I need a language detection script. I tried Textblob library which right now give me the two letter abbreviation of the language. How can I get the complete language expansion?
This detects the language with two letter abbreviation of the language
from textblob import TextBlob
b = TextBlob("cómo estás")
language = b.detect_language()
print(language)
Actual Results : es
Expected Results : Spanish
I have the list of language and their abbreviation from this link
https://developers.google.com/admin-sdk/directory/v1/languages
The code you're using gives you a two-letter abbreviation that conforms to the ISO 639-2 international protocol. You could look up a list of these correspondences (e.g. this page and rig up a method to just input one and output the other, but given you're programming in python, someone's already done that for you.
I recommend pycountry - a general-purpose library for this type of task that also contains a number of other standards. Example of using it for this problem:
from textblob import TextBlob
import pycountry
b = TextBlob("நீங்கள் எப்படி இருக்கிறீர்கள்")
iso_code = b.detect_language()
# iso_code = "ta"
language = pycountry.languages.get(alpha_2=iso_code)
# language = Language(alpha_2='ta', alpha_3='tam', name='Tamil', scope='I', type='L')
print(language.name)
and that prints Tamil, as expected. Same works for Spanish:
>>> pycountry.languages.get(alpha_2='es').name
'Spanish'
and probably most other languages you'll encounter in whatever it is you're doing..
I've been trying
on how to get synonyms for the words I pass. That's an easy piece of cake for wordnet to do. However I'm trying and failing with bigrams
viz. 'i tried', 'horrible food', 'people go'
I looked for these
to learn more on sentiwordnet, since I read documentation. Perhaps, there are no examples on how to use it.
Went on for source too, yet I'm here.
I wrote
the code for doing it in foll. way, point out what needs correction:
from nltk.corpus import sentiwordnet as swn
sentisynset = swn.senti_synset('so horrible')
print(sentisynset)
Well, this clear returns ValeError, not sure why though.
Also, I tried this:
from nltk.corpus.reader.lin import LinThesaurusCorpusReader as ltcr
synon = ltcr.synonyms(ngram='so horrible')
print(synon)
and this returns TypeError, asking me for self parameter to be filled.
I'm trying to use Affect WordNet to use the different emotions that are implemented within, like: anger, dislike, fear negative-fear, joy, sad etc.
I found a code sample for that on the library: https://github.com/clemtoy/WNAffect
from wnaffect import WNAffect
import nltk
nltk.download('dict')
wna = WNAffect('wordnet-1.6/', 'wn-domains-3.2/')
emo = wna.get_emotion('angry', 'JJ')
print(emo)
I tested it and I got this error:
Resource dict not found. Please use the NLTK Downloader to obtain
the resource:
import nltk nltk.download('dict')
I tried to use NLTK downloader to download it, but nothing there !
A similar question was asked before here: Extract “emotion words” / affect words from english corpus?
but they solved it by using sentiwordNet to retrieve the negative and the positive scores !
I have a very simple CherryPy webservice that I hope will be the foundation of a larger project, however, I need to get NLTK to work the way I want.
My python script imports NLTK and uses the collocation (bigram) function of NLTK, to do some analysis on pre-loaded data.
I have a couple of questions:
1) Why is the program not returning the collocations to my browser, but only to my console?.
2) Why if I am specifying from nltk.book import text4, the program imports the whole set of sample books (text1 to text9)?
Please, keep in mind that I am a newbie, so the answer might be in front of me, but I don't see it.
Main question: How do I pass the collocation results to the browser (webservice), instead of console?
Thanks
import cherrypy
import nltk
from nltk.book import text4
class BiGrams:
def index(self):
return text4.collocations(num=20)
index.exposed = True
cherrypy.quickstart(BiGrams())
I have been doing some work with Moby Dick and I stumbled on the answer to the question of importing just one specific text the other day:
>>>import nltk.corpus
>>>from nltk.text import Text
>>>moby = Text(nltk.corpus.gutenberg.words('melville-moby_dick.txt'))
Thus, all you really need is the fileid in order to assign the text of that file to your new Text object. Be careful, though, because only "literary" sources are in the gutenberg.words directory.
