- The spread of an epidemic as the result of bringing in one sick animal can have huge consequences for farmers.
- Epidemier som sprids på grund av att ett sjukt djur förs in kan få oerhörda följder för jordbrukarna.
- Finally, I do not think we should support the proposal to reduce fleet overcapacity, which could have adverse consequences on fisheries in the context of a severe financial and economic crisis.
- Slutligen anser jag inte att vi bör stödja förslaget om att minska flottornas överkapacitet, vilket skulle kunna få skadliga följder för fisket i samband med en allvarlig finansiell och ekonomisk kris.
- If we are to talk about rating agencies and if we are to be delighted with our rapporteur’s initiative, it is because the market is blind and because, in this blindness, rating agencies have obtained, or are obtaining, an altogether essential decision-making power that will have far-reaching consequences for the future of enterprises and, therefore, of jobs.
- Om vi skall tala om kreditvärderingsinstitut och om vi skall glädjas över föredragandens initiativ, är det för att marknaden är blind och för att kreditvärderingsinstituten i denna blindhet har fått, eller får, på det hela taget väsentliga beslutsfattande befogenheter som kommer att få mycket omfattande följder för företagens framtid och därmed, för arbetstillfällena.
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 = 'dobj' AND
s2.val = 'OO' AND
t11.ctag = 'VERB' AND
t21.ctag = 'VERB' AND
t12.ctag = 'NOUN' AND
t22.ctag = 'NOUN' AND
t11.lemma_id = 48540 AND
t12.lemma_id = 13276 AND
t21.lemma_id = 16661 AND
t22.lemma_id = 34997),
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;
;