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How to find synonyms using the relations in wordnet

I am new to NLP, NLTK and Python. I am using wordnet to get the synonyms for a word in given sentence. I am using the below code to get the synonyms and lemma names of those words
synonyms = wordnet.synsets(w,pos)
lemmas.append(list( set(chain.from_iterable([w.lemma_names() for w in synonyms]))))
eg : wordnet.synsets("get",'v')
The lemma_names for this word "get" returns many things which are irrelevant for me.
My search string is "error getting the report". lemma_names has even "buzz off", "gets under one's skin" which are not correct for my statement.
So is there a way get synonyms which are relevant to the statement? is there any concept or algorithms that I can check for?

nltk "OMW" wordnet with Arabic language

I'm working on python/nltk with (OMW) wordnet specifically for The Arabic language. All the functions work fine with the English language yet I can't seem to be able to perform any of them when I use the 'arb' tag. The only thing that works great is extracting the lemma_names from a given Arabic synset.
The code below works fine with u'arb':
The output is a list of Arabic lemmas.
for synset in wn.synsets(u'عام',lang=('arb')):
for lemma in synset.lemma_names(u'arb'):
print lemma
When I try to perform the same logic as the code above with synset, definitions, example, hypernyms, I get an error which says:
TypeError: hyponyms() takes exactly 1 argument (2 given)
(if I supply the 'arb' flag) or
KeyError: u'arb'
This is one of the codes that will not work if I write synset.hyponyms(u'arb'):
for synset in wn.synsets(u'عام',lang=('arb')):
for hypo in synset.hyponyms(): #print the hyponyms in English not Arabic
print hypo
Does this mean that I can't get to use wn.all_synsets and other built-in functions to extract all the Arabic synsets, hypernyms, etc?

The nltk's Open Multilingual Wordnet has English names for all the synsets, since it is a multilingual database centered on the original English Wordnet. Synsets model meanings, hence they are language-independent and cannot be requested in a specific language. But each synset is linked to lemmas for the languages covered by the OMW. Once you have some synsets (original, hyponyms, etc.), just ask for the Arabic lemmas again:
>>> for synset in wn.synsets(u'عام',lang=('arb')):
... for hypo in synset.hyponyms():
... for lemma in hypo.lemmas("arb"):
... print(lemma)
...
Lemma('waft.v.01.إِنْبعث')
Lemma('waft.v.01.انبعث')
Lemma('waft.v.01.إنبعث_كالرائحة_العطرة')
Lemma('waft.v.01.إِنْدفع')
Lemma('waft.v.01.إِنْطلق')
Lemma('waft.v.01.انطلق')
Lemma('waft.v.01.حمل_بخفة')
Lemma('waft.v.01.دفع')
Lemma('calendar_year.n.01.سنة_شمْسِيّة')
Lemma('calendar_year.n.01.سنة_مدنِيّة')
Lemma('fiscal_year.n.01.سنة_ضرِيبِيّة')
Lemma('fiscal_year.n.01.سنة_مالِيّة')
In other words, the lemmas are multilingual, the synsets are not.

Getting a Large List of Nouns (or Adjectives) in Python with NLTK; or Python Mad Libs

Like this question, I am interested in getting a large list of words by part of speech (a long list of nouns; a list of adjectives) to be used programmatically elsewhere. This answer has a solution using the WordNet database (in SQL) format.
Is there a way to get at such list using the corpora/tools built into the Python NLTK. I could take a large bunch of text, parse it and then store the nouns and adjectives. But given the dictionaries and other tools built in, is there a smarter way to simply extract the words that are already present in the NLTK datasets, encoded as nouns/adjectives (whatever)?
Thanks.

It's worth noting that Wordnet is actually one of the corpora included in the NLTK downloader by default. So you could conceivably just use the solution you already found without having to reinvent any wheels.
For instance, you could just do something like this to get all noun synsets:
from nltk.corpus import wordnet as wn
for synset in list(wn.all_synsets('n')):
print synset
# Or, equivalently
for synset in list(wn.all_synsets(wn.NOUN)):
print synset
That example will give you every noun that you want and it will even group them into their synsets so you can try to be sure that they're being used in the correct context.
If you want to get them all into a list you can do something like the following (though this will vary quite a bit based on how you want to use the words and synsets):
all_nouns = []
for synset in wn.all_synsets('n'):
all_nouns.extend(synset.lemma_names())
Or as a one-liner:
all_nouns = [word for synset in wn.all_synsets('n') for word in synset.lemma_names()]

You should use the Moby Parts of Speech Project data. Don't be fixated on using only what is directly in NLTK by default. It would be little work to download the files for this and pretty easy to parse them with NLTK once loaded.

