- Talking about liberal professions, such as lawyer, requires tough ethical rules and qualities.
- När man talar om fria yrken såsom advokatyrket krävs hårda etiska normer och egenskaper.
- We think it is good that the directive is not just being extended to banks but also to other businesses or professions which might be abused for money laundering purposes, and here the problems associated with the liberal professions have been satisfactorily resolved in our view.
- Vi anser att det är bra att direktivet inte bara utvidgas till bankerna, utan också till andra branscher eller yrken, som man kan missbruka för penningtvätt, varvid de problem som hänger samman med de fria yrkena ur vår synpunkt kunde lösas på ett tillfredsställande sätt.
- Mr President, ladies and gentlemen, the members of the liberal professions - tax accountants, lawyers and notaries - are aware of the risk which money laundering poses to the social, financial and economic stability of a country and condemn any professional colleague who wittingly takes part in his client’s criminal activities.
- Företrädarna för de fria yrkena - skatterådgivare, advokater och notarier - är medvetna om risken som tvättning av pengar medför för staternas sociala och ekonomiska stabilitet och fördömer alla yrkeskolleger som avsiktligt deltar i sina uppdragsgivares brottsliga gärningar.
show query
SET search_path TO f9miniensv;
WITH
list AS (SELECT
t11.token_id AS t11,
t12.token_id AS t12,
t21.token_id AS t21,
t22.token_id AS t22,
r1.dep_id AS dep1,
r2.dep_id AS dep2
FROM
deprel r1
JOIN depstr s1 ON s1.dep_id = r1.dep_id
JOIN word_align a1 ON a1.wsource = r1.head AND a1.wsource < a1.wtarget
JOIN word_align a2 ON a2.wsource = r1.dependent
JOIN deprel r2 ON r2.head = a1.wtarget AND r2.dependent = a2.wtarget
JOIN depstr s2 ON s2.dep_id = r2.dep_id
JOIN token t11 ON t11.token_id = r1.head
JOIN token t21 ON t21.token_id = r2.head
JOIN token t12 ON t12.token_id = r1.dependent
JOIN token t22 ON t22.token_id = r2.dependent
WHERE
s1.val = 'amod' AND
s2.val = 'AT' AND
t11.ctag = 'NOUN' AND
t21.ctag = 'NOUN' AND
t12.ctag = 'ADJ' AND
t22.ctag = 'ADJ' AND
t11.lemma_id = 33931 AND
t12.lemma_id = 39166 AND
t21.lemma_id = 15130 AND
t22.lemma_id = 44124),
stats AS (SELECT
sentence_id,
count(DISTINCT token_id) AS c,
count(*) AS c_aligned,
count(DISTINCT wtarget) AS c_target
FROM
token
LEFT JOIN word_align ON wsource = token_id
WHERE
sentence_id IN (
SELECT sentence_id
FROM
list
JOIN token ON token_id IN(t11, t21)
)
GROUP BY sentence_id),
numbered AS (SELECT row_number() OVER () AS i, *
FROM
list),
sentences AS (SELECT *, .2 * (1 / (1 + exp(max(c) OVER (PARTITION BY i) - min(c) OVER (PARTITION BY i)))) +
.8 * (1 / log(avg(c) OVER (PARTITION BY i))) AS w
FROM
(
SELECT i, 1 AS n, sentence_id, ARRAY[t11,t12] AS tokens
FROM
numbered
JOIN token ON token_id = t11
UNION SELECT i, 2 AS n, sentence_id, ARRAY[t21,t22] AS tokens
FROM
numbered
JOIN token ON token_id = t21
) x
JOIN stats USING (sentence_id)
ORDER BY i, n)
SELECT
i,
n,
w,
c,
c_aligned,
c_target,
sentence_id,
string_agg(CASE WHEN lpad THEN ' ' ELSE '' END || '<span class="token' ||
CASE WHEN ARRAY[token_id] <@ tokens THEN ' hl' ELSE '' END || '">' || val || '</span>',
'' ORDER BY token_id ASC) AS s
FROM
sentences
JOIN token USING (sentence_id)
JOIN typestr USING (type_id)
GROUP BY i, n, w, c, c_aligned, c_target, sentence_id
ORDER BY w DESC, i, n;
;