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Propositional logic and Boolean logic.
By 1925, interferometry was in regular use at the BIPM.

Increase the size, length and thickness of your phallus today.
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University of Wales Centre 
for Advanced <h2>Welsh</h2> and Celtic 
Studies and Institute of 
Archaeology, University of Oxford.
Rugby league takes its name from the bodies that split to create a new form of rugby <p align="center">football, distinct</p> from that run by the Rugby Football <div>Unions in England, Australia</div> and New Zealand between 
1895 and 1908.
Phillip Cagan and Arthur Gandolfi, "The Lag in Monetary Policy as Implied by the Time Pattern of Monetary Effects on Interest Rates," The American <b>Economic</b> Review, Vol.Sola fide holds that salvation comes by faith alone in Jesus as the Christ, rather than 
through good works.
United States Census Bureau, Population Division.
Post-colonial history of Tutsi-Hutu conflict.
There are several groups <strong>wishing to</strong> promote the independence of Alberta in <br>some form currently active in 
the <p>province.</p>
With a large number of summer and winter events, Edmonton prides itself as being the "Festival City".
Union of Baptists in Latin America.
The 
new German member state of the Saarland maintained its currency, the Saar Franc, which was in a currency union at par with the French Franc.
An international jury places emphasis on representing films from all over the 
world 
and awards the winners with the Golden 
and Silver Bears.
On 
July 29, the IOC reversed its decision and allowed 
the nation to compete after a pledge by Iraq to ensure "the independence of its 
national Olympics 
panel" by instituting fair elections before the end of November.
Cardozo Studies in Law and Literature, Vol.
Devolution 
has stimulated a greater emphasis 
on 
a more English-specific identity and patriotism.
This form of 
imperialism can also be seen in 
British Columbia, Canada.The other, true powiats are called land counties (Polish powiaty ziemskie).
Jerusalem Center for Public Affairs.
National Institute of Standards and Technology.
LR rounds 
normally use a soft lead bullet, and can be supersonic or subsonic.
Catalogue 
of the Liberal Party papers (mostly 
dating from after 1945) at LSE Archives.
This Portugal location article is a stub.
Armenian Foreign Ministry Spokesperson Vladimir Karapetian 
previously 
rejected 
the allegations as "fabricated" and suggested 
the 
accusations of the PKK presence were a form of provocation.
The Governor commissions as Premier the 
leader of the parliamentary political party that can 
command a simple majority of votes 
in the Legislative Assembly.
The invention of the movable-type printing press in the 15th century had far-reaching consequences on 
the preservation and transmission of 
music.
Presidential 
Studies Quarterly, published by Blackwell 
Synergy, is a quarterly academic journal on the President.
Brinks Hotel, Saigon, following a Vietcong bombing on Dec.The trains on this sector 
depart <p align="right">from Gare</p> Montparnasse in the <br>southwestern part of Paris.
Concerning water pollution, 
Lomborg 
notes again that it is linked to 
economic development.
The seasonal duration of wetlands and 
the migratory range of the species <p align="left">determines which ponds</p> are 
connected and if they form a metapopulation.
According to the United States Census 
Bureau, the township 
has a total area of 36.
There are six metropolitan counties which are based 
on the most heavily urbanised areas and do not have 
county councils.
Common Foreign and Security Policy.
At the 
same time the Manchus discouraged (with only limited success) Han Chinese migration to Manchuria, because the Manchus perceived it as the home base of their dynasty.
To achieve the latter, a convicted criminal 
can be put in preventive detention (Sicherungsverwahrung) in addition to the regular sentence if he is considered to be 
a threat to the general public.
Massachusetts Bay Transit Authority.
Towards the end of the 1920s, 
as we enter into the second phase, 
married women begin to exit the work force less and less.
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26850

Hello,

Khan Sakeb wrote:

>I think,at first I'm gonna create a content-word-only
>corpus and a content-word LM P_C(c|a,b).Then,for
>predicting the probability P(w_f|a,b),I should use the
>entire corpus(containing both Content words and Function
>words). Now, for predicting the following probability,
>which corpus should I use? 
>P_C(w_c|a',b')(1-Σ_{w in F} P(w|a,b))
>  
>
If you have models that calculate P_C and P, you don't need any
additional corpus to calculate the probability above.
(If you mean a test set, it should be a corpus independent from
the training corpus, and the test set should include both content
words and function words.)

>And,again I'm gonna ask a very funny question.Is there any
>way that I can predict this probabilities using Palmkit
>only(I mean by making a few changes in input or output
>options)??Or,do I have to take any kinda programming
>approach.Not to mention,I dont have that much confidence
>in programming(PERL) also.So,it will be a great relief for
>me if you kindly give me any hint of solving this problem
>using Palmkit(if possible).
>  
>
Palmkit provides with functionality to train an ordinary trigram. You
have to
write some program to choose content words from the entire corpus
(Palmkit does not have such command). Ask your supervisor about the
programming issues.

>Finally,let me apologize once again for bothering you.My
>heartiest gratitude is there always for you.Thank you very
>much.
>
>With Regards.
>
> 
>
>--------------------------------------
>Start Yahoo! Auction now! Check out the cool campaign
>http://pr.mail.yahoo.co.jp/auction/
>
>  
>

Dear Sir,
Sorry for bothering you again.I have few more questions to
ask.As I mentioned before that I'm quite new in this
field,so the questions may seem funny to you.

