Single Character Chinese Named Entity Recognition 
Xiaodan Zhu, Mu Li, Jianfeng Gao and Chang-Ning Huang 
Microsoft Research, Asia  
Beijing 100080, China 
xdzhu@msrchina.research.microsoft.com 
{t-muli,jfgao,cnhuang}@microsoft.com 
 
 
Abstract 
Single character named entity (SCNE) is a 
name entity (NE) composed of one Chinese 
character, such as  “a0” (zhong1, China) 
and “a1” a2e2,Russiaa3. SCNE is very 
common in written Chinese text. However, 
due to the lack of in-depth research, SCNE 
is a major source of errors in named entity 
recognition (NER). This paper formulates 
the SCNE recognition within the source-
channel model framework. Our experi-
ments show very encouraging results: an F-
score of 81.01% for single character loca-
tion name recognition, and an F-score of 
68.02% for single character person name 
recognition. An alternative view of the 
SCNE recognition problem is to formulate 
it as a classification task. We construct two 
classifiers based on maximum entropy 
model (ME) and vector space model 
(VSM), respectively. We compare all pro-
posed approaches, showing that the source-
channel model performs the best in most 
cases. 
1 Introduction 
The research of named entity recognition (NER) 
becomes very popular in recent years due to its 
wide applications and the Message Understanding 
Conference (MUC) which provides a standard test-
bed for NER evaluation. Recent research on Eng-
lish NER includes (Collins, 2002; Isozaki, 2002; 
Zhou, 2002; etc.). Chinese NER research includes 
(Liu, 2001; Zheng, 2000; Yu, 1998; Chen, 1998; 
Shen, 1995; Sun, 1994; Zhang, 1992 etc.) 
In Chinese NEs, there is a special kind of NE, 
called single character named entity (SCNE), on 
which there is little in-depth research. SCNE is a 
NE composed of only one Chinese character, such 
as the location name “ a0” (zhong1,China) and 
“ a1” (e2,Russia) in the phrase “ a0a1a4a5” 
(zhong1-e2-mao4-yi4, trade between China and 
Russia). SCNE is very common in written Chinese 
text. For instance, SCNE accounts for 8.17% of all 
NE tokens according to our statistics on a 10MB 
corpus. However, due to the lack of research, 
SCNE is a major source of errors in NER. Among 
three state-of-the-art systems we have, the best F-
scores of single character location (SCL) and sin-
gle character person (SCP) are 43.63% and 43.48% 
respectively. This paper formulates the SCNE rec-
ognition within the source-channel model frame-
work. Our results show very encouraging 
performance. We achieve an F-score of 81.01% for 
SCL recognition and an F-score of 68.02% for 
SCP recognition. An alternative view of the SCNE 
recognition problem is to formulate it as a 
classification task. For example, “a0” is a SCNE 
in “a0a1a4a5”, but not in “a6a7a8a0”(bei3-jing1-
si4-zhong1, Beijing No.4 High School). We then 
construct two classifiers respectively based on two 
statistical models: maximum entropy model (ME) 
and vector space model (VSM). We compare these 
two classifiers with the source-channel model, 
showing that the source-channel model is slightly 
better. We then compare the source-channel model 
with other three state-of-the-art NER systems. 
The remainder of this paper is structured as 
follows: Section 2 introduces the task of SCNE 
recognition and related work. Section 3 and 4 pro-
pose the source-channel model and two classifiers 
for SCNE recognition, respectively. Section 5 pre-
sents experimental results and error analysis. Sec-
tion 6 gives conclusion. 
2 SCNE Recognition and Related Work 
We consider three types of SCNE in this paper: 
single character location name (SCL), person 
name (SCP), and organization name (SCO). Be-
low are examples: 
1.    SCL: “a0”and “a1” in “a0a1a4a5” 
2.  SCP: “a0” (zhou1, Zhou) in “a0a1a2” 
(zhou1-zong3-li3,Premier Zhou), 
3.  SCO: “a3” (guo2, Kuomingtang Party) 
and “a4” (gong4, Communist Party ) in 
“ a3a4 a5a6” (Guo2-gong4-he2-zuo4, 
Cooperation between Kuomingtang Part 
and Communist Party) 
SCNE is very common in written Chinese text. 
As shown in Table 1, SCNE accounts for 8.17% 
of all NE tokens on the 10MB corpus. Especially, 
14.65% of location names are SCLs. However, 
due to the lack of research, SCNE is a major 
source of errors in NER. In our experiments 
described below, we focus on SCL and SCP, 
while SCO is not considered because of its small 
number in the data.  
 
