O sol é grande…

Słońce jest wielkie: ptactwo cicho zniża loty
w okresie, który zwykle w zimnej trwa pogodzie.
Zbudziłbym się przy z góry spadającej wodzie,
lecz nie ze snu bynajmniej — z poważnej zgryzoty.

O rzeczy, ciągle próżne — ciągłe wasze zwroty,
jakie serce was przyjmie w ufności i zgodzie?
Czas mija, dni za dniami ciągną się w pochodzie,
bardziej chwiejne niż żagli na wietrze trzepoty.

Widziałem tu już cienie, widziałem kwitnienie,
widziałem tyle wody, zieleń tak bogatą,
słyszałem wszystkich ptaków o miłości pienie.

Wszystko jest suche, nieme, z barwą popielatą;
ja też, zmieszany, inne przyjmuję odcienie.
Odżywa wszystko inne: nie ma rady na to!

Coat of arms of Portugal
Coat of arms of Portugal. Source: Wikipedia

This is the Polish translation… not of the first Portuguese sonnet but of the most popular one among the earliest Portuguese sonnets. The original, shown below, was written by Francisco de Sá de Miranda between 1530 and 1558. And today, June 10, is the Portugal Day.

O sol é grande: caem coa calma as aves,
Do tempo em tal sazão, que sói ser fria.
Esta água que de alto cai acordar-me-ia,
Do sono não, mas de cuidados graves.

Ó cousas, todas vãs, todas mudaves,
Qual é tal coração que em vós confia?
Passam os tempos, vai dia trás dia,
Incertos muito mais que ao vento as naves.

Eu vira já aqui sombras, vira flores,
Vi tantas águas, vi tanta verdura,
As aves todas cantavam de amores.

Tudo é seco e mudo; e, de mistura,
Também mudando-me eu fiz doutras cores.
E tudo o mais renova: isto é sem cura!

O sol é grande…

Kling-dikt över författarens sinnebild, en silkesmask

Sonet o godle autora, jedwabniku

Ucisz się, myśli! Z wolna spekulujesz,
czym być to może. Spójrz: ta kreatura,
żałosny, nagi robak, to figura,
co nie ma kształtu — nic w niej nie wyczujesz

wzrokiem. Lecz zobacz, przecież więcej tu jest
niż sądzisz: zdatna, czysta, cna natura,
dziwna, przez Boga utworzona, która
gryząc rośliny, kornie włókno snuje,

wyplata nici, tka płótno jedwabne.
Tworzy skarb z liści, aż pusta, zmarniała,
owita przędzą, żywota dokona.

Lecz wstanie nowe stworzenie powabne
o pięknych skrzydłach: świeża i wspaniała
dusza, przez żywe słońce przebudzona.

Coat of arms of Sweden
Coat of arms of Sweden. Source: Wikipedia

I published this translation of the first Swedish sonnet to celebrate June 6, the National Day of Sweden. Below is the original, written in 1644 by Georg Stiernhielm, and a literal re-translation from Polish into English for those who want to criticize my version. For another sonnet with a silkworm in it, read my translation of Farie së ndeerme….

Kling-dikt över författarens sinnebild, en silkesmask

Håll stilla mitt förnuft, dig saktelig besinna,
vad detta vara må. Du sir här en figur,
en usel, naken kropp, en mask, ett kreatur,
som ingen skapnad har, där intet är till finna,

som ögat lyster se. Men märk: här ligger inna
mer än en tänka kan, en nyttig, ädel, pur,
en sällsam, underlig av Gud beredd natur:
en mask, dess spis är blad, dess id är artigt spinna,

dess spunna silkes-tråd, dess verk och väv är siden.
Av blad gör han en skatt, till dess han, tom och mager,
invecklat in-dör i sin väv och livet stäcker.

Men si, en ny figur, med vingar prydd, med tiden
här kommer fram igen, uppkvickter, fin och fager,
en livlig sol hans själ med kraft en gång uppväcker.

Sonnet about the author’s crest, silkworm

Calm down, [my] thought! You slowly speculate
what this can be. Look: this creature,
pathetic, naked worm, is a figure
that has no shape — you will sense nothing in it

with sight. But see: still, there is more here
than you suppose: an apt, pure, noble nature,
strange, formed by God, which,
chewing plants, humbly spins fibre,

plaits threads, weaves silk linen.
It is creating a treasure from leaves until, empty, wasted,
wrapped in yarn, it will end [its] life.

