- You will see huge problems soon.
- Ni kommer snart att få se enorma problem.
- The demobilisation and the reintegration of ex-combatants is a huge problem.
- Demobiliseringen och återintegreringen av före detta soldater är ett enormt problem.
- Mr President, the process of sub-prime lending has definitely been a huge problem causing the financial turbulence which we have witnessed this summer.
- Subprime-utlåningen har definitivt varit ett enormt problem som har orsakat den finansiella turbulens som vi har sett denna sommar.
- Although illegal immigrants themselves are often living in abject circumstances, they also cause huge problems in the societies and to the people living in the Member States of the European Union.
- Dels lever de illegala invandrarna själva ofta under erbarmliga villkor, dels skapar de enorma problem för samhällena och för de människor som lever i Europeiska unionens medlemsstater.
- Madam President, ladies and gentlemen, I share with the rapporteur the idea that the huge problems still present today in Afghanistan should be approached in a new way, in other words with a different definition of priorities compared to what has been done so far.
- Jag delar föredragandens tanke om att de enorma problem som fortfarande finns i Afghanistan i dag bör hanteras på ett nytt sätt, med andra ord genom att man prioriterar på ett annorlunda sätt än vad man har gjort hittills.
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 = 13883 AND
t12.lemma_id = 60006 AND
t21.lemma_id = 13883 AND
t22.lemma_id = 54424),
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;
;