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)