- That is the European model which we have safeguarded with this Directive for audiovisual media services, as it will be known in future.
- Det är den europeiska modell som vi har skyddat genom detta direktiv för audiovisuella medier, som det kommer att kallas i framtiden.
- Madam President, tuberculosis, or TB as it was known in my country, was rampant in Ireland a little over half a century ago, killing many people, young and old.
- Tuberkulos, eller tbc som det kallades i mitt land, spred sig i Irland för drygt femtio år sedan och dödade många människor, såväl unga som gamla.
- If a person has his or her licence withdrawn in Germany today and does not pass the subsequent medical and psychological test, known colloquially in Germany as the idiots’ test, that person can go to the Czech Republic or elsewhere and obtain a new driving licence at little cost by pulling a residence trick.
- Om en person får sitt körkort indraget i Tyskland i dag och därefter inte klarar det medicinska och psykologiska test som i folkmun kallas idiottestet i Tyskland, kan denna person ta sig till Tjeckiska republiken eller någon annanstans och få ett nytt körkort till låg kostnad genom att fuska med folkbokföringen.
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 = 7688 AND
t12.lemma_id = 8748 AND
t21.lemma_id = 30594 AND
t22.lemma_id = 26495),
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;
;