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If all this scoring is getting you down, Gnus has a way of making it all happen automatically—as if by magic. Or rather, as if by artificial stupidity, to be precise.
When you read an article, or mark an article as read, or kill an
article, you leave marks behind. On exit from the group, Gnus can sniff
these marks and add score elements depending on what marks it finds.
You turn on this ability by setting gnus-use-adaptive-scoring
to
t
or (line)
. If you want score adaptively on separate
words appearing in the subjects, you should set this variable to
(word)
. If you want to use both adaptive methods, set this
variable to (word line)
.
To give you complete control over the scoring process, you can customize
the gnus-default-adaptive-score-alist
variable. For instance, it
might look something like this:
(setq gnus-default-adaptive-score-alist '((gnus-unread-mark) (gnus-ticked-mark (from 4)) (gnus-dormant-mark (from 5)) (gnus-del-mark (from -4) (subject -1)) (gnus-read-mark (from 4) (subject 2)) (gnus-expirable-mark (from -1) (subject -1)) (gnus-killed-mark (from -1) (subject -3)) (gnus-kill-file-mark) (gnus-ancient-mark) (gnus-low-score-mark) (gnus-catchup-mark (from -1) (subject -1)))) |
As you see, each element in this alist has a mark as a key (either a
variable name or a “real” mark—a character). Following this key is
a arbitrary number of header/score pairs. If there are no header/score
pairs following the key, no adaptive scoring will be done on articles
that have that key as the article mark. For instance, articles with
gnus-unread-mark
in the example above will not get adaptive score
entries.
Each article can have only one mark, so just a single of these rules will be applied to each article.
To take gnus-del-mark
as an example—this alist says that all
articles that have that mark (i.e., are marked with ‘e’) will have a
score entry added to lower based on the From
header by -4, and
lowered by Subject
by -1. Change this to fit your prejudices.
If you have marked 10 articles with the same subject with
gnus-del-mark
, the rule for that mark will be applied ten times.
That means that that subject will get a score of ten times -1, which
should be, unless I’m much mistaken, -10.
If you have auto-expirable (mail) groups (see section Expiring Mail), all the read articles will be marked with the ‘E’ mark. This’ll probably make adaptive scoring slightly impossible, so auto-expiring and adaptive scoring doesn’t really mix very well.
The headers you can score on are from
, subject
,
message-id
, references
, xref
, lines
,
chars
and date
. In addition, you can score on
followup
, which will create an adaptive score entry that matches
on the References
header using the Message-ID
of the
current article, thereby matching the following thread.
If you use this scheme, you should set the score file atom mark
to something small—like -300, perhaps, to avoid having small random
changes result in articles getting marked as read.
After using adaptive scoring for a week or so, Gnus should start to become properly trained and enhance the authors you like best, and kill the authors you like least, without you having to say so explicitly.
You can control what groups the adaptive scoring is to be performed on by using the score files (see section Score File Format). This will also let you use different rules in different groups.
The adaptive score entries will be put into a file where the name is the
group name with gnus-adaptive-file-suffix
appended. The default
is ‘ADAPT’.
Adaptive score files can get huge and are not meant to be edited by
human hands. If gnus-adaptive-pretty-print
is nil
(the
default) those files will not be written in a human readable way.
When doing adaptive scoring, substring or fuzzy matching would probably
give you the best results in most cases. However, if the header one
matches is short, the possibility for false positives is great, so if
the length of the match is less than
gnus-score-exact-adapt-limit
, exact matching will be used. If
this variable is nil
, exact matching will always be used to avoid
this problem.
As mentioned above, you can adapt either on individual words or entire
headers. If you adapt on words, the
gnus-default-adaptive-word-score-alist
variable says what score
each instance of a word should add given a mark.
(setq gnus-default-adaptive-word-score-alist `((,gnus-read-mark . 30) (,gnus-catchup-mark . -10) (,gnus-killed-mark . -20) (,gnus-del-mark . -15))) |
This is the default value. If you have adaption on words enabled, every
word that appears in subjects of articles marked with
gnus-read-mark
will result in a score rule that increase the
score with 30 points.
Words that appear in the gnus-default-ignored-adaptive-words
list
will be ignored. If you wish to add more words to be ignored, use the
gnus-ignored-adaptive-words
list instead.
Some may feel that short words shouldn’t count when doing adaptive
scoring. If so, you may set gnus-adaptive-word-length-limit
to
an integer. Words shorter than this number will be ignored. This
variable defaults to nil
.
When the scoring is done, gnus-adaptive-word-syntax-table
is the
syntax table in effect. It is similar to the standard syntax table, but
it considers numbers to be non-word-constituent characters.
If gnus-adaptive-word-minimum
is set to a number, the adaptive
word scoring process will never bring down the score of an article to
below this number. The default is nil
.
If gnus-adaptive-word-no-group-words
is set to t
, gnus
won’t adaptively word score any of the words in the group name. Useful
for groups like ‘comp.editors.emacs’, where most of the subject
lines contain the word ‘emacs’.
After using this scheme for a while, it might be nice to write a
gnus-psychoanalyze-user
command to go through the rules and see
what words you like and what words you don’t like. Or perhaps not.
Note that the adaptive word scoring thing is highly experimental and is likely to change in the future. Initial impressions seem to indicate that it’s totally useless as it stands. Some more work (involving more rigorous statistical methods) will have to be done to make this useful.
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