But a new graceful being will rise,
with beautiful wings: a fresh and splendid
soul, awakened by the vivid sun.

Kling-dikt över författarens sinnebild, en silkesmask

What Makes a Best-Selling Novel?

A Machine Learning Approach

In 2013, Ashok et al. answered this question basing on the writing style, with 61–84% accuracy. This post, on the other hand, examines plot themes in best sellers. Note that my model can hardly predict the commercial success of a novel from its plot. That would be quite a surprising feat, making reviewers obsolete. My goal was more modest: finding statistically profitable topics to write about.

Using PetScan and Wikipedia’s page export, I downloaded 25,359 Wikipedia articles belonging to Category:Novels by year. From each article, I extracted the section named Plot, Plot summary, Synopsis, etc. if present and, stripped of MediaWiki markup, saved it into an SQLite database along with the title of the novel, its year of publication, and a Boolean that indicates if it ever topped the New York Times Fiction Best Seller list:

SELECT title, year, was_bestseller, length(plot) FROM Novels
ORDER BY random() LIMIT 5;
Sharpe's Havoc            | 2003 | 0 | 2759
The Rescue (Sparks novel) | 2000 | 1 |
Slayers                   | 1989 | 0 |
The Warden                | 1855 | 0 | 2793
The Fourth Protocol       | 1984 | 1 | 5666

SELECT count(*) FROM Novels

SELECT count(*) FROM Novels
WHERE plot IS NOT NULL AND was_bestseller;

SELECT min(year) FROM Novels  -- The year of publication.
WHERE was_bestseller;  -- The NYT list starts in 1942.

To obtain easy to interpret results, I have built a logistic regression model on top of the TF–IDF transformation of articles processed by the Porter stemmer. The parameters have default values. In particular, the logistic regression uses L2 regularization so all lowercase words that are not stopwords appear in the model.

import nltk
from nltk.corpus import stopwords
from nltk.stem import porter
from sklearn import cross_validation
from sklearn import linear_model
from sklearn import pipeline
from sklearn.feature_extraction import text

def Tokenize(
    punctuation_re = re.compile(
  text = punctuation_re.sub(' ', text)
  tokens = nltk.word_tokenize(text)
  return [stemmer.stem(x) for x in tokens
          if x.lower() not in stop_set and x[0] not in uppercase]

X = []
y = []
connection = sqlite3.connect('novels.sqlite')
for row in connection.cursor().execute(
    """SELECT plot, was_bestseller FROM Novels
    WHERE year >= 1941 AND plot IS NOT NULL"""):
X_train, X_test, y_train, y_test = (
    cross_validation.train_test_split(X, y, test_size=0.3))
model = pipeline.Pipeline(
    [('tfidf', text.TfidfVectorizer(
          lowercase=False, tokenizer=Tokenize)),
     ('logistic', linear_model.LogisticRegression())])
model.fit(X_train, y_train)

The model can return the probability of being a best seller for any novel b with a plot summary:

logit(b) = −4.6 + 2.5 tfidf(lawyer, b) + 2.4 tfidf(kill, b) + ⋯ − 1.5 tfidf(planet, b)

Pr(was_bestseller(b)|plot(b)) = elogit(b) / (1 + elogit(b))

To put these coefficients in context, tfidf(lawyer, The Firm) ≈ 0.06. As it happens, the model returns logit(b) > 0, that is Pr(was_bestseller(b)|plot(b)) > 1/2 for no novel b from the train or test set. The highest probability, 0.39, is predicted for Cross Fire, indeed a best seller in December 2010. Only if I disable the normalization in TF–IDF or weaken the regularization in the logistic regression, I can overfit the model to the train set while for the test set both its precision and recall would be at most 20%. But, like I wrote in the introduction, this is not the point of this exercise. Let us look at the words with high absolute value of coefficients.

  • Apparently, it pays off to write legal thrillers: lawyer +2.5, case +2.4, law +1.5, client +1.3, jury +1.3, trial +1.3, attorney +1.0, suspect +1.0, judge +0.9, convict +0.8;
  • kill +2.4, murder +1.8, terrorist +1.2, shoot +1.1, body +1.1, die +1.0, serial +0.9, attack +0.9, assassin +0.8, kidnap +0.8, killer +0.8.
  • Political thrillers are not bad either: agent +1.4, politics +1.4, president +1.3, defector +1.2.
  • Business may be involved: firm +1.3, company +1.3, career +1.1, million +1.0, success +1.0, business +0.9, money +0.9.
  • Finally, the characters should have families: husband +1.4, family +1.3, house +1.2, couple +1.2, daughter +1.2, baby +1.1, wife +1.0, father +1.0, child +0.9, birth +0.8, pregnant +0.8, and use a car +1.5 and a phone +0.8.

