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Get python to look for words and output them [closed]

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I've been looking into developing a discord bot that can reply to messages by reading their contents and checking if they appear in a list.
My problem is, I need to find a reliable way of getting python to look for certain words from a text, see if they appear in the given list and output the words that are detected.
I've managed to get it working somewhat myself with the following code:
if any(word in text in list):
print("Word Spotted")
I would really apreciate some help.

Here's some code that does something like what you're describing. But really it sounds like you need to spend a significant amount of time working through some basic Python tutorials before you will be able to implement this.
import re
key_words = set(['foo', 'bar', 'baz'])
typed_str = 'You are such a Foo BAR!'
print key_words & set(re.findall('[a-z]+', typed_str.lower()))

I'm not sure exactly what is being asked but somethings to consider (in no particular order) if you are building a bot that is taking in raw user input.
capitalization sensitivity
spell check
understanding intent simplistically
If your environment allows access to libraries you might consider checking out TextBlob. The following commands will give you the functionality needed for the example below.
pip install textblob
python -m textblob.download_corpora
core function
from textblob import TextBlob, Word
import copy
def score_intent(rawstring,keywords,weights=None,threshold=0.01,debug=False):
"""
rawstring: string of text with words that you want to detect
keywords: list of words that you are looking for
weights: (optional) dictionary with relative weights of words you want
threshold: spellcheck confidence threshold
debug: boolean for extra print statements to help debug
"""
allwords = TextBlob(rawstring).words
allwords = [w.upper() for w in allwords]
keywords = [k.upper() for k in keywords]
processed_input_as_list = spellcheck_subject_matter_specific(rawstring,keywords,threshold=threshold,debug=debug)
common_words = intersection(processed_input_as_list,keywords)
intent_score = len(common_words)
if weights:
for special_word in weights.keys():
if special_word.upper() in common_words:
# the minus one is so we dont double count a word.
intent_score = intent_score + weights[special_word] -1
if debug:
print "intent score: %s" %intent_score
print "words of interest found in text: {}".format(common_words)
# you could return common_words and score intent based on the list.
# return common_words, intent_score
return common_words
utilities for intersection & spellchecking
def intersection(a,b):
"""
a and b are lists
function returns a list that is the intersection of the two
"""
return list(set(a)&set(b))
def spellcheck_subject_matter_specific(rawinput,subject_matter_vector,threshold=0.01,capitalize=True,debug=False):
"""
rawinput: all the text that you want to check for spelling
subject_matter_vector: only the words that are worth spellchecking for (since the function can be sort of sensitive it might correct words that you don't want to correct)
threshold: the spell check confidence needed to update the word to the correct spelling
capitalize: boolean determining if you want the return string to be capitalized.
"""
new_input = copy.copy(rawinput)
for w in TextBlob(rawinput).words:
spellchecked_vec = w.spellcheck()
if debug:
print "Word: %s" %w
print "Spellchecked Guesses & Confidences: %s" %spellchecked_vec
print "Only spellchecked confidences greater than {} and in this list {} will be included".format(threshold,subject_matter_vector)
corrected_words = [z[0].upper() for z in spellchecked_vec if z[1] > threshold]
important_words = intersection(corrected_words,subject_matter_vector)
for new_word in important_words:
new_input = new_input + ' ' + new_word
inputBlob = TextBlob(new_input)
processed_input = inputBlob.words
if capitalize:
processed_input = [word.upper() for word in processed_input]
return processed_input
Usage Example
discord_str = "Hi, i want to talk about codee and pYtHon"
words2detect = ["python","code"]
score_intent(rawstring=discord_str,keywords=words2detect,threshold=0.01,debug=True)
output
intent score: 2
words of interest found in text: ['PYTHON', 'CODE']

Merging or reversing n-grams to a single string

How do I merge the bigrams below to a single string?
_bigrams=['the school', 'school boy', 'boy is', 'is reading']
_split=(' '.join(_bigrams)).split()
_newstr=[]
_filter=[_newstr.append(x) for x in _split if x not in _newstr]
_newstr=' '.join(_newstr)
print _newstr
Output:'the school boy is reading'....its the desired output but the approach is too long and not quite efficient given the large size of my data. Secondly, the approach would not support duplicate words in the final string ie 'the school boy is reading, is he?'. Only one of the 'is' will be permitted in the final string in this case.
Any suggestions on how to make this work better? Thanks.