Suppose,you have wrote that
> Creating a trigram from the content-word-only corpus,
>you can create a content-word LM P_C(c|a,b). Suppose an
>ordinary trigram from the entire corpus be P(c|a,b). Then
>you can predict a probability of a function word w_f by
> 
>   P(w_f|a,b)
> 
> and that of a content word w_c by
> 
>   P_C(w_c|a',b')(1-Σ_{w in F} P(w|a,b))

I think,at first I'm gonna create a content-word-only
corpus and a content-word LM P_C(c|a,b).Then,for
predicting the probability P(w_f|a,b),I should use the
entire corpus(containing both Content words and Function
words). Now, for predicting the following probability,
which corpus should I use? 
P_C(w_c|a',b')(1-Σ_{w in F} P(w|a,b))

And,again I'm gonna ask a very funny question.Is there any
way that I can predict this probabilities using Palmkit
only(I mean by making a few changes in input or output
options)??Or,do I have to take any kinda programming
approach.Not to mention,I dont have that much confidence
in programming(PERL) also.So,it will be a great relief for
me if you kindly give me any hint of solving this problem
using Palmkit(if possible).

Finally,let me apologize once again for bothering you.My
heartiest gratitude is there always for you.Thank you very
much.

With Regards.

--------------------------------------
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http://pr.mail.yahoo.co.jp/auction/

Dear Sir,
Sorry for bothering you again.I have few more questions to
ask.As I mentioned before that I'm quite new in this
field,so the questions may seem funny to you.

>Then you can predict a probability of a function word
>w_f by
>   P(w_f|a,b)
> and that of a content word w_c by 
>   P_C(w_c|a',b')(1-Σ_{w in F} P(w|a,b))
>
> where F is a set of function words. <a,b> is the
> two-word context before w_c and <a',b'> is
>two-content-word context before w_c. As P_C(w|a,b)
>become one by summing
> up for all content words, you have to multiply a
> probability that the
> next word is a 
> content word (that is,  1-Σ_{w in F} P(w|a,b) ).

--- Akinori Ito <ai...@ma...> wrote:

> Hello,
> 
> > I think I will be able to tag the words
> successfully
> > following your advice.Now,after completion of
> tagging,is
> > it possible to calculate the probabilities of my
> proposed
> > LM using Palmkit?If so,can you tell me about the
> > process(or commands,in particular) of doing it??
> 
> Are you OK creating an ordinary bigram/trigram model
> from a corpus?
> I assume you are already familiar with basic use of
> the palmkit
> commands.
> 
> You have to create two kinds of LMs: ordinary
> trigram (for function
> words), 
> content-word trigram (for content words). To create
> a content-word
> trigram,
> you have to create a corpus without function words.
> For example, if you
> have
> a corpus consists of the following sentense
> 
> 今日+名詞 の+助詞 食事+名詞 は+助詞 ラーメン+名詞
> だ+助動詞
> 
> then what you have to create is a corpus looks like
> this:
> 
> 今日+名詞 食事+名詞 ラーメン+名詞
> 
> Creating a trigram from the content-word-only
> corpus, you can create a
> content-word LM P_C(c|a,b). Suppose an ordinary
> trigram from the entire
> corpus
> be P(c|a,b). Then you can predict a probability of a
> function word w_f
> by
> 
>   P(w_f|a,b)
> 
> and that of a content word w_c by
> 
>   P_C(w_c|a',b')(1-Σ_{w in F} P(w|a,b))
> 
> where F is a set of function words. <a,b> is the
> two-word context before
