Chinese-English Translation of Company Names and Addresses in a

International Journal of Advanced Intelligence 

Volume 3, Number 2, pp.229-241, July, 2011. 

c⃝ AIA International Advanced Information Institute 

Chinese-English Translation of Company Names and Addresses in a 

Large-scale Database 

Tiejun Zhao, Conghui Zhu, Muyun Yang 

School of Computer Science and Technology, Harbin Institute of Technology, 92 West Dazhi St. 

Harbin, 150001, China 

{tjzhao, chzhu, ymy}@mtlab.hit.edu.cn 

Yingjian Gu, Gang Zhou, Youyou Hong 

National Administration for Code Allocation to Organizations, 46 Yuzhong Xili, Yumin Rd. 

Beijing,100029,China 

{guyingjian, zhougang, olyvia}@nacao.org.cn 

Received (9 January 2011) 

Revised (7 April 2011) 

The article presents a large-scale of application of named entity translation for company names and 

addresses on the database with ten million of entries managed by the National Administration for Code 

Allocation to Organizations in China. Based on the structure analysis of company names and addresses, 

a rule-based approach is applied with strategies of active learning and stepwise refinement. Enhanced 

by translation knowledge acquisitions on domain terms and specific region names the experimental tests 

indicate that the system has achieved a promising performance. 

Keywords: Machine Translation; Named Entity; Active Learning; Stepwise Refinement. 

1. Background 

With China’s accelerated economic growth, it becomes pressing to share the information 

of industry and commerce between China and its trading partners. The basic information of 

an industrial company or a commercial company is the description record about its name, 

category, address etc. In China, the National Administration for Code Allocation to Organizations 

(NACAO) takes charge to collect and manage all of above records for industrial and 

commercial companies registered in mainland. The total amount of the records has reached 

up to ten million entries in the database. As there is a high demand in international trade 

for translating these Chinese records into English, it is a desirable solution by providing the 

information of a company through the bilingual website of NACAO. 

To provide the bilingual website for the information search of Chinese companies, one 

of the crucial tasks is to translate their names and addresses from Chinese into English. Here 

is Example 1 for the name and address of a company: 

Company Name (CN): 苏州市华佳照明器材有限公司 

(in English: Suzhou Huajia Lighting Appliance Co. Ltd.) 

229

230 T. Zhao, C. Zhu, M. Yang, Y. Gu, G. Zhou and Y. Hong 

Address: 苏州市三香路 159 号嘉理大厦 14 楼 D 座 

(in English: Unit D, Floor 14, Jiali Mansion, 159 Sanxiang Rd., Suzhou) 

Obviously, it is impractical to translate manually such large amount of names and addresses. 

The structure regularity of Chinese company names and addresses and huge number 

of records make the task a “killer application” for machine translation (MT), or for 

Named Entity (NE) translation to be specific. These names and addresses are recorded in 

the database, not identified in texts. Although the task skips a difficult phase—NE identification, 

there exist at least two challenges for this large-scale engineering of Chinese-English 

(C-E) translation: (1) analyzing complex structures of CNs and addresses which contain 

multiple sub NEs; (2) identifying large amounts of terms contained in the names when it is 

necessary. 

This paper presents a solution to these challenges with the sections that are arranged as 

following: Section 2 gives an overview of techniques and strategies that we adopted in the 

research and development of the translation system after a brief introduction on NE translation 

approaches. Then we have detailed descriptions on the structure analysis and translation 

of company names and addresses in Section 3 and Section 4, respectively. The experiments 

and tests to the translation system are summarized in Section 5 and the conclusion is given 

in the last section. 

2. Methodology 

2.1. Brief review for NE translation approaches 

NE is first introduced in 1995 by the Message Understanding Conference (MUC-6), 

and it contains mainly 4 types of names or expressions: the name of a person, an organization, 

a location(or address), and numeric expressions including time, date, money and 

percentage. NE translation has drawn wide attention since the end of last century 1 as it can 

effectively improve the performance of translation systems and cross-language information 

retrieval applications, e.g. cross-language question answering 2,3,4 . The main approach for 

NE translation is the transliteration, and the combination of transliteration and free translation 

is needed for translating complex NEs such as organization names and addresses 5 . 

