toponymy: From European Rivers to the Hill of Hills | March 08 2026, 02:54

Reading Nabokov, there “…with the dash of the Danube in his veins…”. Turns out, Danube is Дунай. But that’s okay, trivial stuff, the interesting thing is something else. That Don, Danube, Dniester, Dnieper, Donets, Dvina, and Disna essentially mean more or less the same thing – river. Apparently, the ancient people were not always rich in imagination when it came to toponymy. If you live by the water, you simply call it “River”. Over time, others came, heard this word, took it as a proper name, and altered it slightly to fit their accent. This way “River” (Danu) transformed into a dozen different names across the map of Europe.

The river Volga essentially is also just “river”. Okay, slightly different, “Volga” comes from the Proto-Slavic *Vòlga, which literally means “moisture” or “water”.

Also, it turned out that the Sahara desert is named so because Sahara (الصحراء) is desert. And the Gobi desert is called Gobi because Gobi in Mongolian is desert.

While googling, I stumbled upon another fun thing. There’s a place in England, Torpenhow Hill. The name is composed of four different linguistic layers: Tor — in Old English “hill”, Pen — in Cumbric “hill”, How — in Old Norse “hill”, Hill — in modern English “hill”. Result: “Hill-hill-hill-hill”. Likely, each new people arriving in this area didn’t understand that Tor, Pen, and How were already names for the hill, and added their variant of the word “hill”.

Exploring English: Verbs, Misunderstandings, and Learning Through Contrast | March 06 2026, 23:57

About the English language. When Yuki sees another dog, he adorably places his chin on the ground and presses his paws to his face, but I have to tell him every time not to approach because once he lets them get closer, he suddenly starts growling and instigating a fight. And what verb would you choose for that?

Well, from school I knew that roar meant growl. And I even told everyone “roar” for the first week until I googled it and realized that in roar, it’s tigers, lions, and motorcycles, but for dogs, it’s growl or even snarl (with teeth showing).

Or take the phrase “cook food.” To cook comes to mind, but actually, to cook implies thermal processing (fire, stove). If you’re “cooking” a salad, tea, or a sandwich, a native speaker would say make. Saying “I’m cooking salad” is like you decided to boil it.

Or suppose you decided to watch a movie. In English, the choice of verb depends on where you are and how large the screen is. When you go to the cinema, you use the verb see. “Let’s go see the new Dune movie at the cinema.” If you say “I watched a movie at the cinema,” they’ll understand, but it sounds a bit technical, as if you were sitting there closely studying the screen like a security guard monitoring it.

But. When you turn on your television, laptop, or projector in your living room, watch comes into play. The verb watch implies extended attention to something on a smaller (relative to theater) screen. By the way, if the screen is off, you look at it (as an item). Once you turn it on and a picture appears, you start to watch it.

Generally, for an advanced level, it makes sense to attach each concept to a scale, to remember the words in shades of intensity. For example,

Cry -> Weep -> Sob.

Annoyed -> Irritated -> Angry -> Furious -> Livid.

Smile -> Chuckle -> Laugh -> Giggle -> Guffaw

Spitting -> Drizzling -> Raining -> Pouring

and so on.

And then further distinguish them by paired opposites, like the smile-cry from the example above.

It’s very easy to remember when put together.

But it’s necessary to try to apply them, otherwise it’s no good. Some words may be bookish, and here it’s important in what context it is said. If you told a friend in a pub: “I cannot comprehend this beer” – it would sound as if you’re writing a dissertation on that beer

Exploring LLMs and AI: Connecting Neural Processors to Natural Language Learning | February 15 2026, 15:41

Some thoughts on LLMs and artificial intelligence in general. And in the end about neuromorphic processors and Intel Loihi.

As you all know, fundamentally LLMs operate on the principle of “propose the likely next word using the context from the previous N words,” and then the word enters the context, and the process repeats all over again for the next word. Well, and the context is also processed considering the importance of words.

Now let’s think about how children were taught languages in primitive societies. There were no alphabets, nor grammar. But the grammar itself, according to estimates, was quite complex—based on observations of the small languages of small peoples. Simple grammar is modern when the language has spread to millions and billions.

That is, a child’s brain had to reconstruct grammar in its neurons simply from the flow of speech from those around and through testing the understanding of what was said. It’s likely that the child was corrected if they spoke incorrectly, but somehow this grammar and sound extraction had to settle in the brain—and here the same mechanism as in LLMs is used: which words/sounds go next in what context is determined by latent and uninterpretable rules, which each person in childhood creates in their brain in their own way. That is, roughly speaking, it trains the ML model every time from scratch on the flow of speech from those around. A child does not know what a “case” is, but feels what ending is statistically more likely in a given context.

Actually, modern cognitive science (Karl Friston’s theory) asserts that the brain is literally a “prediction machine.” We constantly generate hypotheses about the next sound or word and correct them when they don’t match (prediction error).

The peculiarity of LLMs is that for them, teachers are texts and images, but for a child’s brain, it’s the living world around, and if all the texts they hear were digitized, their volume wouldn’t even be enough to train a very weak model. LLM sees the word “apple” next to the word “red.” A child sees an apple, feels its smell, taste, weight, and simultaneously hears the sound. This “stitching” of different sensory channels allows building neural connections thousands of times faster than on plain text. That is, modern LLMs take a brute force approach—simply observing the speech of billions, not just their immediate environment. A good question is how the human brain manages to learn from a relatively small dataset. However, it’s a big question whether this dataset is small—for example, lip movements, facial expressions, context provide a lot for building this neural network in the biological brain.