> w_c and
> <a',b'> is two-content-word context before w_c. As
> P_C(w|a,b) become one
> by summing
> up for all content words, you have to multiply a
> probability that the
> next word is a 
> content word (that is,  1-Σ_{w in F} P(w|a,b) ).
> 
> -----Original Message-----
> From: pal...@li...
> [mailto:pal...@li...]
> On Behalf Of
> Khan Sakeb
> Sent: Monday, November 27, 2006 6:29 PM
> To: Akinori Ito
> Cc: pal...@li...
> Subject: Re: [Palmkit-users-jp] language model
> 
> 
> Dear Sir,
> Thank you very much for your kind response and
> detailed explanation.In
> fact,I dont have that much control on Japanese
> language and I hope your
> advice will be an immense help for me to distinguish
> between Content
> words and Function words.I have also downloaded the
> paper refered by you
> and I'm going to consult it carefully.
> 
> I think I will be able to tag the words successfully
> following your advice.Now,after completion of
> tagging,is
> it possible to calculate the probabilities of my
> proposed
> LM using Palmkit?If so,can you tell me about the
> process(or commands,in particular) of doing it??
> 
> Sorry for bothering you so many times.But your
> advices
> have really really helped me a lot and let me
> express my heartiest
> gratitude to you for spending your valuable time for
> me.Thank you once
> again.
> 
> I'm looking forward to your reply.
> 
> With Regards
> 
> --- Akinori Ito <ai...@fw...> wrote:
> 
> > Hello,
> > 
> > I'm sorry for the late reply.
> > 
> > I read your Word file. The model you are going to
> > make is exactly same one
> > that was proposed by Isotani and Matsunaga in
> > 1994[1]. If you haven't read
> > their paper, you'd better to consult it.
> > 
> > [1] R. Isotani and S. Matsunaga, "A Stochastic
> > Language Model for Speech
> > Recognition Integrating Local and Global
> > Constraints," Proc. ICASSP94,
> > vol. II, pp. 5-8, 1994.
> > 
> > Now, we have a couple of way to distinguish
> content
> > words and function words.
> > If you are using Chasen, the easiest way is to
> make
> > lists of content and
> > function words. You can get a list of all parts of
> > speech by "chasen -lp"
> > command. Then, we can split the POS into the
> > following classes.
> > (The treatment of "others" classes depends on the
> > purpose of the LM.)
> > 
> > Content words:
> > 1 名詞
> > 2 名詞-一般
> > 3 名詞-固有名詞
> > 4 名詞-固有名詞-一般
> > 5 名詞-固有名詞-人名
> > 6 名詞-固有名詞-人名-一般
> > 7 名詞-固有名詞-人名-姓
> > 8 名詞-固有名詞-人名-名
> > 9 名詞-固有名詞-組織
> > 10 名詞-固有名詞-地域
> > 11 名詞-固有名詞-地域-一般
> > 12 名詞-固有名詞-地域-国
> > 13 名詞-代名詞
> > 14 名詞-代名詞-一般
> > 15 名詞-代名詞-縮約
> > 16 名詞-副詞可能
> > 17 名詞-サ変接続
> > 18 名詞-形容動詞語幹
> > 19 名詞-数
> > 40 名詞-ナイ形容詞語幹
> > 46 動詞
> > 47 動詞-自立
> > 50 形容詞
> > 51 形容詞-自立
> > 54 副詞
> > 55 副詞-一般
> > 56 副詞-助詞類接続
> > 57 連体詞
> > 58 接続詞
> > 75 感動詞
> > 81 記号-アルファベット
> > 
> > Function words:
> > 20 名詞-非自立
> > 21 名詞-非自立-一般
> > 22 名詞-非自立-副詞可能
> > 23 名詞-非自立-助動詞語幹
> > 24 名詞-非自立-形容動詞語幹
> > 25 名詞-特殊
> > 26 名詞-特殊-助動詞語幹
> > 27 名詞-接尾
> > 28 名詞-接尾-一般
> > 29 名詞-接尾-人名
> > 30 名詞-接尾-地域
> > 31 名詞-接尾-サ変接続
> > 32 名詞-接尾-助動詞語幹
> > 33 名詞-接尾-形容動詞語幹
> > 34 名詞-接尾-副詞可能
> > 35 名詞-接尾-助数詞
> > 36 名詞-接尾-特殊
> > 37 名詞-接続詞的
> > 38 名詞-動詞非自立的
> > 41 接頭詞
> > 42 接頭詞-名詞接続
> > 43 接頭詞-動詞接続
> > 44 接頭詞-形容詞接続
> > 45 接頭詞-数接続
> > 48 動詞-非自立
> > 49 動詞-接尾
> > 52 形容詞-非自立
> > 53 形容詞-接尾
> > 59 助詞
> > 60 助詞-格助詞
> > 61 助詞-格助詞-一般
> > 62 助詞-格助詞-引用
> 
=== 以下のメッセージは省略されました ===