 # SCNE # NE #SCNE / #NE 
PN 5,892 129,317 4.56% 
LN 32,483 221,713 14.65% 
ON 356 122,779 0.29% 
Total 38,731 473,809 8.17%  
Table 1.  Proportion of SCNE in NE 
To our knowledge, most NER systems do not 
report SCNE recognition results separately. 
Some systems (e.g. Liu, 2001) even do not in-
clude SCNE in recognition task. SCNE recogni-
tion is achieved using the same technologies as 
for NER, which can be roughly classified into 
rule-based methods and statistical-based methods, 
while most of state-of-the-art systems use hybrid 
approaches. 
Wang (1999) and Chen (1998) used linguistic 
rules to detect NE with the help of the statistics 
from dictionary.  Ji(2001), Zheng (2000), 
Shen(1995) and Sun(1994) used statistics from 
dictionaries and large corpus to generate PN or 
LN candidates, and used linguistic rules to filter 
the result, and Yu (1998) used language model to 
filter. Liu (2001) applied statistical and linguistic 
knowledge alternatively in a seven-step proce-
dure. Unfortunately, most of these results are 
incomparable due to the different test sets used, 
except the results of Chen (1998) and Yu (1998). 
They took part in Multilingual Entity Task 
(MET-2) on Chinese, held together with MUC-7. 
Between them, Yu (1998)’s results are slightly 
better.    However, these two comparable sys-
tems did not report their results on SCNE sepa-
rately. To evaluate our results, we compare with 
three state-of-the-art system we have. These sys-
tems include: MSWS, PBWS and LCWS. The 
former two are developed by Microsoft® and the 
last one comes from by Beijing Language Uni-
versity.  
3 SCNE Recognition Using an Improved 
Source-Channel Model 
3.1 Improved Source-Channel Model1 
We first conduct SCNE recognition within a 
framework of improved source-channel models, 
which is applied to Chinese word segmentation.  
We define Chinese words as one of the following 
four types: (1) entries in a lexicon, (2) morpho-
logically derived words, (3) named entity (NE), 
and (4) factoid.  Examples are 
1. lexicon word: a7a8 (peng2-you3, friend). 
2. morph-derived word: a9a9a10a10 (gao1-gao1-
xing4-xing4 , happily) 
3. named entity:  a11a12a13a14(wei1-ruan3-gong1-
si1, Microsoft Corporation) 
4. factoid2: a15a16a17a18 (yi1-yue4-jiu3-ri4, Jan 9th)  
Chinese NER is achieved within the framework. 
To make our later discussion on SCNE clear, we 
introduce the model briefly. 
We are given Chinese sentence S, which is 
a character string. For all possible word segmen-
tations W, we will choose the one which 
achieves the highest conditional probability W* 
= argmax w P(W|S). According to Bayes’ law and 
dropping the constant denominator, we acquire 
the following equation: 
                                                      
