Authorship Attribution (Juola, in press) is a form of text
analysis to determine the author of a given text. Authorship
Attribution in Chinese (AAC) is that task when the text
is written in Chinese. It can be considered as a typical
classifi cation problem, where a set of documents with known
authorship are used for training and the aim is to automatically
determine the corresponding author of an anonymous text.
Beyond simple questions of the identity of individual authors,
the underlying technology may also apply to gender, age, social
class, education and nationality.
JGAAP (Java Graphical Authorship Attribution Program) (Juola
et al., 2006) is a program aimed at automatically determining
a document’s author by using corpus linguistics and statistical
analysis. It performs different types of analysis and gives the
best results to the user while hiding the detailed methods and
techniques involved. It can therefore be used by non-experts.
We extend JGAAP to cover the special issues involved
in Chinese attribution and present the results of some
experiments involving novels and blogs.
Why can’t we just use the existing JGAAP software? Chinese
introduces its own problems, caused by the different structures
of English and Chinese. As with English, Chinese texts are
composed of sentences, sentences are composed of words,
and words are composed of characters. However, the Chinese
character set is approximately 50,000 individually meaningful
characters, compared with fi fty or so meaningless symbols for
English. Furthermore, in Chinese texts words are not delimited
by spaces as they are in English. As with English, the word is the
basic meaningful unit in Chinese, but the meaning of a word
may differ from the meaning of the characters compose this
Analysis at the character level is thus fundamentally different
between the two languages. Studies in English show that analysis
at the word level is likely to be a better way to understand
the style and linguistic features of a document, but it is not
clear whether this will apply to Chinese as well. So before we
can analyze word-level features (for comparison) we need to
segment sentences at word-level not by characters. Therefore
the fi rst step for any Chinese information processing system
is the automatically detection of word boundaries and
The Chinese version of JGAAP supports Chinese word
segmentation fi rst then followed by a feature selection process
at word level, as preparation for a later analytic phase. After
getting a set of ordered feature vectors, we then use different
analytical methods to produce authorship judgements.
Unfortunately, the errors introduced by the segmentation
method(s) will almost certainly infl uence the fi nal outcome,
creating a need for testing.
Almost all methods for Chinese word segmentation developed
so far are either structural (Wang et al., 1991) and statisticalbased
(Lua, 1990). A structural algorithm resolves segmentation
ambiguities by examining the structural relationship between
words, while a statistical algorithm usually compares word
frequency and character co-occurrence probability to
detect word boundaries. The diffi culties in this study are the
ambiguity resolution and novel word detection (personal
names, company names, and so on). We use a combination of
Maximum Matching and conditional probabilities to minimize
Maximum matching (Liu et al., 1994) is one of the most
popular structural segmentation algorithms, the process
from the beginning of the text to the end is called Forward
Maximal Matching (FMM), the other way is called Backward
Maximal Matching (BMM). A large lexicon that contains all the
possible words in Chinese is usually used to fi nd segmentation
candidates for input sentences. Here we need a lexicon that
not only has general words but also contains as many personal
names, company names, and organization names as possible
for detecting new words.
Before we scan the text we apply certain rules to divide the
input sentence into small linguistic blocks, such as separating
the document by English letters, numbers, and punctuation,
giving us small pieces of character strings. The segmentation
then starts from both directions of these small character
strings. The major resource of our segmentation system is
this large lexicon. We compare these linguistic blocks with the
words in the lexicon to fi nd the possible word strings. If a
match is found, one word is segmented successfully. We do this
for both directions, if the result is same then this segmentation
is accomplished. If not, we take the one that has fewer words.
If the number of words is the same, we take the result of
BMM as our result. As an example : Suppose ABCDEFGH is
a character string, and our lexicon contains the entries A, AB,
ABC, but not ABCD. For FMM, we start from the beginning of
the string (A) If A is found in the lexicon, we then look for AB
in the lexicon. If AB is also found, we look for ABC and so on,
till the string is not found. For example, ABCD is not found
in the Lexicon, so we consider ABC as a word, then we start
from character D unti the end of this character string. BMM
is just the opposite direction, starting with H, then GH, then
FGH, and so forth. Suppose the segmentation we get from FMM is
(a) A \ B \ CD \ EFG \ H
and the segmentation from BMM is
(b) A \ B \ C \ DE \ FG \ H
We will take result (a), since it has fewer
words. But if what we get from BMM is
(c) AB \ C \ DE \ FG \ H
We will take result (c), since the numbers
of words is same in both method.
After the segmentation step we take the advantage of
JGAAP’s features and add different event sets according to
the characteristics of Chinese, then apply statistical analysis
to determine the fi nal results. It is not clear at this writing,
for example, if the larger character set of Chinese will make
character-based methods more effective in Chinese then they
are in other languages written with the Latin alphabet (like
English). It is also not clear whether the segmentation process
will produce the same type of set of useful “function words”
that are so useful in English authorship attribution. The JGAAP
structure (Juola et al, 2006;Juola et al., submitted), however,
will make it easy to test our system using a variety of different
methods and analysis algorithms.
In order to test the performance on Chinese of our software,
we are in the process of constructing a Chinese test corpus.
We will select three popular novelists and ten novels from
each one, eight novels from each author will be used as training
data, the other two will be used as testing data. We will also
test on the blogs which will be selected from internet. The
testing procedure will be the same as with the novels.
This research demonstrates, fi rst, the JGAAP structure can
easily be adapted to the problems of non-Latin scripts and not
English languages, and second, provides somes cues to the best
practices of authorship attribution in Chinese. It can hopefully
be extended to the development of other non-Latin systems
for authorship attribution.
Patrick Juola, (in press). Authorship Attribution. Delft:NOW
Patrick Juola, John Noecker, and Mike Ryan. (submitted).
“JGAAP3.0: Authorship Attribution for the Rest of Us.”
Submitted to DH2008.
Patrick Juola, John Sofko, and Patrick Brennan. (2006). “A
Prototype for Authorship Attribution Studies.” Literary and
Linguistic Computing 21:169-178
Yuan Liu, Qiang Tan, and Kun Xu Shen. (1994). “The Word
Segmentation Rules and Automatic Word Segmentation
Methods for Chinese Information Processing” (in Chinese).
Qing Hua University Press and Guang Xi Science and
Technology Press, page 36.
K. T. Lua. (1990). From Character to Word. An Application
of Information Theory. Computer Processing of Chinese and
Oriental Languages, Vol. 4, No. 4, pages 304--313, March.
Liang-Jyh Wang, Tzusheng Pei, Wei-Chuan Li, and Lih-Ching
R. Huang. (1991). “A Parsing Method for Identifying Words
in Mandarin Chinese Sentences.” In Processings of 12th
International Joint Conference on Artifi cial Intelligence, pages
1018--1023, Darling Harbour, Sydney, Australia, 24-30 August
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