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sunpinyin for Debian -- rst --

SunPinyin is an SLM (Statistical Language Model) based input method engine (IME) developed by Sun Asian G11N Center. Currently, it supports IIIMF (Internet/Intranet Input Method Framework), SCIM (Smart Common Input Method) and BeCJK.

As a feature-rich IM, SunPinyin provides two input style: classic input style and instant input style.

Options in scim-sunpinyin

Scim-sunpinyin can be customized with SCIM Input Method Setup panel. Changing the setting of `input style' or `character set' will dismiss the un-committed preedit string.

Shortcut keys

Besides the keys which are customizable, scim-sunpinyin comes with some default shortcut key bindings not changeable. They are listed as following:

ctrl-backquote switch between classic/instant style.
only take effects while in Chinese input mode.

ctrl-k switch between gbk/gb2312 charset. shift switch between Chinese/English input mode. escape dismiss un-committed preedit string.

data files

Sunpinyin can hardly work without lexicon and language model data files. First, we can not ship the _really_ large dataset for training the language model. Second, for the sake of performance, we use our binary format for storing the lexicon and language model data.

So, to make sunpinyin usable, you may need to download the lexicon and language data file from open-gram website [2]. Two files are necessary for sunpinyin to work:

  • lm_sc.t3g, pydict_sc.bin
And we prepare two file for each of them:
  • slm_sc.t3g.{be,le}, pydict_sc.bin.{be, le}

The `be' and `le' stand for `Big Endian' and `Little Endian'. Download the right ones according to your computer's byte order, rename them to lm_sc.t3g and pydict_sc.bin respectively, then put them to /usr/share/sunpinyin/data.

Create your own language model

The statistical language model is the building block of SunPinyin. Users are allowed to train their language models by using the tools provided by sunpinyin-slm. (sunpinyin-slm is not packaged in Debian yet, but its source code is available at this input method's project home page [1].)

  • Terms
** Language model

The language model (LM) is used to describe the characteristic of a given languge. SLM is the attempt to capture the regularities of natural language using statistical approaches.

The LM shipped with SunPinyin is actually a data file named lm_sc.t3g. It was trained using a raw corpus collected from some Chinese web sites. lm_sc.t3g stands for Language Model Simplified Chinese Threading 3-Gram. The SunPinyin LM is an SLM which supports back-off and trigram.

** corpus
To build a LM, we need a good training data set, say, corpus. The raw corpus used by SunPinyin was collected from some web sites in Simpified Chinese. Actually, the corpus is just a large set of sentences or text in a given language.
** lexicon/dictionary

To segment the raw corpus into word tokens, we need a Chinese dictionary in which the tuple of the Chinese words and its frequency are stored.

There are two sets of lexicon used in SunPinyin: dict.utf8 and pydict_sc.bin.

*** dict.utf8
The word frequency in dict.utf8 which are used in the first iteration of segmentation of the raw corpus.
*** pydict_sc.bin
This data file is a trie presentation of the syllables and corresponding Chinese words, so that we can lookup the Chinese words with incomplete pinyin-prefix. This lexicon is also sorted by the unigram of previously trained LM.
  • How to train your own language model (LM)?

** Prerequisites

*** raw corpus
To train a new LM, a raw corpus should be prepared. There is no particular need for this raw corpus except that it is should be encoded in UTF-8.
*** sunpinyin-slm
And a suite of tools provided by sunpinyin-slm is also a necessary.
** Steps

To train a decent LM, we need go through two rounds of training process.

raw corpus ---> segmented corpus ---> n-gram result ---> back-off LM
---> pruned trigram ---> threaded LM ---> segment again using the result LM ---> ...
*** Segment the raw corpus
In this step, all words in raw corpus are indentified and are translated to the corresponding IDs. The ${DICTFILE} is always `dict.utf8'.
**** In the first round

Simply segment the raw corpus into words using MMF (Maximum Matching Forwarding segmentation algorithm).

./mmseg -d ${DICTFILE} -f bin -s 10 -a 9 ${CORPUSFILE} >${IDS_FILE}

**** In the second round
With the help of trained LM, we can get a better segmentation by: ./slmseg -d ${DICTFILE} -f bin -s 10 -m ${TSLM_FILE} ${CORPUSFILE} >${IDS_FILE}
*** Calculate the 3-gram

The number of all occurrence of 3-words tuple are calculated and written to file ${IDNGRAM_FILE}, like:

./ids2ngram -n 3 -s ${SWAP_FILE} -o ${IDNGRAM_FILE} -p 5000000 ${IDS_FILE}

*** Build a back-off trigram LM

To build a trigram LM using back-off smoothing model in ${RAW_LM_FILE} from original 3-gram result, just:

./slmbuild -n 3 -o ${RAW_LM_FILE} -w 120000 -c 0,2,2 -d ABS,0.005 -d ABS -d ABS,0.6 -b 10 -e 9 ${IDNGRAM_FILE}

*** Prune the raw back-off LM using an entropy based approach

To remove as many useless probabilities as possible without increasing the relative entropy, we exam the effect of removal of each n-gram to find out the useless ones, like:

./slmprune ${RAW_LM_FILE} ${SLM_FILE} R 100000 1250000 1000000

*** Thread the tree to speed up the looking up

To accelerate the speed of looking up, the lookup tree is threaded using:

./slmthread ${SLM_FILE} ${TSLM_FILE}

In this step, the ${TSLM_FILE} is just the language model, i.e. the file `lm_sc.t3g'.

-- [1] http://code.google.com/p/sunpinyin/downloads/list [2] http://code.google.com/p/open-gram/downloads/list

-- Kov Chai <tchaikov@gmail.com> Sun, 21 Mar 2010 01:48:32 +0800