1 This follows the description of (Gao, 2003). 
2 We define ten types of factoid: date, time (TIME), percent-
age, money, number (NUM), measure, e-mail, phone number, 
and WWW. 
)|()(maxarg* WSPWPW
W
=  (1) 
Following our Chinese word definition, we de-
fine word class C as follows: (1) each lexicon 
word is defined as a class; (2) each morphologi-
cally derived word is defined as a class; (3) each 
type of named entities is defined as a class, e.g. 
all person names belong to a class PN, and (4) 
each type of factoids is defined as a class, e.g. all 
time expressions belong to a class TIME. We 
therefore convert the word segmentation W into 
a word class sequence C. Eq. 1 can then be 
rewritten as: 
)|()(maxarg* CSPCPC
C
= . (2) 
Eq. 2 is the basic form of the source-channel 
models for Chinese word segmentation. The 
models assume that a Chinese sentence S is gen-
erated as follows: First, a person chooses a se-
quence of concepts (i.e., word classes C) to 
output, according to the probability distribution 
P(C); then the person attempts to express each 
concept by choosing a sequence of characters, 
according to the probability distribution P(S|C).  
We use different types of channel models 
for different types of Chinese words. This brings 
several advantages. First, different linguistic 
constraints can be easily added to corresponding 
channel models (see Figure 1). These constraints 
can be dynamic linguistic knowledge acquired 
through statistics or intuitive rules compiled by 
linguists. Second, this framework is data-driven, 
which makes it easy to adapt to other languages. 
We have three channel models for PN, LN and 
ON respectively. (see Figure 1) 
However, although Eq. 2 suggests that channel 
model probability and source model probability 
can be combined through simple multiplication, in 
practice some weighting is desirable. There are two 
reasons. First, some channel models are poorly 
estimated, owing to the sub-optimal assumptions 
we make for simplicity and the insufficiency of the 
training corpus. Combining the channel model 
probability with poorly estimated source model 
probabilities according to Eq. 2 would give the 
context model too little weight. Second, as seen in 
Figure 1, the channel models of different word 
classes are constructed in different ways (e.g. name 
entity models are n-gram models trained on cor-
pora, and factoid models are compiled using lin-
guistic knowledge). Therefore, the quantities of 
channel model probabilities are likely to have 
vastly different dynamic ranges among different 
word classes. One way to balance these probability 
quantities is to add several channel model weight 
CW, each for one word class, to adjust the channel 
model probability P(S|C) to P(S|C)CW. In our ex-
periments, these weights are determined empiri-
cally on a development set. 
Given the source-channel models, the procedure 
of word segmentation involves two steps: first, 
given an input string S, all word candidates are 
generated (and stored in a lattice). Each candidate 
is tagged with its class and the probability P(S’|C), 
where S’ is any substring of S. Second, Viterbi 
search is used to select (from the lattice) the most 
probable word segmentation (i.e. word class se-
quence C*) according to Eq. 2.  
Word class Channel model Linguistic Constraints 
Lexicon word (LW) P(S|LW)=1 if S forms a lexicon entry, 
0 otherwise.  
Word lexicon 
Morphologically derived word 
(MW) 
P(S|MW)=1 if S forms a morph lexicon 
entry, 0 otherwise.  
Morph-lexicon 
Person name (PN) Character bigram  family name list, Chinese PN patterns 
Location name (LN) Character bigram  LN keyword list, LN lexicon, LN abbr. list 
Organization name (ON) Word class bigram ON keyword list, ON abbr. List 
Factoid (FT) P(S|G)=1 if S can be parsed using a 
factoid grammar G, 0 otherwise 
Factoid rules (presented by FSTs). 
Figure 1. Channel models (Gao, 2003) 
3.2 Improved Model for SCNE Recognition 
Although our results show that the source-
channel models achieve the state-of-the-art word 
segmentation performance, they cannot handle 
SCNE very well. Error analysis shows that 
11.6% person name errors come from SCP, and 
47.7% location names come from SCL. There 
are two reasons accounting for it: First, SCNE is 
generated in a different way from that of multi-
character NE. Second, the context of SCNE is 
different from other NE. For example, SCNE 
usually appears one after another such as “a0a1
a2a3”. But this is not the case for multi-
character NE.  
To solve the first problem, we add two new 
channel models to Figure 1, that is, define each 
type of SCNE (i.e. SCL and SCP) as a individual 
class (i.e. NE_SCL and NE_SCP) with its chan-
nel probability P(Sj |NE_SCL), and P(Sj 
|NE_SCP). P(Sj |NE_SCL) is calculated by Eq. 3. 

