- Secondly, the debate will also be strongly influenced by the debates on own resources.
- För det andra kommer debatten också att i hög grad påverkas av debatterna om EU:s egna medel.
- This situation is heavily influenced by the general agricultural situation and women are suffering the consequences of unfair measures in the CAP (common agricultural policy), which have led to the increasing abandonment of small and medium-sized holdings and family-based agriculture.
- Deras situation påverkas starkt av den allmänna jordbrukssituationen och kvinnor drabbas av effekterna av orättvisa åtgärder inom ramen för den gemensamma jordbrukspolitiken, vilket har lett till att allt fler lämnar sina små och medelstora jordbruksföretag och familjejordbruk.
- Work relating to the drafting of the Convention was largely influenced by Community and French policy regarding control of the dangers linked to major accidents involving dangerous substances, taking into account of course, the principle of ‘the polluter pays’ as the general principle within international environmental law.
- Arbetet med att utarbeta förslaget till konvention har till stor del påverkats av Frankrikes och gemenskapens politik beträffande faror i anslutning till stora olyckor med farliga ämnen, naturligtvis med beaktande av principen ”förorenaren betalar” som allmän princip för den internationella miljörätten.
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 = 2122 AND
t12.lemma_id = 6140 AND
t21.lemma_id = 46112 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;
;