# Multi-for generator expression allows us to create a flat iterable of words
all_words = (word for bigram in _bigrams for word in bigram.split())
def no_runs_of_words(words):
"""Takes an iterable of words and returns one with any runs condensed."""
prev_word = None
for word in words:
if word != prev_word:
yield word
prev_word = word
final_string = ' '.join(no_runs_of_words(all_words))
This takes advantage of generators to lazily evaluate and not keep the entire set of words in memory at the same time until generating the one final string.

If you really wanted a oneliner, something like this could work:
' '.join(val.split()[0] for val in (_bigrams)) + ' ' + _bigrams[-1].split()[-1]

Would this do it? It does simply take the first word up to the last entry
_bigrams=['the school', 'school boy', 'boy is', 'is reading']
clause = [a.split()[0] if a != _bigrams[-1] else a for a in _bigrams]
print ' '.join(clause)
Output
the school boy is reading
However, concerning performance probably Amber's solution is a good option

identifying strings which cant be spelt in a list item

I have a list
['mPXSz0qd6j0 youtube ', 'lBz5XJRLHQM youtube ', 'search OpHQOO-DwlQ ',
'sachin 47427243 ', 'alex smith ', 'birthday JEaM8Lg9oK4 ',
'nebula 8x41n9thAU8 ', 'chuck norris ',
'searcher O6tUtqPcHDw ', 'graham wXqsg59z7m0 ', 'queries K70QnTfGjoM ']
Is there some way to identify the strings which can't be spelt in the list item and remove them?

You can use, e.g. PyEnchant for basic dictionary checking and NLTK to take minor spelling issues into account, like this:
import enchant
import nltk
spell_dict = enchant.Dict('en_US') # or whatever language supported
def get_distance_limit(w):
'''
The word is considered good
if it's no further from a known word than this limit.
'''
return len(w)/5 + 2 # just for example, allowing around 1 typo per 5 chars.
def check_word(word):
if spell_dict.check(word):
return True # a known dictionary word
# try similar words
max_dist = get_distance_limit(word)
for suggestion in spell_dict.suggest(word):
if nltk.edit_distance(suggestion, word) < max_dist:
return True
return False
Add a case normalisation and a filter for digits and you'll get a pretty good heuristics.

It is entirely possible to compare your list members to words that you don't believe to be valid for your input.
This can be done in many ways, partially depending on your definition of "properly spelled" and what you end up using for a comparison list. If you decide that numbers preclude an entry from being valid, or underscores, or mixed case, you could test for regex matching.
Post regex, you would have to decide what a valid character to split on should be. Is it spaces (are you willing to break on 'ad hoc' ('ad' is an abbreviation, 'hoc' is not a word))? Is it hyphens (this will break on hyphenated last names)?
With these above criteria decided, it's just a decision of what word, proper name, and common slang list to use and a list comprehension:
word_list[:] = [term for term in word_list if passes_my_membership_criteria(term)]
where passes_my_membership_criteria() is a function that contains the rules for staying in the list of words, returning False for things that you've decided are not valid.