=
= n
i
i
j
j
SSCL
SSCLSCLNE
1
|)(|
|)(|  )_|P(S  (3) 
Here, Sj is a character in SCL list which is ex-
tracted from training corpus. |SCL(Sj)| is the 
number of tokens Sj , which are labeled as SCL 
in training corpus. n is the size of SCL list, 
which includes 177 SCL. Similarly, P(Sj 
|NE_SCP) is calculated by Eq. 4, and the SCP 
list includes 151 SCP. 

=
= n
i
i
j
j
SSCP
SSCPSCPNE
1
|)(|
|)(|  )_|P(S  (4) 
We also use two CW to balance their channel 
probabilities with other NE’s. 
To solve the second problem, we trained a new 
source model P(C) on the re-annotated training 
corpus, where all SCNE are tagged by SCL or SCP.  
For example, “a4” in “a4a5a6”is tagged as SCP 
instead of PN, and “a0” in “a0a1” is tagged as 
SCL in stead of LN. 
4   Character-based Classifiers 
In this section, SCNE recognition is formulated 
as a binary classification problem. Our motiva-
tions are two folds. First, most NER systems do 
not use source-channel model, so our method 
described in the previous section cannot be ap-
plied. However, if we define SCNE as a binary 
classification problem, it would be possible to 
build a separate recognizer which can be used 
together with any NER systems. Second, we are 
interested in comparing the performance of 
source-channel models with that of other meth-
ods. 
For each Chinese character, a classifier is 
built to estimate how likely an occurrence of this 
Chinese character in a text is a SCNE. Some ex-
amples of these Chinese character as well as 
their probabilities of being a SCNE is shown in 
Table 2.  
 
a7a8a9 
a10a7a8a9a11a10a12a13a13
a14a15a16a17
a18a19
a20 a21a22
a23
a21
a24a23a25
a26 a21a22
a27a28a23a28a29
a30 a21a22
a29a27a31a28
a21
a32 a21a22
a28a33a25a28a23
a34 a21a22
a28a29a31a24a28
a35 a35
a36 a21a22a21a21a24a25a24
a37 a21a22a21a21
a24a27a23
a38 a21a22a21a21
a24a29a28
a39 a21a22a21a21a29a23a28
a40 a21a22a21a21
a29a29a31
 
a7a8a41
a10a7a8a41a11a10a12a13a13
a14a15a16a17
a18a19
a42 a21a22a29a24a27a24a29
a43 a21a22a29a27a31a28a21
a44 a21a22
a28a27a45a28a27
a46 a21a22
a28a29a25
a21a21
a47 a21a22
a28a28a28a28a28
a35 a35
a48 a21a22a21a21a21
a27a27
a49 a21a22a21a21a21
a27
a21
a50 a21a22a21a21a21
a27
a21
a51 a21a22a21a21a21
a29
a21
a52 a21a22a21a21a21
a28a33
 
Table 2.  The probability of a character as SCNE 
 
We can see that the probabilities of being a 
SCNE of many characters are very small. Thus, 
SCNE recognition is an ‘unbalanced’ classifica-
tion problem. That is, in most cases, it is safer to 
assume that a character is not a SCNE.  
We construct two classifiers respectively 
based on two statistical models: maximum 
entropy model (ME) and vector space model 
(VSM). Local context characters (i.e. left or right 
characters within a window) are used as features. 
4.1 Maximum Entropy 
ME provides a good framework to integrate 
various features from different knowledge 
sources. Each feature is typically represented as 
a binary constraint f. All features are then com-
bined using a log-linear model shown in Eq. 5. 
)),(exp()(1)|( =
i
ii yxfxZxyP λ
λ
λ  
(5) 
 