Anyway, for help with finding file ids for gutenberg, after import nltk.corpus above, you can use the following command:
>>> nltk.corpus.gutenberg.fileids()
['austen-emma.txt', 'austen-persuasion.txt', 'austen-sense.txt', 'bible-kjv.txt', 'blake-poems.txt', 'bryant-stories.txt', 'burgess-busterbrown.txt', 'carroll-alice.txt', 'chesterton-ball.txt', 'chesterton-brown.txt', 'chesterton-thursday.txt', 'edgeworth-parents.txt', 'melville-moby_dick.txt', 'milton-paradise.txt', 'shakespeare-caesar.txt', 'shakespeare-hamlet.txt', 'shakespeare-macbeth.txt', 'whitman-leaves.txt']
This still doesn't answer the question for your specific corpus, the inaugural addresses, however. For that answer, I found this MIT paper: http://web.mit.edu/6.863/www/fall2012/nltk/ch2-3.pdf
(I recommend it to anyone beginning to work with nltk texts because it talks about grabbing all kinds of textual data for analysis). The answer to getting the inaugural address fileids comes on page 6 (edited a bit):
>>> nltk.corpus.inaugural.fileids()
['1789-Washington.txt', '1793-Washington.txt', '1797-Adams.txt', '1801-Jefferson.txt', '1805-Jefferson.txt', '1809-Madison.txt', '1813-Madison.txt', '1817-Monroe.txt', '1821-Monroe.txt', '1825-Adams.txt', '1829-Jackson.txt', '1833-Jackson.txt', '1837-VanBuren.txt', '1841-Harrison.txt', '1845-Polk.txt', '1849-Taylor.txt', '1853-Pierce.txt', '1857-Buchanan.txt', '1861-Lincoln.txt', '1865-Lincoln.txt', '1869-Grant.txt', '1873-Grant.txt', '1877-Hayes.txt', '1881-Garfield.txt', '1885-Cleveland.txt', '1889-Harrison.txt', '1893-Cleveland.txt', '1897-McKinley.txt', '1901-McKinley.txt', '1905-Roosevelt.txt', '1909-Taft.txt', '1913-Wilson.txt', '1917-Wilson.txt', '1921-Harding.txt', '1925-Coolidge.txt', '1929-Hoover.txt', '1933-Roosevelt.txt', '1937-Roosevelt.txt', '1941-Roosevelt.txt', '1945-Roosevelt.txt', '1949-Truman.txt', '1953-Eisenhower.txt', '1957-Eisenhower.txt', '1961-Kennedy.txt', '1965-Johnson.txt', '1969-Nixon.txt', '1973-Nixon.txt', '1977-Carter.txt', '1981-Reagan.txt', '1985-Reagan.txt', '1989-Bush.txt', '1993-Clinton.txt', '1997-Clinton.txt', '2001-Bush.txt', '2005-Bush.txt', '2009-Obama.txt']
Thus, you should be able to import specific inaugural addresses as Texts (assuming you did "from nltk.text import Text" above) or you can work with them using the "inaugural" identifier imported above. For example, this works:
>>>address1 = Text(nltk.corpus.inaugural.words('2009-Obama.txt'))
In fact, you can treat all inaugural addresses as one document by calling inaugural.words without any arguments, as in the following example from this page:
>>>len(nltk.corpus.inaugural.words())
OR
addresses = Text(nltk.corpus.inaugural.words())
I remembered reading this thread a month ago when trying to answer this question myself, so perhaps this information, if coming late, will be helpful to someone somewhere.
(This is my first contribution to Stack Overflow. I've been reading for months and never had anything useful to add until now. Just want to say generally 'thanks to everyone for all the help.')
My guess is that what you get back from the collocations() call is not a string, and that you need to serialize it. Try this instead:
import cherrypy
import nltk
from nltk.book import text4
import simplejson
class BiGrams:
def index(self):
c = text4.collocations(num=20)
return simplejson.dumps(c)
index.exposed = True
cherrypy.quickstart(BiGrams())
Take a look at the source code (http://code.google.com/p/nltk/source/browse/trunk/nltk/) and you'll learn a lot (I know I did).