About the context: unlike LLMs, a child understands the speaker’s intention. If mom looks at a cup and says “hot,” the child’s brain limits the search space of meanings to one cup. And if he didn’t understand, he’ll get burned and remember.

One might assume, of course, that the brain already has a ready network at birth. It’s true, but science can’t yet explain it properly. Our entire genetic program has about 20,000 genes encoding proteins, and these 20,000 are responsible for everything—where and how the lungs, heart, bones, blood should be built, and they themselves are of mind-boggling complexity, and somewhere among 3 billion nucleotides and 20,000 genes this information must be recorded.

Apparently, genes encode not a map but an algorithm of self-assembly. Essentially, the architecture of the neural network is built dynamically, and this process begins long before birth. Then it is calibrated by all the signals received by the unborn child, and by the time of birth, there is already a somewhat tuned network in the brain.

It’s likely that the child’s brain is millions of neural networks of different “architectures” that evolve and merge in the learning process. Unlike LLMs, here learning and usage are strictly separated in time. But most importantly—the brain, although the most energy-consuming in the body, consumes very little energy in absolute terms, especially compared to the current “candidates for replacements in hardware.”

In the last few years, there has been active development in the field of neuromorphic systems (for example, the old IBM TrueNorth processor and the actively developing Intel Loihi). In conventional AI, neurons transmit numbers (0.15, 0.88…). In neuromorphic systems, they transmit “spikes” (impulses)—as in the living brain (and the architecture is called Spiking Neural Network – SNN). A few years ago, Intel released Loihi 2. Fully programmable. Neurons on Loihi can change their connections (synapses) right during operation. Supports plasticity—the very biological mechanism when the connection between neurons is strengthened if they often “fire” together. But the main thing—it consumes very little.

In this architecture, the model can continue learning “on the fly” right during operation, without forgetting old data (Continual Learning). Besides that—extreme energy efficiency.

Loihi 2 cannot multiply matrices as modern GPUs do, so completely new software has to be written for them (and this is moving very slowly). No PyTorch or TensorFlow—for Loihi there is only the Lava framework available today. And 1 million neurons from Loihi 2 is very little for LLMs. Therefore, Intel creates systems like Hala Point—it’s an array of 1152 Loihi 2 processors. It contains up to 1.15 billion neurons. Theoretically, in terms of performance per watt, such a system can surpass traditional GPUs by 10–50 times when working with AI models.

Experimental LLMs are already being launched on Loihi 2 (for example, models with 370 million parameters). They are not yet going to replace ChatGPT in the cloud, but theoretically, they are the future for “smart” robots and gadgets that need to understand human speech while running off a small battery.

We’ll observe. It might turn out to be a dud, or it could be another major revolution.

From Camels to Bishops: The Fascinating Evolution of Chess Pieces | February 14 2026, 16:24

It all started with a question – why does the elephant ♗ have this notch? And in general, where is the elephant, and where is the bishop, and is this notch about the elephant or the bishop? Anyway, listen to what I dug up, there’s a lot of interesting stuff here.

Chess originates from India. There, this figure was initially called a camel. And their elephant was what we call a rook – which if you think about it, a rook is basically a boat – or in English, rook, which if you think about it in Persian, it means chariot.

The name “Tura”, which we often hear in colloquial speech, is a pure import from Europe. In French – tour. In Italian – torre. In Latin – turris. All of these mean the same thing: tower. When chess arrived in Europe, knights and monks didn’t really understand what a “battle chariot” was (they were out of fashion by then), but they knew very well what a siege tower was.

So, returning to the elephant and the notch.

The short answer – to distinguish it from a pawn. But there’s a long answer.

When chess came to Europe, the Indian camel was switched to the Catholic bishop, and thus the piece was named bishop. The notch supposedly symbolizes a miter – the high headgear of clergymen. That’s precisely why in English the piece is called bishop. Though to me, it’s just a mouth from the Muppet show.

Interestingly, in French, it’s le fou – the jester. In German, it’s Läufer – runner. In Greek – officer (Αξιωματικός). Why officer? I don’t know, but I dug up that in Chinese chess, xiangqi (象棋), the “elephant” piece is indicated and pronounced as xiàng (象). This character indeed means “elephant.” However, in Chinese history, there was a high state office called xiàng (相), usually translated as “chancellor,” “prime minister,” or “chief minister.” This is a different character, although the pronunciation coincides. Probably, the officer comes from here too.

The chess knight is almost a horse in all languages, only in English and a few others, it’s a knight (although, in German, for example, it’s Springer – jumper, and in Sicily – donkey).

So, in German, there is a jumper and a runner. And a little horse in German is actually a king.

I also learned that there are ready-made solutions for ANY chess endgame in which there are seven or fewer pieces on the board, regardless of the position, the composition of the remaining pieces, or possible moves. This information, known as endgame tables, currently occupies 18.4 terabytes.

from the comments: “The most interesting thing is that this week a multi-year work was completed, and there is now a ready solution for any position with 8 pieces or fewer (7 pieces was already about 12 years ago, but there’s a very big difference)”