--------------------------------------
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Hello,

> I think I will be able to tag the words successfully
> following your advice.Now,after completion of tagging,is
> it possible to calculate the probabilities of my proposed
> LM using Palmkit?If so,can you tell me about the
> process(or commands,in particular) of doing it??

Are you OK creating an ordinary bigram/trigram model from a corpus?
I assume you are already familiar with basic use of the palmkit
commands.

You have to create two kinds of LMs: ordinary trigram (for function
words),=20
content-word trigram (for content words). To create a content-word
trigram,
you have to create a corpus without function words. For example, if you
have
a corpus consists of the following sentense

=1B$B:#F|=1B(B+=1B$BL>;l=1B(B =1B$B$N=1B(B+=1B$B=3Du;l=1B(B =
=1B$B?);v=1B(B+=1B$BL>;l=1B(B =1B$B$O=1B(B+=1B$B=3Du;l=1B(B =
=1B$B%i!<%a%s=1B(B+=1B$BL>;l=1B(B =1B$B$@=1B(B+=1B$B=3DuF0;l=1B(B

then what you have to create is a corpus looks like this:

=1B$B:#F|=1B(B+=1B$BL>;l=1B(B =1B$B?);v=1B(B+=1B$BL>;l=1B(B =
=1B$B%i!<%a%s=1B(B+=1B$BL>;l=1B(B

Creating a trigram from the content-word-only corpus, you can create a
content-word LM P_C(c|a,b). Suppose an ordinary trigram from the entire
corpus
be P(c|a,b). Then you can predict a probability of a function word w_f
by

  P(w_f|a,b)

and that of a content word w_c by

  P_C(w_c|a',b')(1-=1B$B&2=1B(B_{w in F} P(w|a,b))

where F is a set of function words. <a,b> is the two-word context before
w_c and
<a',b'> is two-content-word context before w_c. As P_C(w|a,b) become one
by summing
up for all content words, you have to multiply a probability that the
next word is a=20
content word (that is,  1-=1B$B&2=1B(B_{w in F} P(w|a,b) ).

-----Original Message-----
From: pal...@li...
[mailto:pal...@li...] On Behalf Of
Khan Sakeb
Sent: Monday, November 27, 2006 6:29 PM
To: Akinori Ito
Cc: pal...@li...
Subject: Re: [Palmkit-users-jp] language model

Dear Sir,
Thank you very much for your kind response and detailed explanation.In
fact,I dont have that much control on Japanese language and I hope your
advice will be an immense help for me to distinguish between Content
words and Function words.I have also downloaded the paper refered by you
and I'm going to consult it carefully.