The basic approach to translate NEs is to use the pre-compiled NE translation dictionaries, 

but these static dictionaries cannot guarantee the coverage for new names especially 

for rarely occurring names even with a large size of vocabulary. Usually, statistical models 

for transliteration are trained on the bilingual NE dictionaries or corpora to overcome the 

limitation of NE translation dictionaries. There are many approaches proposed in Natural 

Language Processing(NLP) community to enhance the dictionary approach for NE translation. 

The pair of a NE and its translation may be widely contained in Web or some corpora, 

so the resources and search engines are usually used as means of acquiring correct translation. 

Ref. 6 proposed an approach to compare the Chinese NE with English NE candidates 

in retrieved documents based on calculations of their phonetic and semantic similarities, 

and then select the best-matched one as the translation on the target side. Learning algorithms 

were presented in Ref. 7 to get promising experimental results for the NE matching

Chinese-English Translation of Company Names and Addresses 

231 

(i.e. translation) between Chinese and English. Research in Ref. 8 showed that distinguishing 

different classes of NEs was helpful to produce better NE translation. And a typological 

model to handle types of NEs was applied in a rule-oriented NE translation system for the 

specific domain 9 . 

A company name is one type of organization names with composite structures, as well 

as for an address. For translating such a NE, the first step is to divide the NE into several 

basic units which will be translated by either transliteration approach (if the unit belongs to a 

given name or a location name) or free translation approach (if the unit indicates the category 

or the business scope of a company). The crucial technique for correct translations of these 

NEs is to get correct segmentations of basic translation units. Ref. 10 adopted chunk-based 

approach to segment Chinese organization names and applied Web-mining to find the best 

English counterparts. The structures of Chinese organization names were analyzed in Ref. 

11 and a structure-based approach was proposed for translating organization names directly. 

All of above approaches for NE translation needed bilingual translation resource and 

did not guarantee higher precision of NE especially CN translations. 

2.2. Overview on proposed techniques and strategies 

As there are Chinese CNs and addresses in NACAO’s database without a proper C-E 

bilingual corpus, we do not use the approach of statistical machine translation to create a 

NE translation model. In fact, low precision of automatic alignment (e.g. GIZA++ output) 

cannot satisfy the actual translation needs of CNs and addresses. The basic techniques for 

the development of our translation system are based on classes of rules and dictionaries. 

As with general organization names, it is crucial to segment CNs and addresses into 

proper translation units. Then we can apply translation rules or search proper dictionaries 

to produce their accurate translations. We face the challenges mentioned in section 1: what 

strategies should be adopted to guarantee the full coverage for the identification of terms and 

sub-structures in nearly open-size of CNs and addresses when we use rules and dictionaries 

In other words, how do we build the architecture to keep translation knowledge increasing 

with the growing new input 

The proposed strategies here for the solution of above challenges include three components: 

(i) Initial construction of translation knowledge bases (KBs). First, we manually analyzed a 

certain size of the set of CNs and addresses to confirm their internal structures. According 

to the naming rules announced by the State Administration for Industry and Commerce 

(SAIC) of China, the name of a company registered should contain the following four 

parts in sequence: region name, given name, business domain, and company category. 

And, generally, a complete Chinese address consists of three parts with the following 

order: region name, street name and number, and apartment number (sometimes with 

the building name). But a complex address will contain an organization name as one 

of its component as well (see more detail in Sec. 3 and 4). Thus for the translations 

of these parts in CNs or addresses, we can create one class of translation KBs for the 

closed sets, e.g. the set of region names from the administrative divisions and their simple

verifying Manual 

for the iterations 

of 

Fig. 