where a0 i is a weight of the feature fi , and Z(x) is 
a normalization factor. 
Weights (a0 ) are estimated using the maximum 
entropy principle: to satisfy constraints on ob-
served data and assume a uniform distribution 
(with the maximum entropy) on unseen data. The 
training algorithm we used is the improved itera-
tive scaling (IIS) described in (Berger et al, 
1996)3. The context features include six charac-
ters: three on the left of the SCNE, and three on 
the right. Given the context features, the ME 
classifier would estimate the probability of the 
candidate being a SCNE. In our example, we 
treat candidates with the probability larger than 
0.5 as SCNEs. To get the precision-recall curve, 
we can vary the probability threshold from 0.1 to 
0.9. 
4.2 Vector Space Model 
VSM is another model we used to detect SCNE. 
Similar to ME, we use six surrounding characters 
as the features,  as shown in Figure 2. 
 
Figure 2.  Context window 
 
In this approach, we apply the standard tf-idf 
weighting technique with one minor adaptation: 
the same character appearing in different 
positions within the context window is 
considered as different terms. For example, 
character Cj appearing at position i, ia1{-3,-2,-
1,1,2,3}, is regarded as term  Cji,. Term weight-
ing of Cji is acquired with Eq.6.   
ijijiji PWCIDFCTFCWei *)(*)()( =  (6) 
With this adaptation, we can apply an additional 
weighting coefficient PWi to different position, so 
as to reflect the importance of different positions. 
PWi is determined in a heuristic way as shown in 
Table.3 with the underlying principle that the 
closer the context character is to the SCNE candi-
date, the larger PWi is.  
                                                      
3 Thank Joshua Goodman for providing the ME toolkit. 
Pos -3 -2 -1 1 2 3 
PWi 1 4 7 7 4 1  
Table 3.  Weights assigned to deferent positions 
A precision/recall curve can be obtained by mul-
tiplying a factor to one of the two cosine dis-
tances we get, before comparing them.  
5   Experiment results  
5.1 Evaluation Methodology 
To achieve a reliable evaluation, we developed 
an annotated test set. First, we discuss a standard 
of Chinese NE and SCNE. Most previous re-
searches define their own standards; hence re-
sults of different systems are not comparable. 
Recently, two widely accepted standards were 
developed. They are (1) MET-2 (Multilingual 
Entity Task)4 for Chinese and Japanese NE, and 
(2) IEER-99’ 5  for Chinese NE. IEER-99 is a 
slightly modified version of MET-2. Our 
NE/SCNE standard is based on these two well-
known standards. 
Second, we manually annotated a 10MB 
training corpus and a 1MB test corpus. The texts 
are randomly selected from People’s Daily, in-
cluding articles from 10 subjects and 5 writing 
styles. This test set is much larger than MET-2 
test data (which is about 106 KB), and contains 
more SCNE for evaluation. 
The evaluation metrics we used include 
precision (P), recall (R), and F-score. F-score is 
calculated as 
RP
RP
+
+
)*(
**)0.1(
2
2
β
β , while 
a2 =1 in our 
experiments. 
 
5.2 Results of Source-Channel Models 
We show the SCNE recognition results using the 
source-channel models described in Section 3. 
Two versions of NE models are used. M1 is the 
original model described in Section 3.1. M2 is the 
one adapted for SCNE, shown in Section 3.2. The 
results in Table 4 show that obvious improvement 
can be achieved on SCL and SCP after adapting 
source-channel models for SCNE. As shown in 
Table 5, the improvement of SCL and SCP has 
significant impact on performance of LN and PN 
                                                      
