corpus = Corpus(corpus_name="SZ_2009_14")
query = r'@0[lemma="Angela"]? @1[lemma="Merkel"] [word="\("] @2[lemma="CDU"] [word="\)"]'
result = corpus.query(query)
concordance = corpus.concordance(result)
print(concordance.breakdown)
print(concordance.size)
print(concordance.lines([567792]))
corpus = Corpus(corpus_name="SZ_2009_14")
query = '[lemma="Angela"]? [lemma="Merkel"] [word="\\("] [lemma="CDU"] [word="\\)"]'
result = corpus.query(query, s_meta=['text_id'])
collocates = corpus.collocates(result)
print(collocates.show(window=5, order="log_likelihood").head())
@pytest.mark.readme_keywords
def test_keywords_sz():
meta = read_csv(
"/home/ausgerechnet/corpora/cwb/upload/efe/sz-2009-14.tsv.gz",
sep="\t",
index_col=0,
dtype=str)
ids = set(meta.loc[(meta['ressort'] == "Panorama")
& (meta['month'] == '201103')].index.values)
meta['s_id'] = meta.index
from ccc import Corpus
corpus = Corpus("BREXIT_V20190522_DEDUP")
matches = corpus.query('[lemma="Merkel"%cd]', context=20, s_context='tweet')
collocates = corpus.collocates(matches, p_query="lemma")
df = collocates.show(order='log_likelihood')
df.to_csv("BREXIT_merkel-ll.tsv", sep="\t")