The genres to avoid for prospective best-selling authors?

  • Sci-fi: planet −1.5, human −1.0, space −0.7, star −0.4, robot −0.3, orbit −0.3.
  • Children’s literature: boy −1.3, school −1.0, young −0.8, girl −0.8, youth −0.4, teacher −0.4, aunt −0.4, grow −0.4.
  • Geography and travels: village −1.0, city −1.0, ship −0.8, way −0.7, go −0.7, land −0.6, adventure −0.6, colony −0.5, native −0.5, follow −0.5, mountain −0.5, crew −0.5, forest −0.5, travel −0.5, inhabit −0.4, sail −0.4, road −0.4, map −0.3, tribe −0.3.
  • War: fight −1.0, warrior −0.6, war −0.6, weapon −0.5, soldier −0.5, army −0.5, ally −0.4, enemy −0.3, conquer −0.3.
  • Fantasy: magic −0.9, creature −0.5, magician −0.4, zombie −0.3, treasure −0.3, dragon −0.3.
  • History: princess −0.5, rule −0.5, kingdom −0.4, castle −0.4, century −0.4, ruler −0.3, palace −0.3 (for what it’s worth, A Game of Thrones only made it to the third place on the list so it does not count as a best seller).

Note that the code above ignores capitalized words. If it does not, the most significant words become the names of characters from best selling book series: Scarpetta +3.0, Stephanie +2.9, Ayla +2.0, etc., with additional insights like FBI +1.3, CIA +1.3, NATO +0.9, Soviet +0.9, or Earth −1.1.

What Makes a Best-Selling Novel?

Wisła in Fact Likes Cracovia but Doesn’t Know How to Start Talking

The relationships between fans of football clubs in Poland can be fourfold: neutrality, friendship (zgoda), enmity (kosa), or pact (układ). The belief that there are two disjoint blocs gathered around The Great Triad (Arka, Cracovia, and Lech) and Three Kings of Great Cities (Śląsk, Wisła, and Lechia) is false. Here is the largest connected component of the graph of friendships.


The graph of enmities would be less clear. For instance, Cracovia has friendships with Tarnovia and Sandecja but Tarnovia and Sandecja are enemies. Or GKS, Górnik, Ruch, and Zagłębie: every two of them are enemies.

Source: http://polscyhools.w.interiowo.pl/ekipy.html.

Wisła in Fact Likes Cracovia but Doesn’t Know How to Start Talking

Stylometry—It Works! (in Some Circumstances)

This post was supposed to reveal the author of the 13th Book of Pan Tadeusz, an anonymous pornographic sequel to the Polish national epic. Despite my attempts that took into account rhyming sounds, word syllable count, and custom morphological analysis for Early Modern Polish, I failed to identify the author. Which is not that bad: authorship attribution, especially when regurgitated by journalists, is often reduced to ex cathedra statements: “a computer has proven that work X was written by author Y”; the fact that the confidence level is unknown is not reported.

Instead of a literary discovery, I present you a little game: Which Polish text is your writing like? It tells me that The 13th Book is most similar to Antymonachomachia by Ignacy Krasicki who died 33 years before the publication of Pan Tadeusz. Oh well.

The game is based on texts from Wolne Lektury, the Polish equivalent of Project Gutenberg. I appreciate Radek Czajka’s help in downloading them.

Since I know little about writing style analysis (known as stylometry), the entire sophistication of my program lies in calculating the frequency of a few dozen of tokens in each text. This idea is similar to Alphonse Bertillon’s anthropometry, a late-19th-century efficient system of identifying recidivists by classifying eleven body parts as small, medium or large.

We compare text style rather than text topics, so the program pays little attention to content words. It counts final punctuation marks, commas, and 86 frequent function words, that is conjunctions, prepositions, adverbs, and so-called qubliks. These counts are divided by the total number of tokens in the text, yielding a 90-dimensional vector of token frequency for each text.

The figure below shows the results of hierarchical clustering of the texts longer than 5000 tokens, obtained with

        frequency_matrix, method=’ward’, distance=’euclidean’))


I, for one, am impressed by its gathering together most of texts written by Kasprowicz, Krasicki, Rzewuski, and Sienkiewicz, or translated by Boy–Żeleński and Ulrich.