4 http://www.itl.nist.gov/iaui/894.02/related_projects/muc/ 
5 http://www.nist.gov/speech/tests/ie-er/er_99/er_99.htm 
recognition. We can see that the increase of F-
score of LN is 5.13%, and PN is 0.92% absolutely. 
SCL SCP  
P% R% F P% R% F 
M1 83.77 15.25 25.80 * * * 
M2 84.38 77.90 81.01 76.14 61.47 68.02 
* No SCP is detected 
Table 4.  Improvement of SCL and SCP recognition     
LN PN  
P% R% F P% R% F 
M1 88.60 71.35 79.04 83.23 76.17 79.54 
M2 88.20 80.49 84.17 83.51 77.63 80.46 
Table 5.  Improvement of LN and PN recognition  
5.3 Results of Different Methods 
In Figures 3 and 4, we compare the results of the 
source-channel models with two classifiers de-
scribed in Section 4: ME and VSM. 
a0
a0a1a2
a0a1a3
a0a1a4
a0a1a5
a0a1a6
a0a1a7
a0a1a8
a0a1a9
a0a1a10
a2
a0 a0a1a2 a0a1a3 a0a1a4 a0a1a5 a0a1a6 a0a1a7 a0a1a8 a0a1a9 a0a1a10 a2
a11a12
a13a14a15a15
a16
a17
a18
a19
a20
a21
a20
a22
a23
a24
a25a26a27a28a29
a12a30 a31
a28a32a27a13
a12 a33a34
a14a35a35
a12
a15 a36a28
a30a12
a15 a36a37 a38
a31
a36
 
Figure 3.  Result of different methods on SCL 
 
a39
a39a40a41
a39a40a42
a39a40a43
a39a40a44
a39a40a45
a39a40a46
a39a40a47
a39a40a48
a39a40a49
a41
a39 a39a40a41 a39a40a42 a39a40a43 a39a40a44 a39a40a45 a39a40a46 a39a40a47 a39a40a48 a39a40a49 a41
a50a51a52a53a54a54
a55
a56
a57
a58
a59
a60
a59
a61
a62
a63
a64a65a66a67a68
a51a69 a70
a67a71a66
a52a51 a72a73a53
a74a74
a51a54 a75
a67
a69a51a54 a75a76 a77a70a75
 
