- Bluetongue is a horrible animal epidemic affecting ruminants that has caused great damage throughout the livestock sector.
- Bluetongue är en fruktansvärd djurepidemi som drabbar idisslare och som har orsakat stora skador i hela sektorn för djuruppfödning.
- The decision taken by the 14 has done great damage to the perception that the citizens of the European Union have of Europe.
- I den europeiska befolkningens Europamedvetande har de fjortons beslut åstadkommit stor skada.
- Now that the Commission is rejecting Sweden’s argument and placing the emphasis upon the operation of the internal market, we fear that alcohol policy will sustain great damage in Sweden.
- När nu kommissionen underkänner Sveriges argument och lägger tonvikten vid den inre marknadens funktion, befarar vi att alkoholpolitiken kommer att lida stor skada i Sverige.
- For instance the flooding in Mozambique caused much greater damage to dams, road, bridges and so on than it would have done if the country had had normal, well-maintained, strong and more expensive infrastructure.
- Översvämningarna i Moçambique, till exempel, tillfogade mycket större skador på dammar, vägar, broar och så vidare än vad de skulle ha gjort om landet hade haft en normal, väl underhållen, starkare och dyrare infrastruktur.
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 = 49959 AND
t12.lemma_id = 43163 AND
t21.lemma_id = 42798 AND
t22.lemma_id = 35323),
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;
;