How reliable are the results? To answer this question, I perturbed the token counts: for each text composed of N tokens, I replaced k occurrences of each counted token by a random variable with the binomial distribution B(N, k/N), that is the count of heads in N tosses of a biased coin whose heads probability is k/N. For each text from Wolne Lektury, the x axis in the figures below shows the total number of tokens. The y axis shows the frequency with which the nearest point by the Euclidean metric corresponded to a different text or a text by another author/translator, measured in 1000 such random perturbations. In case you wonder how the y axis appears logarithmic and contains zero at once, the plotted variable is log(y + 0.001).

I approximated both the text misattribution probability and the author misattribution probability by 1−(erf(√N/c))b, with empirical values of constants b and c depending on the language, the tokens, and the texts.

Here is my hand-waving explanation of this formula. The coordinates of perturbed points, multiplied by N, have a multivariate binomial distribution (it does not matter whether the coordinates are correlated or not). When N approaches infinity and k/N remains constant, the binomial distribution is asymptotically normal with variance proportional to N (by the central limit theorem applied to tossing the coin), and the multivariate binomial distribution is asymptotically multivariate normal. Dividing the random variables by N, we return to the coordinates, which asymptotically have a multivariate normal distribution with individual variances and covariances proportional to 1/N.

The points divide the 90-dimensional vector space into Voronoi cells whose centres correspond to the mean vectors of the distributions. Moving a point to the other side of some wall of its Voronoi cell means moving it by more than d in the direction perpendicular to the wall. The projection of any multivariate normal distribution with variances and covariances proportional to 1/N onto a vector is a (univariate) normal distribution with variance proportional to 1/N. The probability that a random variable with variance σ2=a/N differs from its mean by more than d (that is, that the permuted point crosses the wall, causing a misattribution) equals 1−erf(d/σ) = 1−erf(dN/√a) = 1−erf(√N/c). Since the Voronoi cell has many walls in different directions, the overall probability that the point exits its cell is approximately equal to 1−erf(√N/c1)×⋯×erf(√N/cn). The erf function decreases rapidly so the factors with the smallest cis dominate the product, which can be approximated by the formula 1−(erf(√N/c))b.


The figures explain why it was hard to ascribe the author to The 13th Book: even if other works by the author belonged to the Wolne Lektury corpus (they probably do not), The 13th Book has merely 1773 tokens.

Stylometry—It Works! (in Some Circumstances)

Sipping Rum: Some New Palindromes

A somewhat popular sport [1, 2] is extending Leigh Mercer’s immortal palindrome “A man, a plan, a canal—Panama!” It occurred to me that its principle can be applied to the Polish palindrome by Julian Tuwim: “Popija rum as, samuraj i pop.” (“Both an ace, a samurai, and an Orthodox priest are sipping rum.”) All we need is a computer program and a list of Polish animate nouns. Here we go:

Popija rum as, said, diak, goj, drab, tokolog, igrek, odlewca, mim, tenor, abba, rodak, imam, alkad, gigant, alb, ober, retor, fan, ilot, rapper, nowy car, usar, adresat, efor, papa, grek, saper, treser, epik, bob, angol, ananas, aga, mameluk, urka, tatka, ergolog, ladro, lis, ork, induna, grum, fleja, batiar, akyn, wał, sowar, psar, kudła, renegat, symplak, ilota, kat, alumn, amor, eponim, daremnik, spec, tan, gajowy, durnota, kret, inka, mods, esbol, rajtar, bidak, tamada, mongoloid, arat, sir, abat, imamita, barista, radiolog, nomada, mat, kadi, brat, jarl, obses, domak, niter, katon, rudy woj, agnat, cep, skin, mer, admin, operoman, mulat, akatolik, alp, mysta, generał, duk, ras, prawosławny, karaita, baj, elf, murga, nudnik, rosi, lord, algolog, reak, tata, kruk, ulema, mag, asan, analog, nabob, kiper, eser, trep, asker, gapa, profeta, serdar, asura, cywon, rep, partolina, froter, reb, oblat, nagi gdak, lama, mikado, rab, baronet, mima, cwel, doker, gigolo, kot, bard, jog, kaid, dias, samuraj i pop.

(The adjectives nowy, rudy, and nagi got mixed among nouns. For a better effect, I manually removed the commas that followed them.)

