- When we talk about customs processing what comes to mind is a negative image of red tape, inefficiency and unnecessary duplication.
- Att tala om tullförfaranden påminner oss om en negativ byråkratisk bild, bristande effektivitet och en fördubbling av verksamheten.
- let us not create a negative image of the Korean coach builder.
- Vi, tillsammans med kommissionen, skapade förutsättningarna för en negativ bild av den polske rörmokaren, låt oss inte skapa en negativ bild av den koreanska busstillverkaren.
- to the company in question because of the resulting financial costs, and to the European institutions because of the extremely negative image that they project as a result of the delay in concluding these proceedings.
- Den här typen av konflikter drabbar alla inblandade, såväl företaget i fråga på grund av ekonomiska förluster, som EU-institutionerna till följd av den extremt negativa bild som skapas av att förfaranden inte avslutas.
- That causes enormous damage to the European ideal in the workplace, and creates a negative image of the EU in the press.
- Vidare borde vi stå emot alla försök att urholka direktiven om ömsesidigt erkännande vilken bland annat då och då händer för lärare, och det av nationella egoistiska skäl Detta skadar Europatanken avsevärt och präglar den negativa bilden av EU i pressen.
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 = 5423 AND
t12.lemma_id = 14133 AND
t21.lemma_id = 7162 AND
t22.lemma_id = 54653),
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;
;