Extraction 

1. The process of Stepwise Refinement to improve the translation system. 

KBs translation 


translations in Chinese Pinyin. As for the semi-closed set, e.g. the names of business 

domain, their translations can be extracted from an initial C-E dictionary. For the open 

sets, e.g. given names of a company, or street names, each character of them is translated 

in Chinese Pinyin. 

(ii) Active learning for the identification of incorrect translations. Because of the limitation 

in the translation KBs for semi-closed and open sets, the initial KBs cannot translate a 

complete company name for most entries in the CN database. Almost all those incorrect 

translations are caused by segmentation errors in business domain or given name. Similarly, 

most incorrect translations for addresses take place in the street name if there is a 

preceding local region name which is out of the administrative division list, or take place 

in the building name if there is a segmentation ambiguity between it and the street name. 

We propose a segmentation strategy with the principle of active learning to identify the 

error candidates in such parts and verify them manually. 

(iii) Iteration of stepwise refinement for the enlargement of KBs. As the small size of initial 

translation KBs, large amount of translation errors occurred at the beginning while we 

applied them for the large-scale of translation in whole CN and address database. Practically, 

we controlled the size of those translation errors waiting for manual correction 

with frequency threshold. Those candidates will be verified to acquire more translation 

knowledge. After being accumulated translation knowledge from many small-size translation 

experiments, we run several large-scale translation tests on half-size or full-size 

databases. During every iteration of the experiment or the test, the translation KBs are 

extended and refined. The process of stepwise refinement will stop till that no more 

knowledge can be added to KBs. Fig. 1 illustrates such a process. 

errors System running system improvements Translation 

Expansion of KBs 

Initial

Fig. 2. The CN structure and corresponding translation resource. 

KB translation 


233 

3. The translation of CNs 

3.1. The structures of Chinese CNs 

We have mentioned that the key for Chinese CN and address translation is to analyze 

their structure correctly. For Chinese companies, their names are given according to the 

naming rules announced by SAIC and include four parts in sequence: 

(i) the name of the region where the company locates. In general, a region can be a 

province, or a city, or a county listed in the administrative divisions of China; or two 

regions that the latter one is the sub-division of the former one. For instance, “ 苏州 ” 

is a city name in Ex. 1. Sometimes such a region name is followed by an administrative 

division mark, e.g. “ 市 ”(city) in the instance, or “ 省 ”(province), or “ 县 ” 

(county). All region names of counties or larger administrative divisions are included 

in a closed-set with Chinese Pinyin as their translations. 

(ii) given name of the company. Any composition of Chinese characters can be used, but 

conventionally the given name is in two (over 50 percent), three, or four characters. 

Although it is an open-set, the translation is simple, just Pinyin, e.g. “ 华佳 ”(Huajia) 

in the above example. 

(iii) the name of the business domain that indicates the business scope of the company. This 

part usually contains one or more terms to represent what kind of products produced 

or sold by the company or what kind of service provided by the company. “ 照明器 

材 ”(lighting appliance) explains the trading scope of the company in Ex 1. 

(iv) the general category name of the company, e.g. factory ( 厂 ), Co. Ltd. (“ 有限公司 ” 

in the above example), etc. It is a closed-set, but more organization category names are 

included into this translation KB, e.g. “ 局 ”(bureau) because sometimes a company 

may subject to another organization. 

The four parts of the CN in Ex. 1 are illustrated in Fig. 2. In the base of the figure, there 

are translation resource to support the translation of Part 1, Part 3 and Part 4. 

1 

州市 Suzhou 华佳苏 

2 3 4 

照明器材 LightingAppliance 有限公司 Co.Ltd. Huajia 

translation KB 

divisions 

a ministrative 

terminology bilingual 

category company


3.2. Pattern matching for CN segmentation and translation 

Based on the structure analysis of CN, the basic translation units should match the four 

parts of an CN. It is crucial for the CN translation to correctly segment a CN into four parts. 