Lazily, I used only slightly modified Peter Norvig’s backtracking program. I extracted the nouns from a text file used in the Polish morphological analyzer Polimorfologik. The lines of the file look like this:

samuraj         samuraj subst:sg:nom:m1
samuraja        samuraj subst:sg:acc:m1+subst:sg:gen:m1
samurajach      samuraj subst:pl:loc:m1
samurajami      samuraj subst:pl:inst:m1

The appropriate forms of nouns can be extracted with

$ grep subst:sg.*nom.*m1 polimorfologik.txt | cut -f 1 > npdict.txt

The m1 class contains masculine-personal aka virile nouns. Although the names of animals from the m2 class would also suit our purposes, that class contains also names of odd things like currencies, dances, car brands, or mushroom genera that would look strange in the sentence. With apologies to feminists, I have no means of extracting feminine-personal nouns or neuter-personal nouns automatically as they play no special role in Polish grammar.

The palindrome above contains only singular forms of common nouns (152 words in total). If we allow also singular masculine forms of adjectives, we can get a 269-word palindrome:

Popija rum as, said, diak, goj, drab, perski murga, kadi beż netto, klawy rzutki froter, kto, penolog, iglany sini magaski frant, utyty bojowny kaper, trak, sanowy cynawy rusy rotny sracz, sowar, aspan, ilot, raptor, mamlas, on, rebe, wali lis, jebak, cacy woli amor, fan, wodnik, ergolog, lama, mim, asker, gad, jarski men, as, pajac, darmy rosi preser, oferent, rapsod, nabab, abat, symplak, induna, tebriski gamrat, akatolik, rodak, ladro, lisi migany wandal, papa, cwel, doker, gid, rumski golkiper, asura, elew, okej arat, siwy ratar, maksi potowy żywotni wig, orski alb, obyły baca, cywil, paskuda, gemajn, inka, tępy wolowaty raby skin, turkos, esbol, użyty spec, ki marecki mima, kruk, a-ż popi lżywy wozak, angol, ananas, a-z raja, jary picer, kok, tato, nominat, inaki cap, ospały wilk, muli pupka, mods, aborter, dr, enat, siny talib, abba, reb, be rab, babi latynista, nerd, retro, bas, domak, pupil, umkliwy łaps, opaci kani tani mono tatko, kreci pyra, jajarz, asan, analog, nakazowy wyżli pop, żak, urka, mimik, ceramik, cep, syty żul, obses, okrutnik, sybaryta, wolowy pętak, ninja, mega duk, sapliwy caca były bob, laik, srogi wintowy żywotopis, kamrat, arywista, rajek, ow, elear, usar, epik, logik, smurd, igrek, odlewca, papla, dnawy nagi misi lord, alkad, ork, ilota, katar, magik, sir, beta, nudnik, alp, mysta, baba, bandos, partner, efor, eser, pisorym, radca, japs, anemik, srajda, grek, sam, imam, algolog, rekin, down, afro, mailowy cacka, bej, si lila, weber, nosal, mamrot, partolina, psar, awosz, car, syn, torys, urywany cywon, askar, trep, akyn, woj, obyty tutnar, fiks, aga, mini synal, gigolo, nepot, kret, orfik, tuz, rywal, kot, tenże bidaka, grum, iks, rep, bard, jog, kaid, dias, samuraj i pop.

Using plural and proper nouns, we can reach at least 1493 words, for instance:

Popija rum as, kalif, drab, Belg, Remus, Leon, pank, said, diak, Goj, Acis, Pac, Tabak, Kajus, reb, luj, Iwon, Noe, Selim, kacap, Damon, Melcer, epicy, Rob, Atkinson, Jahn, Wahl, Omar, turowiec, ubici, nabab, Mobutu, helota, Nagórski, Dyda, kraker, Gola, Timur, Gil, Ramzes, Romanik, imperator, Ajnos, waleci, biker, rajtar, Umer, Miotk, Popek, Nils, Lefeld, epik, car, Tamil, Amoni, Capała, Sarnat, Jeron, Urban, Orwelle, Tym, Einar, Usarek, rabi, nemrodzi, Kramnik, Sałacki, Sawini, basza, Idzi, Zaremba, raja, Rogacz, Rubeni, Atlas, ontolog, anemicy, Ron, imitator, Inka, Bulik, setkarz, sublokator, Eisler, akatolicy, tato, idioci, Vidor, Apacz, Alan, Ziomek, Albin, Rom, Oktawian, odaj, Arka, kadi, boss, Ursyn, Ahmad, Dassin, Eden, Siadlak, Oscar, Idec, udecy, Rupert, rastamani, Armin, Onak, Rola, Gill, Eco, były, Tom, Eluard, Niski, Nyk, Anatol, Olson, Orkan, rasta, Geller, Bask, oblat, Emil, Ado, Izydor, ras, sipaj, Amnon, Nelson, imam, lapicyda, Bill, Iwan, Alo, Skiba, Tim, Jankes, Uryga, Nycz, Darek, Ardelli, arbek, lirycy, Raczek, Orion, Roda, Cortazar, Odo, idol, Otis, Sobik, cep, opaci, Sak, Lew, Morka, bydlak, Sommer, Feret, Tyrsi, robol, opilec, Rama, Gert, ufolog, Iżyccy, Malka, Kwak, Malak, Tal, Redak, Solti, Lopez, Sot, rabbi, Latacz, Darren, Sopata, Taj, ninje, limnolog, flisak, udek, Kimak, Byrski, Fibak, Masaj, Elsner, efeb, lokator, Papała, Bacik, Cepil, Sabat, sietniak, tokolog, Igrek, Sade, Mahomet, Opacki, sowar, Racki, Amor, Rawik, suswał, Syta, rwacz, Dow, Zadura, Dunin, Olas, Bakuła, Bil, Loeb, ergolog, Lasek, Liw, jarle, Varga, mener, Tabaka, logicy, Razin, Roja, Paganini, Lak, Baran, ilot, rapper, twoi, Wołosi, Colin, Eweni, Borak, Jaksa, gimnazjasta, Rams, Uri, Cywka, Durka, Meisels, Ilia, Koj, Jordi, Vadim, Arrow, Agaton, Rudnik, Eric, Neil, knajak, Tyrawa, Bazan, Nestor, Olek, Nawoj, uwol, Racine, Magnus, mahdi, Wronka, Bełza, Floryni, Zub, Ołdak, Lasota, Linde, Ibsen, Negr, Ezopi, Woda, Swatek, odlewcy, Cezary, preser, eks, ubol, Koba, katar, Katz, Sapir, Nehru, lider, ubek, Collina, blogger, git, Nastak, Inuita, Maj, akolici, Fuk, nomada, rapsod, Adad, Jagger, autycy, Ted, Jeka, Jakub, ulema, Kwoka, Byrnes, rajas, abaci, Bukała, Bigos, satrapa, kortyzani, Lewek, alastor, profes, Ares, Kobak, lord, Labuda, Prada, Gleba, Trak, Sas, Able, Baka, Dubik, Cortez, Carsten, Rokita, Sornat, stenograf, Asser, Roth, celnik, Surmiak, Kern, Elgar, Edgar, Agis, tutnar, Ozawa, Kret, ultrasi, Bulak, Rurak, Dubois, Eldar, Bator, askar, Buksa, Kulig, Annamita, Rodak, Edyp, Agha, psor, Engel, Orione, Rota, King, asan, agregat, symplak, Ezaw, Ed, Toni, Gamrat, Artur, Ammon, organik, radny, doyeni, woj, agnat, sir, tatul, Pini, Drabik, modele, Depa, Dowi, Losey, Lesik, Sobota, Lis, pajac, Rolnik, Topor, Knysak, Turner, Olsen, Rabin, Mizak, Lada, Tyl, kady, filareci, patron, etatowi, Sasak, lemani, Wontor, Wanat, esbol, logik, Seliga, Mały, Waluk, Komsta, radiesteci, Nata, lump, Radlak, Stefani, Samsel, Tibor, Deptuła, baje, rabini, 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menago, Lot, nosal, Taine, burzca, Goraj, Arab, mer, Aziz, Diaz, Sabini, Wasik, Cała, skin, markiz, Dor, meni, Barker, asura, niemy, Telle, Wrona, Bruno, Rejtan, Rasała, Pacino, Mali, Matracki, pedle, Fels, Linke, pop, kto, Imre, Murat, Jarre, kibice, Lawson, Jarota, rep, Mikina, Morse, zmarli, grum, italogrek, Arkady, Diks, Róg, Anatole, Hutu, Bomba, banici, buce, Iwo, Rut, ramol, Hawn, Hajnos, Nik, taboryci, Perec, Lem, nomad, Pacak, Miles, eon, nowi, Jul, Ber, Sujak, Kabat, cap, Sica, jog, kaid, dias, Knap, Noel, Sumer, Gleb, bard, Filak, samuraj i pop.

Sipping Rum: Some New Palindromes