推荐 ：快速掌握spacy在python中进行自然语言处理（附代码&链接）

import spacynlp = 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 Truerain rain NOUN Falsein in ADP TrueSpain Spain PROPN Falsefalls fall VERB Falsemainly mainly ADV Falseon on ADP Truethe the DET Trueplain 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, style="dep")

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 warningswarnings.filterwarnings("ignore")

from bs4 import BeautifulSoupimport requestsimport 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 0.9482039305669306asl bsd 0.9391555350757145bsd 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 JobsSteve WozniakApple ComputerJanuaryCupertinoCalifornia

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

displacy.render(doc, style="ent")

import?nltknltk.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 WordnetAnnotatorprint("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)