We implement the Max-Matching segmentation approach. As the two ends of a CN, i.e. Part 

1 and Part 4 have a closed-set of matching elements, exact segmentations can be achieved 

in these two parts. So the strategy of “two-ends to center” is employed during segmenting, 

i.e. starting the matching progress from the beginning and the end of a CN string and then 

moving to the center of the string. The process of pattern matching is listed in the below: 

(i) To segment Part 1 and Part 4 by matching translation KBs for the administrative divisions 

and the company categories, respectively, and return the translations of two 

parts. 

(ii) To segment Part 3 by matching a term translation KB whose size is expanded in iterations, 

and return the translations. 

(iii) To check the left string excluding Part 1, 3 and 4: if the length of the left string is 

larger than 4, an additional translation KB is used for identifying the local region name 

which is under a county in the hierarchy of the administrative divisions. Such a name 

is generally followed to Part 1. 

(iv) Otherwise, the left string as the given name of a company is translated by Pinyin of its 

characters; specially, 4 characters will be divided into 2 words in the output to satisfy 

the two-syllable convention of Chinese. 

Each translation rule in KBs is in the following form: 

MatchUnit → TransAssign + UnitMark [+ PositionAssign] (1) 

In Form 1, MatchUnit is a matched Chinese word in every part while TransAssign 

gives its translation. UnitMark indicates the class of matched word, e.g. a city or a county, 

a company or a branch, etc. The mark is helpful to recognize sequential region names and 

company names in one CN.PositionAssign is the sequence number in English output and 

is used in the address translation for reordering of every translation units. 

4. The translation of addresses 

4.1. The structures of Chinese addesses 

Usually, a Chinese address includes 3 parts as follows with a sequence from a larger 

region-unit to a small one: 

(i) the region names with the order that is in the hierarchy of the administrative divisions, 

i.e. the name of a province or an autonomous region or a municipality; then the name 

of a city or a county belongs to the former part; furthermore, the name of a district 

belongs to the city or a town belongs to a county. 

(ii) the street name and the doorplate number, E.g. “ 三香路 ”(Sanxiang Rd.) and “159 

号 ”(No. 159) in Ex. 1.

D, Floor 14, Jiali Mansion Tower 

No. 159, Sanxi ang Road Suzhou 

Fig. 3. The address structure and corresponding translation resource 

KB translation 


235 

(iii) As most Chinese in cities live in apartments, a doorplate does not mean only a house 

but may refer to a building with many apartment units. So the address should further 

indicate the unit number with the floor number, even with the building name or number 

if there are several buildings sharing the same number in the street. In Ex. 1, “ 嘉理 

大厦 14 楼 D 座 ”(Unit D, Floor 14, Jiali Mansion) is a typical instance. 

The three parts of the address in Ex. 1 are illustrated by Fig. 3. Similar to Fig. 2, there 

are several translation KBs displayed in the bottom of the figure. Among these KBs, the 

translation KB for the administrative divisions is same as the one for CN translation. Mark 

KB is closed-set of those location words, e.g. “ 街 ”(street), “ 路 ”(road), “ 大厦 ” 

(mansion), “ 小区 ”(quarters), etc. Some new names for special administrative regions 

such as “ 经济技术开发区 ”(Economy and Technology Development Zone) often appear 

in addresses and they are clustered in one translation KB. 

1 2 3 

香路 159 号嘉理大厦 14 楼 D 座三市州苏 

divisions translation KB Mark KB & special location administrative 

For some non-standard addresses in NACAO database, there exists such an address 

structure: an CN will be contained in it. In such case, the identification process for CN 

should be fired if an organization category name is found. Then the CN translations will be 

returned from the sub-process and merged into the whole translation of the address. 

4.2. Pattern matching for address segmentation and translation 

According to the convention of a Chinese address, the pattern matching from left to 

right is used and the order from large to small address unit is accepted as normal sequence. 

The process of pattern matching is also the process to segment the basic translation units 

of an address with Max-Matching approach. Once a correct segmentation of units is got, 

their English translation will have a reverse order, i.e. from small to large address unit. The


pattern matching for an address is represented as follow: 

(i) The segmentation starting from the leftmost with matching of the administrative divisions. 

It is similar to the matching of Part 1 in CN translation. 