Discovering Poetic Form with NLTK and CMU Dict

Edit: This code has been worked on and released as a basic module: https://github.com/hyperreality/Poetry-Tools
I'm a linguist who has recently picked up python and I'm working on a project which hopes to automatically analyze poems, including detecting the form of the poem. I.e. if it found a 10 syllable line with 0101010101 stress pattern, it would declare that it's iambic pentameter. A poem with 5-7-5 syllable pattern would be a haiku.
I'm using the following code, part of a larger script, but I have a number of problems which are listed below the program:
corpus in the script is simply the raw text input of the poem.
import sys, getopt, nltk, re, string
from nltk.tokenize import RegexpTokenizer
from nltk.util import bigrams, trigrams
from nltk.corpus import cmudict
from curses.ascii import isdigit
...
def cmuform():
tokens = [word for sent in nltk.sent_tokenize(corpus) for word in nltk.word_tokenize(sent)]
d = cmudict.dict()
text = nltk.Text(tokens)
words = [w.lower() for w in text]
regexp = "[A-Za-z]+"
exp = re.compile(regexp)
def nsyl(word):
lowercase = word.lower()
if lowercase not in d:
return 0
else:
first = [' '.join([str(c) for c in lst]) for lst in max(d[lowercase])]
second = ''.join(first)
third = ''.join([i for i in second if i.isdigit()]).replace('2', '1')
return third
#return max([len([y for y in x if isdigit(y[-1])]) for x in d[lowercase]])
sum1 = 0
for a in words:
if exp.match(a):
print a,nsyl(a),
sum1 = sum1 + len(str(nsyl(a)))
print "\nTotal syllables:",sum1
I guess that the output that I want would be like this:
1101111101
0101111001
1101010111
The first problem is that I lost the line breaks during the tokenization, and I really need the line breaks to be able to identify form. This should not be too hard to deal with though. The bigger problems are that:
I can't deal with non-dictionary words. At the moment I return 0 for them, but this will confound any attempt to identify the poem, as the syllabic count of the line will probably decrease.
In addition, the CMU dictionary often says that there is stress on a word - '1' - when there is not - '0 - . Which is why the output looks like this: 1101111101, when it should be the stress of iambic pentameter: 0101010101
So how would I add some fudging factor so the poem still gets identified as iambic pentameter when it only approximates the pattern? It's no good to code a function that identifies lines of 01's when the CMU dictionary is not going to output such a clean result. I suppose I'm asking how to code a 'partial match' algorithm.

Welcome to stack overflow. I'm not that familiar with Python, but I see you have not received many answers yet so I'll try to help you with your queries.
First some advice: You'll find that if you focus your questions your chances of getting answers are greatly improved. Your post is too long and contains several different questions, so it is beyond the "attention span" of most people answering questions here.
Back on topic:
Before you revised your question you asked how to make it less messy. That's a big question, but you might want to use the top-down procedural approach and break your code into functional units:
split corpus into lines
For each line: find the syllable length and stress pattern.
Classify stress patterns.
You'll find that the first step is a single function call in python:
corpus.split("\n");
and can remain in the main function but the second step would be better placed in its own function and the third step would require to be split up itself, and would probably be better tackled with an object oriented approach. If you're in academy you might be able to convince the CS faculty to lend you a post-grad for a couple of months and help you instead of some workshop requirement.
Now to your other questions:
Not loosing line breaks: as #ykaganovich mentioned, you probably want to split the corpus into lines and feed those to the tokenizer.
Words not in dictionary/errors: The CMU dictionary home page says:
Find an error? Please contact the developers. We will look at the problem and improve the dictionary. (See at bottom for contact information.)
There is probably a way to add custom words to the dictionary / change existing ones, look in their site, or contact the dictionary maintainers directly.
You can also ask here in a separate question if you can't figure it out. There's bound to be someone in stackoverflow that knows the answer or can point you to the correct resource.
Whatever you decide, you'll want to contact the maintainers and offer them any extra words and corrections anyway to improve the dictionary.
Classifying input corpus when it doesn't exactly match the pattern: You might want to look at the link ykaganovich provided for fuzzy string comparisons. Some algorithms to look for:
Levenshtein distance: gives you a measure of how different two strings are as the number of changes needed to turn one string into another. Pros: easy to implement, Cons: not normalized, a score of 2 means a good match for a pattern of length 20 but a bad match for a pattern of length 3.
Jaro-Winkler string similarity measure: similar to Levenshtein, but based on how many character sequences appear in the same order in both strings. It is a bit harder to implement but gives you normalized values (0.0 - completely different, 1.0 - the same) and is suitable for classifying the stress patterns. A CS postgrad or last year undergrad should not have too much trouble with it ( hint hint ).
I think those were all your questions. Hope this helps a bit.

