- Africa is made up of young nations which are consolidating their democratic systems with every day that goes by.
- Afrika består av unga nationer, under utveckling, som dag för dag befäster sina demokratiska system.
- Mr President, when a governing body receives orders from no-one and is made up of a very small number of people, what is it called?
- Herr ordförande, när en ledningsgrupp inte tar emot order från någon och består av ett litet antal personer, vad kallas det?
- This ’package’ is made up of seven agreements on road and air transport, the free movement of persons, public procurement, research and development, mutual recognition in relation to conformity assessment, and agriculture.
- Detta ”paket” består av sju avtal som rör väg- och lufttransport, fri rörlighet för personer, offentlig upphandling, forskning och utveckling, ömsesidigt erkännande i samband med bedömning av överensstämmelse samt jordbruk.
- In the case of the textile industry, the fact that the sector is mainly made up of small and medium-sized enterprises is a disadvantage on the market that represents 6% of total world trade and an estimated turnover of EUR 566 billion.
- I textilindustrins fall är det faktum att sektorn i huvudsak består av små och medelstora företag en nackdel på en marknad som utgör 6 procent av den totala världshandeln och har en beräknad omsättning på 566 miljarder euro.
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 = 13701 AND
t12.lemma_id = 23323 AND
t21.lemma_id = 16816 AND
t22.lemma_id = 3216),
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;
;