This document discusses natural language processing tasks related to analyzing fictional languages from the book series A Song of Ice and Fire. It presents code samples in Python using the NLTK library to process text samples in Dothraki, Astapori Valyrian, and High Valyrian: cleaning and tokenizing text, calculating word frequencies, and extracting phonological features to compare across the languages. It also analyzes a sample of Assamese text to determine positional restrictions and frequency of certain sounds. The document concludes with proposals for further work incorporating the phonological features into language classifiers.

1. Is that Dothraki or Valyrian?
and other NLP tasks with Python and NLTK
Charlie Redmon | SupStat, Inc.
August 18, 2014

2. Dothraki

3. Astapori Valyrian

4. High Valyrian

5. Importing raw text
dothraki_f = codecs.open(
"/home/cr/Python/westeros/dothraki.txt",
encoding=’utf -8’)
dothraki_raw = dothraki_f.read ()
print dothraki_raw
Athchomar chomakaan , [zhey] khal vezhven. Azha
anhaan asshilat ... Itte oakah! Jadi , zhey Jora
Andahli. Khal vezhven. Ajjalan anha zalat vitiherat
yer hatif. Kash qoy qoyi thira disse. Hash shafka
zali addrivat mae , zhey Khaleesi? Ishish chare
...

6. Text processing: Cleaning
punct_re = re.compile(
ur’[. ,;:?! u2014u2019u2026 []] ’,
re.UNICODE)
dothraki_proc = punct_re.sub(’’, dothraki_raw)
dothraki_proc = dothraki_proc.lower ()
print dothraki_proc
athchomar chomakaan zhey khal vezhven azha anhaan
asshilat itte oakah jadi zhey jora andahli khal
vezhven ajjalan anha zalat vitiherat yer hatif kash
qoy qoyi thira disse
...

7. Text processing: Tokenizing
dothraki_tokens = re.split(ur’s+’, dothraki_proc)
dothraki_types = set(dothraki_tokens )
print dothraki_types
set([u’izzi ’, u’ale’, u’morea ’, u’vesazhao ’,
u’yeri ’, u’ishish ’, u’dalen ’, u’vesazhae ’, u’yera ’,
u’afisi ’, u’rhae ’, u’mawizzi ’, u’vee’, u’arrisse ’,
u’ti’, u’ven’, u’rizh ’, u’afichak ’, u’gache ’,
u’zigerek ’, u’zigereo ’, u’drivoe ’, u’maaz ’,
u’zigeree ’, u’ayyeyoon ’, u’maan ’, u’mahrazhi ’,
u’ma’, u’vos’, u’movekkhi ’, u’mahrazhis ’,
u’meshafka ’, u’qisi ’, u’sani ’, u’ville ’, u’vikeesi ’,
u’ifak ’, u’javrathi ’, u’zisa ’, u’chek ’, u’nem’,
...
])

8. Inspecting the lexical distribution in a text
dothraki_freqdist = FreqDist( dothraki_tokens)
print dothraki_freqdist
<FreqDist: u’anha ’: 50, u’vos’: 40, u’me’: 39,
u’ma’: 38, u’zhey ’: 29, u’mae’: 27, u’anni ’: 26,
u’hash ’: 23, u’yer’: 23, u’khal ’: 16,
u’khaleesi ’: 16, u’mori ’: 15, u’jin’: 13,
u’kisha ’: 12, u’nem’: 11, u’vo’: 11, u’che’: 10,
u’jini ’: 10, u’she’: 10, ... >
dothraki_freqdist .plot (20, cumulative=True)

9. CFD of Dothraki words
Top 10: anha, vos, me, ma, zhey, mae, anni, hash, yer, khal

10. Valyrian vocabulary distribution
Astapori Valyrian (Top 10):
ji, me, do, espo, si, mysa, eji, ez, ivetr´a, sa
High Valyrian (Top 10):
daor, se, issa, syt, ziry, hen, jem¯ele, lue, yne, avy

11. Feature 1: Consonant proportion
def c_prop(word ):
c_num = 0
for letter in u’bcdfgjklmnpqrstvxz u00f1 ’:
c_num += word.count(letter)
return c_num / len(word)
c_prop(u’zu016bgusy ’)
0.5

12. Word-internal consonant proportions across languages

13. Feature 2: Obstruent proportion
def obstruent_prop (word ):
obstruent_num = 0
for letter in u’bcdfgjkpqstvxz ’
obstruent_num += word.count(letter)
return obstruent_num / len(word)
obstruent_prop (u’u012blvi ’)
0.25