Figure 4.  Result of different methods on SCP 
 
We can see that source-channel model achieves 
the best result. This can be interpreted as follows. 
The source-channel models use more informa-
tion than the other two methods. The feature set 
of ME or VSM classifiers includes only six sur-
rounding characters while the source-channel 
models use much rich global and local informa-
tion as shown in Figure 1. Based on our analysis, 
we believe that even enlarging the window size 
of the local context, the performance gain of 
these classifiers is very limited, because most 
error tags cannot be correctly classified using 
local context features. We can then say with con-
fidence that the source-channel models can 
achieve comparable results with ME and VSM 
even if they used more local context. 
5.4 Comparison with Other State-of-The-Art 
Systems 
The section compares the performance of the 
source-channel models M2, with three state-of-
the-art systems: MSWS, LCWS and PBWS. 
1. The MSWS system is one of the best avail-
able products. It is released by Microsoft® (as 
a set of Windows APIs). MSWS first con-
ducts the word breaking using MM (aug-
mented by heuristic rules for disambiguation), 
then conducts factoid detection and NER us-
ing rules. 
2. The LCWS system is one of the best research 
systems in mainland China. It is released by 
Beijing Language University. The system 
works similarly to MSWS, but has a larger 
dictionary containing more PNs and LNs. 
3. The PBWS system is a rule-based Chinese 
parser, which can also output the NER results. 
It explores high-level linguistic knowledge 
such as syntactic structure for Chinese word 
segmentation and NER. 
To compare the results across different systems, 
we have to consider the problem that they might 
have different tagging format or spec. For exam-
ple, the LCWS system tags the two-character 
string “ a78a79” as a location name, and tags “a80
a81a82” other than “
a80” as a  person name. We 
then manually convert all tagging results of these 
three systems according to our spec. The results 
are shown in Table 6. 
* No SCL is detected 
SCL SCP 
P% R% F R% P% F 
a0a1a2a1 a3 a3 a3 a4a5
a6
a7a8 a9a5
a6
a8a10 a7a9
a6
a7a11
a12a13a2a1 a5a14a14
a6
a14 a8a5
a6
a7a9 a9a10
a6
a9a14 a15a15
a6
a15a4 a8a10
a6
a14a14 a9a15
a6
a9a15
a16a17a2a1 a18a8
a6
a9a5 a8a11
a6
a10a4 a7a9
a6
a15a9 a5a14a14
a6
a14 a5a11
a6
a4a10 a9a5
a6
a10a11
a0a8 a11a7
a6
a9a11 a4a4
a6
a18a14 a11a5
a6
a14a5 a4a15
a6
a5a7 a15a5
a6
a7a4 a15a11
a6
a14a8
Table 6.  Comparison with other systems 
We can see that our system (M2) achieves the 
best results in both SCL and SCP recognition. 
PBWS has the second best result in recognizing 
SCL (43.63%), and MSWS in SCP (43.48%). 
However, they achieved the worst result on SCP 
and SCL, respectively.  
5.5 Error Analysis 
Through human checking, we list the typical er-
rors as follows:  
1. Ambiguity between NE: “a19a78a20a21a22a23a24
a25a26”(mei3-zhong1-mao4-yi4-quan2-guo2-
wei3-yuan2-hui4, National Committee on 
United States-China Relations) is a ON, but 
“a19”, “ a78” are usually wrongly recognized 
as SCL. On the contrary, “a27a78a28a29a30a31
a32”(ri4- zhong1-you3-hao3-qi1-tuan2-ti3, 
seven Janpan-China  friendship organiza-
tions )  is tagged as ON falsely. So 
“a27”(ri4,Japan), “ a78” are missed.  
2. SCNE list acquire from training data cannot 
covers some cases in test data: “ a78a33a34a35
a36 ”(zhong1-ka3-zu2-qiu2-sai4, China-
Qatar soccer match), “ a33”(ka3, Qatar)  
here is stand for “ a33a37a38”(ka3-ta1-er3, 
Qatar), which is out of SCL list.  
3. Other errors: “ a78a39a40a41a42a43”(zhong1-ba1-
shi3-chu1-da4-men2, the middle bus drives 
out from the gate),“ a78”(zhong1, middle), 
“a39” (ba1, bus)are recognized falsely as 
SCL. Because“ a78” and “a39” can also stand 
for China and Pakistan.“ a39” can even 
stand for other countries such as “a39a44”
a45ba1-xi1, Brazil
a46. 
Errors in (1) account for about 40% of all errors. 
SCNE is usually a part of multi-character NE, such  
as “a19”, “ a78” in “a19a78a20a21a22a23a24a25a26”.Viterbi 
search has to make a decision: recognizing the 
multi-character NE, or recognizing SCNE. Current 
features we used seem not powful enough to 
resovle this ambiguity well. Errors in (3) come 
from another kind of ambiguities such as 
ambiguity between SCNE and normal lexicon 
words. They are partly caused by noises in training 
data, because SCNE are very likely to be neglected 
by annotators, which makes training data more 
sparse. Both errors in (1) and (3) are not easy to 
handle. 
Our immediate work is to cope with errors in 
(2), which account for about 8.9% of all errors. We 
can obtain additional SCNE entries from resources 
such as abbreviation dictionaries. However, the 
procedure to select SENE entries should be careful, 
because the SCNE characters we do not cover 
currently might be rare to act as SCNE, and 
difficult to recall. Besides, unsupervised methods 
can be applied to the task, considering 
insufficiency of the training data of the task. 
6   Conclusion 
Although SCNE is very common in written Chi-
nese text, due to the lack of in-depth research, 
SCNE is a major source of errors in NER. This 
paper formulates the SCNE recognition within the 
source-channel model framework. Our experi-
ments show very encouraging results: an F-score 
of 81.01% for single character location name rec-
ognition, an F-score of 68.02% for single character 
person name recognition. An alternative view of 
the SCNE recognition problem is to formulate it as 
a classification task. We construct two classifiers 
respectively based on maximum entropy model 
(ME) and vector space model (VSM), respectively. 
We compare all proposed approaches, showing 
that the source-channel model performs the best in 
most cases. 

