- Mr President, given the time constraints and also the feeling in my group that the ECB has essentially been doing a good job in difficult circumstances, I would like to concentrate on some remaining structural issues concerning communications and accountability.
- Med hänsyn till den begränsade tiden och även uppfattningen i min grupp, att ECB i allt väsentligt har gjort ett bra arbete under svåra omständigheter, skulle jag vilja koncentrera mig på några återstående strukturella frågor som gäller kommunikation och ansvarighet.
- The Commission and Mr Cunha have both done very good jobs in analysing the results of the final year of the third multiannual guidance programme.
- Kommissionen och Cunha har gjort ett mycket bra arbete med att analysera resultaten av de sista året av det tredje fleråriga utvecklingsprogrammet.
- First of all we were enormously grateful to the delegation from Parliament that went recently to Kosovo and was able to confirm that our team there is doing a good job and that there is not an absorption capacity problem as far as the assistance is concerned.
- Vi var för det första oerhört tacksamma för den delegation från parlamentet som nyligen besökte Kosovo och som kunde bekräfta att vår grupp där utför ett bra arbete, och att det inte finns något absorptionskapacitetsproblem vad gäller stödet.
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
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t12.ctag = 'ADJ' AND
t22.ctag = 'ADJ' AND
t11.lemma_id = 43879 AND
t12.lemma_id = 12586 AND
t21.lemma_id = 40666 AND
t22.lemma_id = 9885),
stats AS (SELECT
sentence_id,
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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
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FROM
numbered
JOIN token ON token_id = t21
) x
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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
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JOIN typestr USING (type_id)
GROUP BY i, n, w, c, c_aligned, c_target, sentence_id
ORDER BY w DESC, i, n;
;