14. Word-internal obstruent proportions across languages

15. Feature 3: Coda presence
def c_coda(word ):
if word [-1] in u’bcdfgjklmnpqrstvxz u00f1 ’:
return 1
else:
return 0
def obstruent_coda (word ):
if word [-1] in u’bcdfgjkpqstvxz ’:
return 1
else:
return 0
c_coda(u’lysoon ’)
1
obstruent_coda (u’lysoon ’)
0

16. Mean coda consonant presence across languages

17. Mean coda obstruent presence across languages

18. Feature 4: Consonant clusters
regex = ur’[ bcdfghjklmnpqrstvxz u00f1]
[ bcdfghjklmnpqrstvxz u00f1 ]+’
def c_cluster(word ):
cc_set = re.findall(regex , word , re.UNICODE)
return len(cc_set)
c_cluster(u’avvirsosh ’)
3

19. Mean consonant cluster frequency across languages

20. Feature 5: Obstruent clusters
regex1 = ur’[bcdfghjkpqstvxz ][ bcdfghjkpqstvxz ]+’
def obs_cluster(word ):
oo_set = re.findall(regex1 , word , re.UNICODE)
return len(oo_set)
obs_cluster(u’avvirsosh ’)
2

21. Mean obstruent cluster frequency across languages

22. Feature 6: Vowel clusters
regex2 = ur’[ bcdfghjklmnpqrstvxz u00f1 ]+’
def v_cluster(word ):
v_set = re.split(regex2 , word , re.UNICODE)
vv_set = [v for v in v_set if len(v) > 1]
return len(vv_set)
v_cluster(u’haeshi ’)
1

23. Mean vowel cluster frequency across languages

24. Data from real languages

25. TDIL Assamese Corpus

26. TDIL Assamese Corpus

27. Assamese corpus files
directory = "/home/cr/Documents/NLPwP_pres/
TDIL_assamese_corpus_data "
os.listdir(directory)
[’subj_art2.txt’, ’subj_politics1 .txt’, ’lit3.txt’,
’drama.txt’, ’religion2.txt’, ’criticism2.txt’,
’criticism1.txt’, ’subj_science3.txt’,
’ref_encyclopaedia -entry.txt’, ’subj_science2.txt’,
’subj_social -studies.txt’, ’music.txt’, ’subj_art1.txt
’subj_science1.txt’, ’subj_art3.txt’, ’news.txt’,
’subj_sociology .txt’, ’criticism3.txt’, ’lit8.txt’,
’subj_history1.txt’, ’lit4.txt’, ’lit6.txt’, ’religion
’subj_law.txt’, ’lit7.txt’, ’religion1.txt’, ’criticis
’lit5.txt’, ’subj_math.txt’, ’lit1.txt’, ’subj_science
’subj_science_5 .txt’, ’subj_history2.txt’, ’lit2.txt’,
’subj_science4.txt’, ’letter.txt’]

28. Assamese sample: ‘lit5.txt’

29. Frequency of the sound /x/ in ’lit5.txt’
len(re.findall(ur’[ u09b6u09b7u09b8]’,
assamese_sample_raw , re.UNICODE ))
1313
len(re.findall(ur’u09b6 ’, assamese_sample_raw ,
re.UNICODE ))
298
len(re.findall(ur’u09b7 ’, assamese_sample_raw ,
re.UNICODE ))
195
len(re.findall(ur’u09b8 ’, assamese_sample_raw ,
re.UNICODE ))
820

30. Positional restrictions
Beginning a word:
len(re.findall(ur’b[ u09b6u09b7u09b8]’,
assamese_sample_raw , re.UNICODE ))
1129
Ending a word:
len(re.findall(ur’[ u09b6u09b7u09b8 ]b’,
assamese_sample_raw , re.UNICODE ))
895

31. Positional restrictions
Following /a/:
len(re.findall(ur’u09be [ u09b6u09b7u09b8]’,
assamese_sample_raw , re.UNICODE ))
57
Following /i/:
len(re.findall(ur’[ u09bfu09c0 ][ u09b6u09b7u09b8]’,
ssamese_sample_raw , re.UNICODE ))
70
Following /u/:
len(re.findall(ur’[ u09c1u09c2 ][ u09b6u09b7u09b8]’,
assamese_sample_raw , re.UNICODE ))
10

32. Further work
Incorporate segmental parameters into classifier (fix Unicode
issues with NLTK’s classify module)
Use classifier to predict assignment of random words from
Westeros to Dothraki, Astapori Valyrian, and High Valyrian
languages
Isolate most important word-internal parameters in
classification model (log-likelihood ranking in Naive Bayes
model)
Use full distributional account of select Assamese consonants
as priors in acoustic classification model

33. Thank you