To preserve newlines, parse line by line before sending each line to the cmu parser.
For dealing with single-syllable words, you probably want to try both 0 and 1 for it when nltk returns 1 (looks like nltk already returns 0 for some words that would never get stressed, like "the"). So, you'll end up with multiple permutations:
1101111101
0101010101
1101010101
and so forth. Then you have to pick ones that look like a known forms.
For non-dictionary words, I'd also fudge it the same way: figure out the number of syllables (the dumbest way would be by counting the vowels), and permutate all possible stresses. Maybe add some more rules like "ea is a single syllable, trailing e is silent"...
I've never worked with other kinds of fuzzying, but you can check https://stackoverflow.com/questions/682367/good-python-modules-for-fuzzy-string-comparison for some ideas.

This is my first post on stackoverflow.
And I'm a python newbie, so please excuse any deficits in code style.
But I too am attempting to extract accurate metre from poems.
And the code included in this question helped me, so I post what I came up with that builds on that foundation. It is one way to extract the stress as a single string, correct with a 'fudging factor' for the cmudict bias, and not lose words that are not in the cmudict.
import nltk
from nltk.corpus import cmudict
prondict = cmudict.dict()
#
# parseStressOfLine(line)
# function that takes a line
# parses it for stress
# corrects the cmudict bias toward 1
# and returns two strings
#
# 'stress' in form '0101*,*110110'
# -- 'stress' also returns words not in cmudict '0101*,*1*zeon*10110'
# 'stress_no_punct' in form '0101110110'
def parseStressOfLine(line):
stress=""
stress_no_punct=""
print line
tokens = [words.lower() for words in nltk.word_tokenize(line)]
for word in tokens:
word_punct = strip_punctuation_stressed(word.lower())
word = word_punct['word']
punct = word_punct['punct']
#print word
if word not in prondict:
# if word is not in dictionary
# add it to the string that includes punctuation
stress= stress+"*"+word+"*"
else:
zero_bool=True
for s in prondict[word]:
# oppose the cmudict bias toward 1
# search for a zero in array returned from prondict
# if it exists use it
# print strip_letters(s),word
if strip_letters(s)=="0":
stress = stress + "0"
stress_no_punct = stress_no_punct + "0"
zero_bool=False
break
if zero_bool:
stress = stress + strip_letters(prondict[word][0])
stress_no_punct=stress_no_punct + strip_letters(prondict[word][0])
if len(punct)>0:
stress= stress+"*"+punct+"*"
return {'stress':stress,'stress_no_punct':stress_no_punct}
# STRIP PUNCTUATION but keep it
def strip_punctuation_stressed(word):
# define punctuations
punctuations = '!()-[]{};:"\,<>./?##$%^&*_~'
my_str = word
# remove punctuations from the string
no_punct = ""
punct=""
for char in my_str:
if char not in punctuations:
no_punct = no_punct + char
else:
punct = punct+char
return {'word':no_punct,'punct':punct}
# CONVERT the cmudict prondict into just numbers
def strip_letters(ls):
#print "strip_letters"
nm = ''
for ws in ls:
#print "ws",ws
for ch in list(ws):
#print "ch",ch
if ch.isdigit():
nm=nm+ch
#print "ad to nm",nm, type(nm)
return nm
# TESTING results
# i do not correct for the '2'
line = "This day (the year I dare not tell)"
print parseStressOfLine(line)
line = "Apollo play'd the midwife's part;"
print parseStressOfLine(line)
line = "Into the world Corinna fell,"
print parseStressOfLine(line)
"""
OUTPUT
This day (the year I dare not tell)
{'stress': '01***(*011111***)*', 'stress_no_punct': '01011111'}
Apollo play'd the midwife's part;
{'stress': "0101*'d*01211***;*", 'stress_no_punct': '010101211'}
Into the world Corinna fell,
{'stress': '01012101*,*', 'stress_no_punct': '01012101'}

All synonyms for word in python? [duplicate]

This question already has answers here:
How to get synonyms from nltk WordNet Python
(8 answers)
Closed 7 years ago.
The code to get the synonyms of a word in python is say:
from nltk.corpus import wordnet
dog = wordnet.synset('dog.n.01')
print dog.lemma_names
>>['dog', 'domestic_dog', 'Canis_familiaris']
However dog.n.02 gives different words. For any words i can't know how many words there may be. How can i return all of the synonyms for a word?