I saw a similar question earlier this week (can't find the link), but like I said then, I don't think maintaining a list of nouns/adjectives/whatever is a great idea. This is primarily because the same word can have different parts of speech, depending on the context.
However, if you are still dead set on using these lists, then here's how I would do it (I don't have a working NLTK install on this machine, but I remember the basics):
nouns = set()
for sentence in my_corpus.sents():
# each sentence is either a list of words or a list of (word, POS tag) tuples
for word, pos in nltk.pos_tag(sentence): # remove the call to nltk.pos_tag if `sentence` is a list of tuples as described above
if pos in ['NN', "NNP"]: # feel free to add any other noun tags
nouns.add(word)
Hope this helps

NLTK words lemmatizing

I am trying to do lemmatization on words with NLTK.
What I can find now is that I can use the stem package to get some results like transform "cars" to "car" and "women" to "woman", however I cannot do lemmatization on some words with affixes like "acknowledgement".
When using WordNetLemmatizer() on "acknowledgement", it returns "acknowledgement" and using .PorterStemmer(), it returns "acknowledg" rather than "acknowledge".
Can anyone tell me how to eliminate the affixes of words?
Say, when input is "acknowledgement", the output to be "acknowledge"

Lemmatization does not (and should not) return "acknowledge" for "acknowledgement". The former is a verb, while the latter is a noun. Porter's stemming algorithm, on the other hand, simply uses a fixed set of rules. So, your only way there is to change the rules at source. (NOT the right way to fix your problem).
What you are looking for is the derivationally related form of "acknowledgement", and for this, your best source is WordNet. You can check this online on WordNet.
There are quite a few WordNet-based libraries that you can use for this (e.g. in JWNL in Java). In Python, NLTK should be able to get the derivationally related form you saw online:
from nltk.corpus import wordnet as wn
acknowledgment_synset = wn.synset('acknowledgement.n.01')
acknowledgment_lemma = acknowledgment_synset.lemmas[1]
print(acknowledgment_lemma.derivationally_related_forms())
# [Lemma('admit.v.01.acknowledge'), Lemma('acknowledge.v.06.acknowledge')]

Using NLTK and WordNet; how do I convert simple tense verb into its present, past or past participle form?

Using NLTK and WordNet, how do I convert simple tense verb into its present, past or past participle form?
For example:
I want to write a function which would give me verb in expected form as follows.
v = 'go'
present = present_tense(v)
print present # prints "going"
past = past_tense(v)
print past # prints "went"

With the help of NLTK this can also be done. It can give the base form of the verb. But not the exact tense, but it still can be useful. Try the following code.
from nltk.stem.wordnet import WordNetLemmatizer
words = ['gave','went','going','dating']
for word in words:
print word+"-->"+WordNetLemmatizer().lemmatize(word,'v')
The output is:
gave-->give
went-->go
going-->go
dating-->date
Have a look at Stack Overflow question NLTK WordNet Lemmatizer: Shouldn't it lemmatize all inflections of a word?.

I think what you're looking for is the NodeBox::Linguistics library. It does exactly that:
print en.verb.present("gave")
>>> give

For Python3:
pip install pattern
then
from pattern.en import conjugate, lemma, lexeme, PRESENT, SG
print (lemma('gave'))
print (lexeme('gave'))
print (conjugate(verb='give',tense=PRESENT,number=SG)) # he / she / it
yields
give
['give', 'gives', 'giving', 'gave', 'given']
gives
thnks to #Agargara for pointing & authors of Pattern for their beautiful work, go support them ;-)
PS. To use most of pattern's functionality in python 3.7+, you might want to use the trick described here

JWI (the WordNet library by MIT) also has a stemmer (WordNetStemmer) which converts different morphological forms of a word like ("written", "writes", "wrote") to their base form. It seems it works only for nouns (like plurals) and verbs though.
Word Stemming in Java with WordNet and JWNL also shows how to do this kind of stemming using JWNL, another Java-based Wordnet library: