Index: head/textproc/libtextcat/Makefile
===================================================================
--- head/textproc/libtextcat/Makefile	(revision 462572)
+++ head/textproc/libtextcat/Makefile	(revision 462573)
@@ -1,41 +1,43 @@
 # Created by: thierry@pompo.net
 # $FreeBSD$
 
 PORTNAME=	libtextcat
 PORTVERSION=	2.2
 PORTREVISION=	6
 CATEGORIES=	textproc
 MASTER_SITES=	http://software.wise-guys.nl/download/
 
 MAINTAINER=	thierry@FreeBSD.org
 COMMENT=	Language guessing by N-Gram-Based Text Categorization
 
 LICENSE=	BSD3CLAUSE
 LICENSE_FILE=	${WRKSRC}/LICENSE
 
 GNU_CONFIGURE=	yes
 USES=		libtool
 USE_LDCONFIG=	yes
 
 OPTIONS_DEFINE=	DOCS
 
 PORTDOCS=	README TODO
 
 post-install:
 	${INSTALL_DATA} ${WRKSRC}/src/textcat.h ${STAGEDIR}${PREFIX}/include/
 	${MKDIR} ${STAGEDIR}${DATADIR}/LM
 	@${ECHO_MSG} "Installing language models provided in Gertjan van Noord's TextCat package"
 	(cd ${WRKSRC}/langclass/LM &&		\
 	${FIND} . -name "*.lm" -exec ${INSTALL_DATA} {} "${STAGEDIR}${DATADIR}/LM/{}" \;)
 	${INSTALL_DATA} ${WRKSRC}/langclass/conf.txt "${STAGEDIR}${DATADIR}"
-	${MKDIR} ${STAGEDIR}${DOCSDIR}
+
+post-install-DOCS-on:
+	@${MKDIR} ${STAGEDIR}${DOCSDIR}
 	${INSTALL_DATA} ${PORTDOCS:S|^|${WRKSRC}/|} ${STAGEDIR}${DOCSDIR}
 
-regression-test:
+do-test:
 	(cd ${WRKSRC}/langclass/ &&		\
 	for t in `${LS} ShortTexts/*.txt` ; do	\
 	${ECHO_MSG} "Analyzing $$t..." ;	\
 	../src/testtextcat conf.txt < $$t ;	\
 	done)
 
 .include <bsd.port.mk>
Index: head/textproc/libtextcat/pkg-descr
===================================================================
--- head/textproc/libtextcat/pkg-descr	(revision 462572)
+++ head/textproc/libtextcat/pkg-descr	(revision 462573)
@@ -1,17 +1,17 @@
 Libtextcat is a library with functions that implement the classification
 technique described in Cavnar & Trenkle, "N-Gram-Based Text Categorization" [1].
 It was primarily developed for language guessing, a task on which it is known to
 perform with near-perfect accuracy.
 
 The central idea of the Cavnar & Trenkle technique is to calculate a
 "fingerprint" of a document with an unknown category, and compare this with the
 fingerprints of a number of documents of which the categories are known. The
 categories of the closest matches are output as the classification. A
 fingerprint is a list of the most frequent n-grams occurring in a document,
 ordered by frequency. Fingerprints are compared with a simple out-of-place
 metric.
 
 [1] The document that started it all: William B. Cavnar & John M. Trenkle (1994)
 N-Gram-Based Text Categorization, <http://citeseer.ist.psu.edu/68861.html>.
 
-WWW: http://software.wise-guys.nl/libtextcat/
+WWW: https://software.wise-guys.nl/libtextcat/