• When sectors are in crisis, we must always ensure that we take the right measures in good time.
• När en industrigren hamnar i en krissituation borde vi alltid se till att vi i rätt tid förbereder de rätta åtgärderna.

• The WTO may be in a crisis similar to that of Aesop’s frog, because it is in danger of swelling up, both because of the number of Members - 148, as I said - and because of its growing agenda, in the face, moreover, of the inertia of other international organisations on themes which are their particular interest and responsibility.
• WTO kan hamna i en liknande krissituation som Aisopos groda eftersom organisationen riskerar att svälla upp både på grund av antalet medlemmar - 148 som sagt - och på grund av den svällande agendan, inte minst med tanke på andra internationella organisationers tröghet på områden som ligger i deras särskilda intresse och som de ansvarar för.

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 = 24585 AND
				         t12.lemma_id = 8748 AND
				         t21.lemma_id = 39979 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;
;