1) Collocations is returning to your console because that's what it is supposed to do.
help(text4.collocations)
will give you:
Help on method collocations in module nltk.text:
collocations(self, num=20, window_size=2) method of nltk.text.Text instance
Print collocations derived from the text, ignoring stopwords.
#seealso: L{find_collocations}
#param num: The maximum number of collocations to print.
#type num: C{int}
#param window_size: The number of tokens spanned by a collocation (default=2)
#type window_size: C{int}
Browse the source in text.py and you'll find the method for collocations is pretty straight-forward.
2) Importing nltk.book loads each text. You could could just grab the bits you need from book.py and write a method that only loads the inaugural addresses.
What is the best way to approach writing a program in Python to translate English words and/or phrases into other languages?
AJAX Language API
This is an incredibly difficult problem -- language is very very very complicated. Think about all the things you'd have to do -- parse the phrase, work out what the words mean, translate them. That's probably not idiomatic so you'll need special cases for different syntaxes. Many, many special cases. You'll need to work out the syntax of the foreign language if it differs from English -- "the big green ball" goes to "the ball big green" in Spanish, for instance.
Don't reinvent the wheel. Google provide an API to their translation service, which has undoubtedly had many many clever people thinking really quite hard about it.
I think you should look into the Google Translate API. Here is a library implemented specifically for this purpose in python.
the simplest way to do this is to make a dictionary that matches one language's words to another language's words. However, this is extremely silly and would not take into account grammar at all and it would literally take a very long time to create a translator, especially if you plan to use it for multiple languages. If grammar is not important to you (for example, if you were creating your own language for a game or story that doesn't have grammar different from english) than you could get away with using dictionaries and simply having a function look for a requested match in the dictionary
# command : pip install mtranslate
from mtranslate import translate
>>> from mtranslate import translate
>>> translate("Tranalating to kannada language (my mother tongue) ", to_language = "kn")
'ಕನ್ನಡ ಭಾಷೆಗೆ ಅನುವಾದ (ನನ್ನ ಮಾತೃಭಾಷೆ)'
You can use Goslate Package for that
its very easy to use
example
import goslate
print(goslate.Goslate().translate('hello world', 'ar'))
now first argument is text you want to translate and second argument is in which language you want to translate.
i hope you will find the answer usefull
# Please install Microsoft translate using >> pip install translate
from translate import Translator
class clsTranslate():
def translateText(self, strString, strTolang):
self.strString = strString
self.strTolang = strTolang
translator = Translator(to_lang=self.strTolang)
translation = translator.translate(self.strString)
return (str(translation))
# Create a Class object and call the Translate function
# Pass the language as a parameter to the function, de: German zh: Chinese etc
objTrans=clsTranslate()
strTranslatedText= objTrans.translateText('Howare you', 'de')
print(strTranslatedText)
It's very very easy if you use deep-translator! Here's the source code(make sure to install deep-translator module):
from deep_translator import GoogleTranslator
import time
def start():
while True:
def translate():
line_to_translate = input('Which line/phrase/word you want to translate?\n')
to_lang = input('In which language you want to translate it?\n')
to_lang = to_lang.lower()
translation = GoogleTranslator(source='auto', target=to_lang).translate(text=line_to_translate)
return translation
time.sleep(1 sec)
esc = (input("Enter 'q' to exit and 'r' to restart.\n"))
while True:
if esc.lower() in {'q', 'r'}:
break
else:
print('Please enter a valid Option!!')
time.sleep(1)
esc = (input("Enter 'q' to exit and 'r' to restart.\n"))
if esc.lower() == 'q':
return
elif esc.lower() == 'r':
pass
start()
# command : pip install mtranslate
from mtranslate import translate
>>> from mtranslate import translate
>>> translate("Tranalating to kannada language (my mother tongue) ", to_language = "kn")
'ಕನ್ನಡ ಭಾಷೆಗೆ ಅನುವಾದ (ನನ್ನ ಮಾತೃಭಾಷೆ)'