References 
A. Berger, S. Della Pietra and V. Della Pietra, 1996. A 
maximum entropy approach to natural language 
processing. Computational Linguistics 22(1):39-71. 
Andrew Borthwick, John Sterling, Eugene Agichtein, 
and Ralph Grishman. 1998. NYU: Description of the 
MENE Named Entity System as Used in MUC-7. 
Proceedings of the Seventh Message Understanding 
Conference (MUC-7)  
 Hsin-Hsi Chen, Yung-Wei Ding, Shih-Chung Tsai and 
Guo-Wei Bian, 1998. Description of the NTU Sys-
tem used for MET-2, Proceedings of the Seventh 
Message Understanding Conference (MUC-7). 
Michael Collins, 2002. Ranking Algorithm for Named-
Entity Extraction: Boosting and Voted Perceptron.  
Proceedings of 40th Annual Meeting of Association 
for Computational Linguistics. 
Jianfeng Gao, Mu Li and Chang-Ning Huang, 2003. 
Improved Source-Channel Models for Chinese Word 
Segmentation. Proceedings of 41th Annual Meeting 
of Association for Computational Linguistics. 
Hideki Isozaki and Hideto Kazawa, 2002. Efficient 
Support Vector Classifier for Named Entity Recogni-
tion, Proceedings of 19th International Conference 
on Computational Linguistics. 
Heng Ji, Zhensheng Luo, 2001. Inverse Name Fre-
quency Model and Rule Based Chinese Name Identi-
fication. (In Chinese) Natural Language 
Understanding and Machine Translation. Tsinghua 
University Press.  pp. 123 -128. 
Kaiying Liu, 2001. Research on Chinese Proper Noun 
and Internet Words Recognition. (In Chinese) Pro-
ceedings of Conference of the 20th Anniversary of 
CIPSC. Tsinghua University Press. pp. 7-13. 
Song Rou, Benjamin K T’sou, 2001. Primary Study on 
Chinese Proper Noun. (In Chinese) Proceedings of 
Conference of the 20th Anniversary of CIPSC. 
Tsinghua University Press,14-19. 
Dayang Shen, Maosong Sun, 1995. Chinese Location 
Name Recognition. (In Chinese) Development and 
Applications of Computational Linguistics. Tsinghua 
University Press.  
Jian Sun, Jianfeng Gao, Lei Zhang, Ming Zhou, and 
Changning Huang, 2002. Chinese named entity iden-
tification using class-based language model. 
COLING 2002. Taipei, Taiwan, August 24-25, 2002. 
Maosong Sun, Changning Huang, Haiyan Gao, Jie Fang, 
1994. Chinese Person Name Recognition.  (In Chi-
nese) Journal of Chinese Information Processing. 
Xing Wang, Degen Huang, Yuansheng Yang, 1999. 
Identifying Chinese Names Based on Combination of 
Statistics and Rules.  (In Chinese) proceedings of 
JSCL-99. Tsinghua University Press. pp. 155 -161 
Shihong Yu, Shuanhu Bai and Paul Wu.  Description of 
the Kent Ridge Digital Labs System Used for MUC-
7. Proceedings of the Seventh Message Understand-
ing Conference (MUC-7), 1998. 
Junsheng Zhang, 1992. Chinese Person Name Recogni-
tion.  (In Chinese) Journal of Chinese Information 
Processing. 9(2) . 
Jiahen Zheng, Xin Li, Hongye Tan, 2000. The Researh 
of Chinese Names Recognition Based on Corpus,. (In 
Chinese) Journal of Chinese Information Processing. 
14(1): 7-12  
Guodong Zhou and Jian Su, 2002. Named Entity Rec-
ognition using an HMM-based Chunk Tagger. Pro-
ceedings of 40th Annual Meeting of Association for 
Computational Linguistics. 