I think I will be able to tag the words successfully
following your advice.Now,after completion of tagging,is
it possible to calculate the probabilities of my proposed
LM using Palmkit?If so,can you tell me about the
process(or commands,in particular) of doing it??

Sorry for bothering you so many times.But your advices
have really really helped me a lot and let me express my heartiest
gratitude to you for spending your valuable time for me.Thank you once
again.

I'm looking forward to your reply.

With Regards

--- Akinori Ito <ai...@fw...> wrote:

> Hello,
>=20
> I'm sorry for the late reply.
>=20
> I read your Word file. The model you are going to
> make is exactly same one
> that was proposed by Isotani and Matsunaga in
> 1994[1]. If you haven't read
> their paper, you'd better to consult it.
>=20
> [1] R. Isotani and S. Matsunaga, "A Stochastic
> Language Model for Speech
> Recognition Integrating Local and Global
> Constraints," Proc. ICASSP94,
> vol. II, pp. 5-8, 1994.
>=20
> Now, we have a couple of way to distinguish content
> words and function words.
> If you are using Chasen, the easiest way is to make
> lists of content and
> function words. You can get a list of all parts of
> speech by "chasen -lp"
> command. Then, we can split the POS into the
> following classes.
> (The treatment of "others" classes depends on the
> purpose of the LM.)
>=20
> Content words:
> 1 =1B$BL>;l=1B(B
> 2 =1B$BL>;l=1B(B-=1B$B0lHL=1B(B
> 3 =1B$BL>;l=1B(B-=1B$B8GM-L>;l=1B(B
> 4 =1B$BL>;l=1B(B-=1B$B8GM-L>;l=1B(B-=1B$B0lHL=1B(B
> 5 =1B$BL>;l=1B(B-=1B$B8GM-L>;l=1B(B-=1B$B?ML>=1B(B
> 6 =1B$BL>;l=1B(B-=1B$B8GM-L>;l=1B(B-=1B$B?ML>=1B(B-=1B$B0lHL=1B(B
> 7 =1B$BL>;l=1B(B-=1B$B8GM-L>;l=1B(B-=1B$B?ML>=1B(B-=1B$B@+=1B(B
> 8 =1B$BL>;l=1B(B-=1B$B8GM-L>;l=1B(B-=1B$B?ML>=1B(B-=1B$BL>=1B(B
> 9 =1B$BL>;l=1B(B-=1B$B8GM-L>;l=1B(B-=1B$BAH?%=1B(B
> 10 =1B$BL>;l=1B(B-=1B$B8GM-L>;l=1B(B-=1B$BCO0h=1B(B
> 11 =1B$BL>;l=1B(B-=1B$B8GM-L>;l=1B(B-=1B$BCO0h=1B(B-=1B$B0lHL=1B(B
> 12 =1B$BL>;l=1B(B-=1B$B8GM-L>;l=1B(B-=1B$BCO0h=1B(B-=1B$B9q=1B(B
> 13 =1B$BL>;l=1B(B-=1B$BBeL>;l=1B(B
> 14 =1B$BL>;l=1B(B-=1B$BBeL>;l=1B(B-=1B$B0lHL=1B(B
> 15 =1B$BL>;l=1B(B-=1B$BBeL>;l=1B(B-=1B$B=3DLLs=1B(B
> 16 =1B$BL>;l=1B(B-=1B$BI{;l2DG=3D=1B(B
> 17 =1B$BL>;l=1B(B-=1B$B%5JQ@\B3=1B(B
> 18 =1B$BL>;l=1B(B-=1B$B7AMFF0;l8l44=1B(B
> 19 =1B$BL>;l=1B(B-=1B$B?t=1B(B
> 40 =1B$BL>;l=1B(B-=1B$B%J%$7AMF;l8l44=1B(B
> 46 =1B$BF0;l=1B(B
> 47 =1B$BF0;l=1B(B-=1B$B<+N)=1B(B
> 50 =1B$B7AMF;l=1B(B
> 51 =1B$B7AMF;l=1B(B-=1B$B<+N)=1B(B
> 54 =1B$BI{;l=1B(B
> 55 =1B$BI{;l=1B(B-=1B$B0lHL=1B(B
> 56 =1B$BI{;l=1B(B-=1B$B=3Du;lN`@\B3=1B(B
> 57 =1B$BO"BN;l=1B(B
> 58 =1B$B@\B3;l=1B(B
> 75 =1B$B46F0;l=1B(B
> 81 =1B$B5-9f=1B(B-=1B$B%"%k%U%!%Y%C%H=1B(B
>=20
> Function words:
> 20 =1B$BL>;l=1B(B-=1B$BHs<+N)=1B(B
> 21 =1B$BL>;l=1B(B-=1B$BHs<+N)=1B(B-=1B$B0lHL=1B(B
> 22 =1B$BL>;l=1B(B-=1B$BHs<+N)=1B(B-=1B$BI{;l2DG=3D=1B(B
> 23 =1B$BL>;l=1B(B-=1B$BHs<+N)=1B(B-=1B$B=3DuF0;l8l44=1B(B
> 24 =1B$BL>;l=1B(B-=1B$BHs<+N)=1B(B-=1B$B7AMFF0;l8l44=1B(B
> 25 =1B$BL>;l=1B(B-=1B$BFC<l=1B(B
> 26 =1B$BL>;l=1B(B-=1B$BFC<l=1B(B-=1B$B=3DuF0;l8l44=1B(B
> 27 =1B$BL>;l=1B(B-=1B$B@\Hx=1B(B
> 28 =1B$BL>;l=1B(B-=1B$B@\Hx=1B(B-=1B$B0lHL=1B(B
> 29 =1B$BL>;l=1B(B-=1B$B@\Hx=1B(B-=1B$B?ML>=1B(B
> 30 =1B$BL>;l=1B(B-=1B$B@\Hx=1B(B-=1B$BCO0h=1B(B
> 31 =1B$BL>;l=1B(B-=1B$B@\Hx=1B(B-=1B$B%5JQ@\B3=1B(B
> 32 =1B$BL>;l=1B(B-=1B$B@\Hx=1B(B-=1B$B=3DuF0;l8l44=1B(B
> 33 =1B$BL>;l=1B(B-=1B$B@\Hx=1B(B-=1B$B7AMFF0;l8l44=1B(B
> 34 =1B$BL>;l=1B(B-=1B$B@\Hx=1B(B-=1B$BI{;l2DG=3D=1B(B
> 35 =1B$BL>;l=1B(B-=1B$B@\Hx=1B(B-=1B$B=3Du?t;l=1B(B
> 36 =1B$BL>;l=1B(B-=1B$B@\Hx=1B(B-=1B$BFC<l=1B(B
> 37 =1B$BL>;l=1B(B-=1B$B@\B3;lE*=1B(B
> 38 =1B$BL>;l=1B(B-=1B$BF0;lHs<+N)E*=1B(B
> 41 =1B$B@\F,;l=1B(B
> 42 =1B$B@\F,;l=1B(B-=1B$BL>;l@\B3=1B(B
> 43 =1B$B@\F,;l=1B(B-=1B$BF0;l@\B3=1B(B
> 44 =1B$B@\F,;l=1B(B-=1B$B7AMF;l@\B3=1B(B
