- Coming, as I do, from an area that has suffered very high unemployment, I can say that it is one of the greatest problems that we face in the European Union at this moment in time.
- När man som jag kommer från ett område som drabbats av en mycket hög arbetslöshet så kan jag säga att detta är ett av de största problemen vi har inom Europeiska unionen för närvarande.
- Efforts are being made to resolve the problem of unemployment by stepping up the neo-liberal monetarist policies and persisting with the financial prudence which were responsible for the current recession and high unemployment in the first place.
- Genom att intensifiera den nyliberala monetaristiska politiken och genom att hålla fast vid kravet på budgetdisciplin försöker man lösa problemet med arbetslösheten, samtidigt som man bortser från att det är denna politik som bär ansvaret för dagens kris och den höga arbetslösheten.
- But it is correct that all the criteria which you have listed must certainly be taken into account, e.g. for industrial areas, dependency on a particular branch of industry and associated exceptionally high unemployment, for city areas, the extent of social exclusion, for instance, and for rural areas, not just dependency on agriculture, but also the danger of emigration associated with the decline of agriculture.
- Men det är riktigt att alla de kriterier som ni nämnt kommer det med säkerhet att tas hänsyn till, t.ex. för industriområden, om de är beroende av någon speciell industrigren, en särskilt hög arbetslöshet som hänger ihop med detta, för stadsområden, graden av social marginalisering och för landsbygdsområden inte bara beroendet av jordbruket, utan även utflyttningens faror som hänger ihop med tillbakagången inom jordbruket.
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
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WHERE
s1.val = 'amod' AND
s2.val = 'AT' AND
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t22.ctag = 'ADJ' AND
t11.lemma_id = 23859 AND
t12.lemma_id = 23000 AND
t21.lemma_id = 55072 AND
t22.lemma_id = 7438),
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
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' ||
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'' ORDER BY token_id ASC) AS s
FROM
sentences
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JOIN typestr USING (type_id)
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ORDER BY w DESC, i, n;
;