Using wn.synset('dog.n.1').lemma_names is the correct way to access the synonyms of a sense. It's because a word has many senses and it's more appropriate to list synonyms of a particular meaning/sense. To enumerate words with similar meanings, possibly you can also look at the hyponyms.
Sadly, the size of Wordnet is very limited so there are very few lemma_names available for each senses.
Using Wordnet as a dictionary/thesarus is not very apt per se, because it was developed as an inventory of sense/meaning rather than a inventory of words. However you can use access the a particular sense and several (not a lot) related words to the sense. One can use Wordnet as a:
Dictionary: given a word, what are the different meaning of the word
for i,j in enumerate(wn.synsets('dog')):
print "Meaning",i, "NLTK ID:", j.name
print "Definition:",j.definition
Thesarus: given a word, what are the different words for each meaning of the word
for i,j in enumerate(wn.synsets('dog')):
print "Meaning",i, "NLTK ID:", j.name
print "Definition:",j.definition
print "Synonyms:", ", ".join(j.lemma_names)
print
Ontology: given a word, what are the hyponyms (i.e. sub-types) and hypernyms (i.e. super-types).
for i,j in enumerate(wn.synsets('dog')):
print "Meaning",i, "NLTK ID:", j.name
print "Hypernyms:", ", ".join(list(chain(*[l.lemma_names for l in j.hypernyms()])))
print "Hyponyms:", ", ".join(list(chain(*[l.lemma_names for l in j.hyponyms()])))
print
[Ontology Output]
Meaning 0 NLTK ID: dog.n.01
Hypernyms words domestic_animal, domesticated_animal, canine, canid
Hyponyms puppy, Great_Pyrenees, basenji, Newfoundland, Newfoundland_dog, lapdog, poodle, poodle_dog, Leonberg, toy_dog, toy, spitz, pooch, doggie, doggy, barker, bow-wow, cur, mongrel, mutt, Mexican_hairless, hunting_dog, working_dog, dalmatian, coach_dog, carriage_dog, pug, pug-dog, corgi, Welsh_corgi, griffon, Brussels_griffon, Belgian_griffon
Meaning 1 NLTK ID: frump.n.01
Hypernyms: unpleasant_woman, disagreeable_woman
Hyponyms:
Meaning 2 NLTK ID: dog.n.03
Hypernyms: chap, fellow, feller, fella, lad, gent, blighter, cuss, bloke
Hyponyms:
Meaning 3 NLTK ID: cad.n.01
Hypernyms: villain, scoundrel
Hyponyms: perisher

Note this other answer:
>>> wn.synsets('small')
[Synset('small.n.01'),
Synset('small.n.02'),
Synset('small.a.01'),
Synset('minor.s.10'),
Synset('little.s.03'),
Synset('small.s.04'),
Synset('humble.s.01'),
Synset('little.s.07'),
Synset('little.s.05'),
Synset('small.s.08'),
Synset('modest.s.02'),
Synset('belittled.s.01'),
Synset('small.r.01')]
Keep in mind that in your code you were trying to get the lemmas, but that's one level too deep for what you want. The synset level is about meaning, while the lemma level gives you words. In other words:
In WordNet (and I’m speaking of English WordNet here, though I think
the ones in other langauges are similarly organized) a lemma has
senses. Specifically, a lemma (that is, a base word form that is
indexed in WordNet) has exactly as many senses as the number of
synsets that it participates in. Conversely, and as you say, synsets
contain one more more lemmas, which means that multiple lemmas (words)
can represent the same sense, or meaning.
Also have a look at the NLTK's WordNet how to for a few more ways of exploring around a meaning or a word.

The documentation suggests
wordnet.synsets('dog')
to get all synsets for dog.