> 45 =1B$B@\F,;l=1B(B-=1B$B?t@\B3=1B(B
> 48 =1B$BF0;l=1B(B-=1B$BHs<+N)=1B(B
> 49 =1B$BF0;l=1B(B-=1B$B@\Hx=1B(B
> 52 =1B$B7AMF;l=1B(B-=1B$BHs<+N)=1B(B
> 53 =1B$B7AMF;l=1B(B-=1B$B@\Hx=1B(B
> 59 =1B$B=3Du;l=1B(B
> 60 =1B$B=3Du;l=1B(B-=1B$B3J=3Du;l=1B(B
> 61 =1B$B=3Du;l=1B(B-=1B$B3J=3Du;l=1B(B-=1B$B0lHL=1B(B
> 62 =1B$B=3Du;l=1B(B-=1B$B3J=3Du;l=1B(B-=1B$B0zMQ=1B(B
> 63 =1B$B=3Du;l=1B(B-=1B$B3J=3Du;l=1B(B-=1B$BO"8l=1B(B
> 64 =1B$B=3Du;l=1B(B-=1B$B@\B3=3Du;l=1B(B
> 65 =1B$B=3Du;l=1B(B-=1B$B78=3Du;l=1B(B
> 66 =1B$B=3Du;l=1B(B-=1B$BI{=3Du;l=1B(B
> 67 =1B$B=3Du;l=1B(B-=1B$B4VEj=3Du;l=1B(B
> 68 =1B$B=3Du;l=1B(B-=1B$BJBN)=3Du;l=1B(B
> 69 =1B$B=3Du;l=1B(B-=1B$B=3D*=3Du;l=1B(B
> 70 =1B$B=3Du;l=1B(B-=1B$BI{=3Du;l!?JBN)=3Du;l!?=3D*=3Du;l=1B(B
> 71 =1B$B=3Du;l=1B(B-=1B$BO"BN2=3D=1B(B
> 72 =1B$B=3Du;l=1B(B-=1B$BI{;l2=3D=1B(B
> 73 =1B$B=3Du;l=1B(B-=1B$BFC<l=1B(B
> 74 =1B$B=3DuF0;l=1B(B
>=20
> Others (Not a word)
> 84 =1B$B$=3D$NB>=1B(B
> 85 =1B$B$=3D$NB>=1B(B-=1B$B4VEj=1B(B
> 86 =1B$B%U%#%i!<=1B(B
> 87 =1B$BHs8@8l2;=1B(B
> 88 =1B$B8lCGJR=1B(B
>=20
> Others (They don't have speech form)
> 0 BOS/EOS
> 39 =1B$BL>;l=1B(B-=1B$B0zMQJ8;zNs=1B(B
> 76 =1B$B5-9f=1B(B
> 77 =1B$B5-9f=1B(B-=1B$B0lHL=1B(B
> 78 =1B$B5-9f=1B(B-=1B$B6gE@=1B(B
> 79 =1B$B5-9f=1B(B-=1B$BFIE@=1B(B
> 80 =1B$B5-9f=1B(B-=1B$B6uGr=1B(B
> 82 =1B$B5-9f=1B(B-=1B$B3g8L3+=1B(B
> 83 =1B$B5-9f=1B(B-=1B$B3g8LJD=1B(B
>=20
>=20
> Khan Sakeb wrote:
> > Dear Sir,
> >   Thank you very much for your kind and prompt
> response.Let me apologize at first for my reply
> being late.I could successfully make classlist using
> the "ctext2class" command.But can you plz tell me
> which order do the words follow when they appear in
> the classlist?
> >   =20
> >   Now,let me focus on my research topic.I'm
> attaching a MS-WORD file along with this mail which
> describes the equations of my proposed language
> models.I'm confused about which approach to take for calcualting the=20
> probablities.At first, I just want to determine the probablity of the=20
> next word
> being=1B$B<+N)8l=1B(B(Ci=3D1)/=1B$BIUB08l=1B(B(Ci=3D0) in a tri-gram =
model.
> I mean =1B$B-t=1B(BP(Ci=3D1| Wi-2 Wi-1) or =1B$B-t=1B(BP(Ci=3D0| Wi-2 =
Wi-1).
> >   =20
> >   You mentioned in your mail to use some kind of
> tagger to distinguish between =1B$B<+N)8l=1B(B and =1B$BIUB08l=1B(B.
> Write now,I'm using 'CHASEN' for morphological
> analysis.Can you plz give me an idea about using
> CHASEN effectively to distinguish between =1B$B<+N)8l=1B(B and
> =1B$BIUB08l=1B(B. I think then I can use palmkit to generate
> more specific classlists.
> >   =20
> >   Thank you very much once again.I will be highly
> grateful if you kindly reply to my mail at your
> convenient time.
> >   =20
> >   With Regards
> >   upal1660
> > =20
> >=20
> >=20
> > ---------------------------------
> > Start Yahoo! Auction now! Check out the cool
> campaign
> >=20
> >=20
> >
>
------------------------------------------------------------------------
> >=20
> >
>
------------------------------------------------------------------------
-
> > Take Surveys. Earn Cash. Influence the Future of
> IT
> > Join SourceForge.net's Techsay panel and you'll
> get the chance to share your
> > opinions on IT & business topics through brief
> surveys - and earn cash
> >
>
http://www.techsay.com/default.php?page=3Djoin.php&p=3Dsourceforge&CID=3D=
DEVDE
V
> >=20
> >=20
> >
>
------------------------------------------------------------------------
> >=20
> > _______________________________________________
> > Palmkit-users-jp mailing list Pal...@li...
> >
>
https://lists.sourceforge.net/lists/listinfo/palmkit-users-jp
>=20
> --
> =1B$B0KF#=1B(B =1B$B>4B'=1B(B =1B$BElKLBg3X=1B(B =
=1B$BBg3X1!9)3X8&5f2J=1B(B
> Akinori Ito, Assoc. Prof.
> Graduate School of Engineering, Tohoku Univ.
> TEL: 022-795-7084 E-mail: ai...@fw...
>=20
>=20
>=20

