推荐 ：快速掌握spacy在python中进行自然语言处理（附代码&链接）

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)