import spacy	nlp = spacy.load("en_core_web_sm")

text = "The rain in Spain falls mainly on the plain."	doc = nlp(text)		for token in doc:	    print(token.text, token.lemma_, token.pos_, token.is_stop)

The the DET True	rain rain NOUN False	in in ADP True	Spain Spain PROPN False	falls fall VERB False	mainly mainly ADV False	on on ADP True	the the DET True	plain plain NOUN False	. . PUNCT False

import pandas as pd		cols = ("text", "lemma", "POS", "explain", "stopword")	rows = []		for t in doc:	    row = [t.text, t.lemma_, t.pos_, spacy.explain(t.pos_), t.is_stop]	    rows.append(row)		df = pd.DataFrame(rows, columns=cols)		df

from spacy import displacy		displacy.render(doc, )

text = "We were all out at the zoo one day, I was doing some acting, walking on the railing of the gorilla exhibit. I fell in. Everyone screamed and Tommy jumped in after me, forgetting that he had blueberries in his front pocket. The gorillas just went wild."		doc = nlp(text)		for sent in doc.sents:	    print(">", sent)

We were all out at the zoo one day, I was doing some acting, walking on the railing of the gorilla exhibit.	I fell in.	Everyone screamed and Tommy jumped in after me, forgetting that he had blueberries in his front pocket.	The gorillas just went wild.

for sent in doc.sents:	    print(">", sent.start, sent.end)

doc[48:54]	The gorillas just went wild.

token = doc[51]	print(token.text, token.lemma_, token.pos_)	went go VERB

import sysimport warnings	warnings.filter	warnings("ignore")

from bs4 import BeautifulSoup	import requests	import traceback		def get_text (url): 	    buf = []		    try:	        soup = BeautifulSoup(requests.get(url).text, "html.parser")		        for p in soup.find_all("p"): 	            buf.append(p.get_text())		        return "\n".join(buf)	    except: 	        print(traceback.format_exc())	        sys.exit(-1)

lic = {}	lic["mit"] = nlp(get_text("https://opensource.org/licenses/MIT"))	lic["asl"] = nlp(get_text("https://opensource.org/licenses/Apache-2.0"))	lic["bsd"] = nlp(get_text("https://opensource.org/licenses/BSD-3-Clause"))	for sent in lic["bsd"].sents:    print(">", sent)

> SPDX short identifier: BSD-3-Clause	> Note: This license has also been called the "New BSD License" or  "Modified BSD License"	> See also the 2-clause BSD License.	…

pairs = [	    ["mit", "asl"], 	    ["asl", "bsd"], 	    ["bsd", "mit"]	]		for a, b in pairs:	print(a, b, lic[a].similarity(lic[b]))

mit asl 0.9482039305669306	asl bsd 0.9391555350757145	bsd mit 0.9895838089575453

text = "Steve Jobs and Steve Wozniak incorporated Apple Computer on January 3, 1977, in Cupertino, California."	doc = nlp(text)		for chunk in doc.noun_chunks: 	    print(chunk.text)

Steve Jobs	Steve Wozniak	Apple Computer	January	Cupertino	California

for ent in doc.ents:   	print(ent.text, ent.label_)

displacy.render(doc, )

import nltk	nltk.download("wordnet")	[nltk_data] Downloading package wordnet to /home/ceteri/nltk_data...	[nltk_data] Package wordnet is already up-to-date!True

from spacy_wordnet.wordnet_annotator import WordnetAnnotator	print("before", nlp.pipe_names)	if "WordnetAnnotator" not in nlp.pipe_names:    nlp.add_pipe(WordnetAnnotator(nlp.lang), after="tagger")	print("after", nlp.pipe_names)before ['tagger', 'parser', 'ner']after ['tagger', 'WordnetAnnotator', 'parser', 'ner']

token = nlp("withdraw")[0]	token._.wordnet.synsets()

[Synset('withdraw.v.01'),	Synset('retire.v.02'),	Synset('disengage.v.01'),	Synset('recall.v.07'),	Synset('swallow.v.05'),	Synset('seclude.v.01'),	Synset('adjourn.v.02'),	Synset('bow_out.v.02'),	Synset('withdraw.v.09'),	Synset('retire.v.08'),	Synset('retreat.v.04'),	Synset('remove.v.01')]

token._.wordnet.lemmas()

[Lemma('withdraw.v.01.withdraw'),	Lemma('withdraw.v.01.retreat'),	Lemma('withdraw.v.01.pull_away'), 	Lemma('withdraw.v.01.draw_back'), 	Lemma('withdraw.v.01.recede'),	Lemma('withdraw.v.01.pull_back'), 	Lemma('withdraw.v.01.retire'),	…

token._.wordnet.wordnet_domains()

['astronomy',	'school',	'telegraphy',	'industry',	'psychology',	'ethnology',	'ethnology',	'administration',	'school',	'finance',	'economy',	'exchange',	'banking',	'commerce',	'medicine',	'ethnology', 	'university',	…

domains = ["finance", "banking"]	sentence = nlp("I want to withdraw 5,000 euros.")		enriched_sent = []		for token in sentence:	    # get synsets within the desired domains	    synsets = token._.wordnet.wordnet_synsets_for_domain(domains)		    if synsets:	       lemmas_for_synset = []		       for s in synsets:	           # get synset variants and add to the enriched sentence	           lemmas_for_synset.extend(s.lemma_names())	           enriched_sent.append("({})".format("|".join(set(lemmas_for_synset))))	    else:	        enriched_sent.append(token.text)		print(" ".join(enriched_sent))

I (require|want|need) to (draw_off|withdraw|draw|take_out) 5,000 euros .

import scattertext as st 	if "merge_entities" not in nlp.pipe_names:    	nlp.add_pipe(nlp.create_pipe("merge_entities")) 	if "merge_noun_chunks" not in nlp.pipe_names:    	nlp.add_pipe(nlp.create_pipe("merge_noun_chunks")) 	convention_df = st.SampleCorpora.ConventionData2012.get_data()	corpus = st.CorpusFromPandas(convention_df, 	category_col="party",                            	text_col="text",                            	nlp=nlp).build()

html = st.produce_scattertext_explorer(	    corpus,	    category="democrat",	    category_name="Democratic",	    not_category_name="Republican",	    width_in_pixels=1000,	    metadata=convention_df["speaker"]	)

from IPython.display import IFrame 		file_name = "foo.html" 	with open(file_name, "wb") as f:	     f.write(html.encode("utf-8")) 	     IFrame(src=file_name, width = 1200, height=700)