--------------------------------------
Start Yahoo! Auction now! Check out the cool campaign
http://pr.mail.yahoo.co.jp/auction/

Dear Sir,
Thank you very much for your kind response and detailed
explanation.In fact,I dont have that much control on
Japanese language and I hope your advice will be an
immense help for me to distinguish between Content words
and Function words.I have also downloaded the paper
refered by you and I'm going to consult it carefully.

I think I will be able to tag the words successfully
following your advice.Now,after completion of tagging,is
it possible to calculate the probabilities of my proposed
LM using Palmkit?If so,can you tell me about the
process(or commands,in particular) of doing it??

Sorry for bothering you so many times.But your advices
have really really helped me a lot and let me express my
heartiest gratitude to you for spending your valuable time
for me.Thank you once again.

I'm looking forward to your reply.

With Regards

--- Akinori Ito <ai...@fw...> wrote:

> Hello,
> 
> I'm sorry for the late reply.
> 
> I read your Word file. The model you are going to
> make is exactly same one
> that was proposed by Isotani and Matsunaga in
> 1994[1]. If you haven't read
> their paper, you'd better to consult it.
> 
> [1] R. Isotani and S. Matsunaga, "A Stochastic
> Language Model for Speech
> Recognition Integrating Local and Global
> Constraints," Proc. ICASSP94,
> vol. II, pp. 5-8, 1994.
> 
> Now, we have a couple of way to distinguish content
> words and function words.
> If you are using Chasen, the easiest way is to make
> lists of content and
> function words. You can get a list of all parts of
> speech by "chasen -lp"
> command. Then, we can split the POS into the
> following classes.
> (The treatment of "others" classes depends on the
> purpose of the LM.)
> 
> Content words:
> 1 名詞
> 2 名詞-一般
> 3 名詞-固有名詞
> 4 名詞-固有名詞-一般
> 5 名詞-固有名詞-人名
> 6 名詞-固有名詞-人名-一般
> 7 名詞-固有名詞-人名-姓
> 8 名詞-固有名詞-人名-名
> 9 名詞-固有名詞-組織
> 10 名詞-固有名詞-地域
> 11 名詞-固有名詞-地域-一般
> 12 名詞-固有名詞-地域-国
> 13 名詞-代名詞
> 14 名詞-代名詞-一般
> 15 名詞-代名詞-縮約
> 16 名詞-副詞可能
> 17 名詞-サ変接続
> 18 名詞-形容動詞語幹
> 19 名詞-数
> 40 名詞-ナイ形容詞語幹
> 46 動詞
> 47 動詞-自立
> 50 形容詞
> 51 形容詞-自立
> 54 副詞
> 55 副詞-一般
> 56 副詞-助詞類接続
> 57 連体詞
> 58 接続詞
> 75 感動詞
> 81 記号-アルファベット
> 
> Function words:
> 20 名詞-非自立
> 21 名詞-非自立-一般
> 22 名詞-非自立-副詞可能
> 23 名詞-非自立-助動詞語幹
> 24 名詞-非自立-形容動詞語幹
> 25 名詞-特殊
> 26 名詞-特殊-助動詞語幹
> 27 名詞-接尾
> 28 名詞-接尾-一般
> 29 名詞-接尾-人名
> 30 名詞-接尾-地域
> 31 名詞-接尾-サ変接続
> 32 名詞-接尾-助動詞語幹
> 33 名詞-接尾-形容動詞語幹
> 34 名詞-接尾-副詞可能
> 35 名詞-接尾-助数詞
> 36 名詞-接尾-特殊
> 37 名詞-接続詞的
> 38 名詞-動詞非自立的
> 41 接頭詞
> 42 接頭詞-名詞接続
> 43 接頭詞-動詞接続
> 44 接頭詞-形容詞接続
> 45 接頭詞-数接続
> 48 動詞-非自立
> 49 動詞-接尾
> 52 形容詞-非自立
> 53 形容詞-接尾
> 59 助詞
> 60 助詞-格助詞
> 61 助詞-格助詞-一般
> 62 助詞-格助詞-引用
> 63 助詞-格助詞-連語
> 64 助詞-接続助詞
> 65 助詞-係助詞
> 66 助詞-副助詞
> 67 助詞-間投助詞
> 68 助詞-並立助詞
> 69 助詞-終助詞
> 70 助詞-副助詞／並立助詞／終助詞
> 71 助詞-連体化
> 72 助詞-副詞化
> 73 助詞-特殊
> 74 助動詞
> 
> Others (Not a word)
> 84 その他
> 85 その他-間投
> 86 フィラー
> 87 非言語音
> 88 語断片
> 
> Others (They don't have speech form)
> 0 BOS/EOS
> 39 名詞-引用文字列
> 76 記号
> 77 記号-一般
> 78 記号-句点
> 79 記号-読点
> 80 記号-空白
> 82 記号-括弧開
> 83 記号-括弧閉
> 
> 
> Khan Sakeb wrote:
> > Dear Sir,
> >   Thank you very much for your kind and prompt
> response.Let me apologize at first for my reply
> being late.I could successfully make classlist using
> the "ctext2class" command.But can you plz tell me
> which order do the words follow when they appear in
> the classlist?
> >    
> >   Now,let me focus on my research topic.I'm
> attaching a MS-WORD file along with this mail which
> describes the equations of my proposed language
> models.I'm confused about which approach to take for
> calcualting the probablities.At first, I just want
> to determine the probablity of the next word
> being自立語(Ci=1)/付属語(Ci=0) in a tri-gram model.
> I mean P(Ci=1| Wi-2 Wi-1) or P(Ci=0| Wi-2 Wi-1).
> >    
> >   You mentioned in your mail to use some kind of
> tagger to distinguish between 自立語 and 付属語.
> Write now,I'm using 'CHASEN' for morphological
> analysis.Can you plz give me an idea about using
> CHASEN effectively to distinguish between 自立語 and
> 付属語. I think then I can use palmkit to generate
> more specific classlists.
> >    
> >   Thank you very much once again.I will be highly
> grateful if you kindly reply to my mail at your
> convenient time.
> >    
> >   With Regards
> >   upal1660
> >  
> > 
> > 
> > ---------------------------------
> > Start Yahoo! Auction now! Check out the cool
> campaign
> > 
> > 
> >
>
------------------------------------------------------------------------
> > 
> >
>
-------------------------------------------------------------------------
> > Take Surveys. Earn Cash. Influence the Future of
> IT
> > Join SourceForge.net's Techsay panel and you'll
> get the chance to share your
> > opinions on IT & business topics through brief
> surveys - and earn cash
> >
>
http://www.techsay.com/default.php?page=join.php&p=sourceforge&CID=DEVDEV
> > 
> > 
> >
>
------------------------------------------------------------------------
> > 
> > _______________________________________________
> > Palmkit-users-jp mailing list
> > Pal...@li...
> >
>
https://lists.sourceforge.net/lists/listinfo/palmkit-users-jp
> 
> -- 
> 伊藤 彰則 東北大学 大学院工学研究科
> Akinori Ito, Assoc. Prof.
> Graduate School of Engineering, Tohoku Univ.
> TEL: 022-795-7084 E-mail: ai...@fw...
> 
> 
> 

--------------------------------------
Start Yahoo! Auction now! Check out the cool campaign
http://pr.mail.yahoo.co.jp/auction/

Hello,

I'm sorry for the late reply.

I read your Word file. The model you are going to make is exactly same one
that was proposed by Isotani and Matsunaga in 1994[1]. If you haven't read
their paper, you'd better to consult it.

[1] R. Isotani and S. Matsunaga, "A Stochastic Language Model for Speech
Recognition Integrating Local and Global Constraints," Proc. ICASSP94,
vol. II, pp. 5-8, 1994.

Now, we have a couple of way to distinguish content words and function words.
If you are using Chasen, the easiest way is to make lists of content and
function words. You can get a list of all parts of speech by "chasen -lp"
command. Then, we can split the POS into the following classes.
(The treatment of "others" classes depends on the purpose of the LM.)

