- We need to urge Member States to focus this year on labour market reform so that we can remove obstacles to higher employment levels.
- Vi måste uppmana medlemsstaterna att i år fokusera på arbetsmarknadsreformer så att vi kan avskaffa alla hinder för en hög sysselsättningsgrad.
- I urge the Member States to ensure that projects funded by Cohesion and Structural Funds do not harm biodiversity and ecosystem services but optimise benefits to biodiversity.
- Jag uppmanar medlemsstaterna att se till att de projekt som finansieras av sammanhållnings- och strukturfonderna inte skadar den biologiska mångfalden och ekosystemtjänsterna, utan medför fördelar för den biologiska mångfalden.
- As the original author of the resolution on today’s paper, I urge Member States to act on these proposals and also, at next week’s summit of Heads of State and Government, to honour the declaration of the summit of 12 December last, which should be put officially on the record, preferably along with the final text of the EU-ETS report, as otherwise it will not appear in the Official Journal.
- Det var jag som skrev den ursprungliga texten till den resolution som vi behandlar i dag, och jag uppmanar medlemsstaterna att vidta åtgärder för att genomföra förslagen.
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 = 'dobj' AND
s2.val = 'OO' AND
t11.ctag = 'VERB' AND
t21.ctag = 'VERB' AND
t12.ctag = 'NOUN' AND
t22.ctag = 'NOUN' AND
t11.lemma_id = 34756 AND
t12.lemma_id = 27744 AND
t21.lemma_id = 2815 AND
t22.lemma_id = 36192),
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;
;