- This is very important work, which I hope we can do more to support.
- Det är ett väldigt viktigt arbete som jag hoppas att vi kan stödja mera.
- We are assisting Greece in the important work it is doing to build up a system that is in line with European law.
- Vi hjälper Grekland i det viktiga arbete landet utför för att bygga upp ett system som stämmer överens med EU-lagstiftningen.
- I thank you on behalf of the Commission, but also very much on my own behalf, for the important work you have done for the benefit of European agriculture.
- Jag tackar er i kommissionens namn, men också personligen, för det viktiga arbete som ni har åstadkommit för det europeiska jordbrukets välfärd, och önskar er allt gott även för framtiden!
- Mr President, the recommendations under discussion today put forward by my colleague, Mrs Paulsen, are a continuation of the important work she has been so very commendably involved in as Parliament’s rapporteur regarding updating the legislation on food safety.
- De rekommendationer av kollegan Paulsen som behandlas i dag är en fortsättning på det viktiga arbete, där hon på ett mycket berömligt sätt har fungerat som parlamentets föredragande när det gäller uppdateringen av lagstiftningen rörande livsmedelssäkerhet.
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 = 10877 AND
t12.lemma_id = 36766 AND
t21.lemma_id = 40666 AND
t22.lemma_id = 14863),
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;
;