Content words:
1 名詞
2 名詞-一般
3 名詞-固有名詞
4 名詞-固有名詞-一般
5 名詞-固有名詞-人名
6 名詞-固有名詞-人名-一般
7 名詞-固有名詞-人名-姓
8 名詞-固有名詞-人名-名
9 名詞-固有名詞-組織
10 名詞-固有名詞-地域
11 名詞-固有名詞-地域-一般
12 名詞-固有名詞-地域-国
13 名詞-代名詞
14 名詞-代名詞-一般
15 名詞-代名詞-縮約
16 名詞-副詞可能
17 名詞-サ変接続
18 名詞-形容動詞語幹
19 名詞-数
40 名詞-ナイ形容詞語幹
46 動詞
47 動詞-自立
50 形容詞
51 形容詞-自立
54 副詞
55 副詞-一般
56 副詞-助詞類接続
57 連体詞
58 接続詞
75 感動詞
81 記号-アルファベット

Function words:
20 名詞-非自立
21 名詞-非自立-一般
22 名詞-非自立-副詞可能
23 名詞-非自立-助動詞語幹
24 名詞-非自立-形容動詞語幹
25 名詞-特殊
26 名詞-特殊-助動詞語幹
27 名詞-接尾
28 名詞-接尾-一般
29 名詞-接尾-人名
30 名詞-接尾-地域
31 名詞-接尾-サ変接続
32 名詞-接尾-助動詞語幹
33 名詞-接尾-形容動詞語幹
34 名詞-接尾-副詞可能
35 名詞-接尾-助数詞
36 名詞-接尾-特殊
37 名詞-接続詞的
38 名詞-動詞非自立的
41 接頭詞
42 接頭詞-名詞接続
43 接頭詞-動詞接続
44 接頭詞-形容詞接続
45 接頭詞-数接続
48 動詞-非自立
49 動詞-接尾
52 形容詞-非自立
53 形容詞-接尾
59 助詞
60 助詞-格助詞
61 助詞-格助詞-一般
62 助詞-格助詞-引用
63 助詞-格助詞-連語
64 助詞-接続助詞
65 助詞-係助詞
66 助詞-副助詞
67 助詞-間投助詞
68 助詞-並立助詞
69 助詞-終助詞
70 助詞-副助詞／並立助詞／終助詞
71 助詞-連体化
72 助詞-副詞化
73 助詞-特殊
74 助動詞

Others (Not a word)
84 その他
85 その他-間投
86 フィラー
87 非言語音
88 語断片

Others (They don't have speech form)
0 BOS/EOS
39 名詞-引用文字列
76 記号
77 記号-一般
78 記号-句点
79 記号-読点
80 記号-空白
82 記号-括弧開
83 記号-括弧閉

Khan Sakeb wrote:
> Dear Sir,
>   Thank you very much for your kind and prompt response.Let me apologize at first for my reply being late.I could successfully make classlist using the "ctext2class" command.But can you plz tell me which order do the words follow when they appear in the classlist?
>    
>   Now,let me focus on my research topic.I'm attaching a MS-WORD file along with this mail which describes the equations of my proposed language models.I'm confused about which approach to take for calcualting the probablities.At first, I just want to determine the probablity of the next word being自立語(Ci=1)/付属語(Ci=0) in a tri-gram model. I mean P(Ci=1| Wi-2 Wi-1) or P(Ci=0| Wi-2 Wi-1).
>    
>   You mentioned in your mail to use some kind of tagger to distinguish between 自立語 and 付属語. Write now,I'm using 'CHASEN' for morphological analysis.Can you plz give me an idea about using CHASEN effectively to distinguish between 自立語 and 付属語. I think then I can use palmkit to generate more specific classlists.
>    
>   Thank you very much once again.I will be highly grateful if you kindly reply to my mail at your convenient time.
>    
>   With Regards
>   upal1660
>  
> 
> 
> ---------------------------------
> Start Yahoo! Auction now! Check out the cool campaign
> 
> 
> ------------------------------------------------------------------------
> 
> -------------------------------------------------------------------------
> Take Surveys. Earn Cash. Influence the Future of IT
> Join SourceForge.net's Techsay panel and you'll get the chance to share your
> opinions on IT & business topics through brief surveys - and earn cash
> http://www.techsay.com/default.php?page=join.php&p=sourceforge&CID=DEVDEV
> 
> 
> ------------------------------------------------------------------------
> 
> _______________________________________________
> Palmkit-users-jp mailing list
> Pal...@li...
> https://lists.sourceforge.net/lists/listinfo/palmkit-users-jp

-- 
伊藤 彰則 東北大学 大学院工学研究科
Akinori Ito, Assoc. Prof.
Graduate School of Engineering, Tohoku Univ.
TEL: 022-795-7084 E-mail: ai...@fw...

Dear all,
Good Day!
I have mentioned earlier that I'm a beginner in using
palmkit-1.0.31.I aim to make different language models for

自立語 and 付属語 in Japanese language..Can anyone plz
give me an idea about how can I do this using palmkit??I
was thinking of using the commands "text2class" and
"ctext2class".But they appear as 'plain text document' in
my downloaded palmkit-1.0.31 package.How can I change them
into 
実行可能ファイル?

Waiting eagerly for your reply.Thanks in Advance

With Regards
upal1660 

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