- The EU countries are characterised by limited ability to adjust to various structural changes and are therefore afflicted by permanently high unemployment figures.
- EU-länderna utmärks av en svag förmåga att anpassa sig till olika strukturella förändringar och plågas därför av permanent höga arbetslöshetssiffror.
- In particular, emphasis must be placed on reducing disparities in levels of development of regions which are characterised by specific natural and geographic conditions.
- Framför allt måste vi prioritera minskade skillnader i sysselsättningsnivå för regioner som utmärks av särskilda naturliga och geografiska förhållanden.
- I do think, however, that we have some way to go in today’s vote before we have a report that - without also attracting censure from India, Pakistan or anyone else - ultimately serves our common aim of helping Kashmir to finally become a region characterised by peace, prosperity, and respect for the environment and human rights.
- Jag anser emellertid att vi har en del arbete att utföra under dagens omröstning innan vi har ett betänkande som, utan att dra på oss censur från Indien, Pakistan eller från annat håll, slutligen kan tjäna vårt gemensamma syfte att hjälpa Kashmir att bli en region som utmärks av fred, framgång och respekt för miljön och de mänskliga rättigheterna.
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 = 'prep' AND
t11.ctag = 'VERB' AND
t21.ctag = 'VERB' AND
t12.ctag = 'ADP' AND
t22.ctag = 'ADP' AND
t11.lemma_id = 5425 AND
t12.lemma_id = 6140 AND
t21.lemma_id = 43151 AND
t22.lemma_id = 3216),
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;
;