- The human aspect of what we are dealing with is huge:
- Den mänskliga aspekten på det vi arbetar med är enorm:
- Fortunately the state facilities worked well, the fires were put out in good time, and the State institutions are now dealing with compensation for those affected.
- Som tur var fungerade de statliga insatserna bra, bränderna släcktes i tid och de statliga institutionerna arbetar nu med kompensation för dem som drabbades.
- It is a sign of the enormous progress that the European Union has made that we are now actually dealing with the practicalities of the single currency and not simply talking about the theory of economic and monetary union.
- De tecken på enorma framsteg som Europeiska unionen har gjort att vi nu faktiskt arbetar med de praktiska detaljerna i den gemensamma valutan och inte bara pratar om teorierna bakom en ekonomisk och monetär union.
- I voted in favour of Mrs Lambert’s report on social security to signal my support for rationalising, simplifying and making the existing texts on Regulation (EC) No 1408/71 more accessible to the citizens of Europe and to those who are dealing with it on a daily basis.
- Jag röstade för Lamberts betänkande om de sociala trygghetssystemen för att uttrycka mitt stöd för en rationalisering och förenkling av förordning (EG) nr 1408/71 och för att göra de nuvarande texterna mer lättillgängliga för Europas medborgare och för dem som dagligen arbetar med denna förordning.
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 = 12650 AND
t12.lemma_id = 12425 AND
t21.lemma_id = 63839 AND
t22.lemma_id = 57357),
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;
;