(ii) The matching of local region names if existing. An address often contains the region 

name which is under a county or a district in a city in the hierarchy of the administrative 

divisions. These small region names can be identified with location words. Here is an 

example: “ 上海市松江区新桥镇莘松路 1115 弄 145 号 ”(No. 145, Lane 1155, 

Xinsong Road, Xinqiao Town, Songjiang District, Shanghai). “ 新桥镇 ”(Xinqiao 

Town) is a town belonging to the Songjiang District which can be recognized by the 

function word “ 镇 ”(town). 

(iii) The matching of specific region names if existing. Some new names of special zones 

may appear in an address, for instance: “ 天津新技术产业园区华苑产业区鑫 

茂科技园综合楼 5 层 511 室 ”(Room 511, Floor 5, Comprehensive Building, 

Xinmao Sci-Tech Park, Huayuan Industrial Zone, Tianjin New Technology Industrial 

Park). Here, “ 新技术产业园区 ”(New Technology Industrial Park), “ 产业区 ” 

(Industrial Zone), and “ 科技园 ”(Sci-Tech Park) are such types of names. Both (ii) 

and (iii) will get translations from related KBs and Pinyin for given names. 

(iv) The identification and translation of streets and doorplates. This is one easy step. 

Sometimes they are skipped to the next step directly if there is exact local region name 

in the front, e.g. “ 北京市海淀区五道口东升园华清嘉园 7 楼 1804 号 ”(No. 1804, 

Floor 7, Huaqing Quarters, Dongshengyuan, Wudaokou, Haidian District, Beijing). 

In this example, two small region names, “Wudaokou” and “Dongshengyuan” have 

given the detail address. 

(v) The matching and translation of the building name and apartment number. An instance 

has been introduced in (iv): “ 华清嘉园 7 楼 1804 号 ”(No. 1804, Floor 7, Huaqing 

Quarters). Sometimes this part appears rather complex: “ 广州市天河区五山路五 

山科技广场 A 座第六层自编 619 号 ”(No. 619, Self-arranging, Floor 6, Tower A, 

Wushan Science & Technology Plaza, Wushan Road, Tianhe District, Guangzhou). 

For this instance, “ 五山科技广场 A 座 ”(Tower A, Wushan Science & Technology 

Plaza) is the building name and number; “ 第六层自编 619 号 ”(No. 619, Selfarranging, 

Floor 6) is the exact location in the building. 

There are two difficulties in the pattern matching of addresses: one is that an address 

contains a CN name, e.g. “ 天津市北辰区津围公路西汽车检测站内 ”(In Automobile 

Testing Station, West of Jinwei Highway, Beichen District, Tianjin). To identify the 

CN name “ 汽车检测站 ”(Automobile Testing Station) in this example, the key word 

“Testing Station”is found and then the CN translation system should be called as a subroutine. 

The other is that an address is in abnormal order i.e. larger region name follows 

small region name, e.g. “ 高桥镇大同北路 ”(North Datong Rd., Gaoqiao Town). Here 

“ 大同 ”(Datong) is a city but “ 高桥镇 ”is a small town. When a city name appears in 

such a case, it must be followed by a street mark, i.e. “ 街 ”, “ 路 ”etc. An order-control 

list works with UnitMark in rules to guarantee the correct processing of abnormal orders.


237 

5. Tests for the translation system 

5.1. Evaluation standard 

For the large-scale application of MT system, fixed-size test set of BLEU-style evaluation 

criterion is not proper for its limited coverage. In our case, it is necessary to test the 

coverage of translation KBs and the robustness of the rule-based approach with stochastic 

sampling from large amount of the system output for human evaluation . A popular MT 

evaluation criterion for human evaluation on the system output is adopted in our task. This 

criterion is designed and adopted by the State High-Tech Development Plan of China (863 

Program) 12 . It provided an adequacy score and a fluency score for each translation according 

to the scoring criterion, ranging from 0 to 5. From its definition on the scoring criterion, a 

new criterion to our evaluation task is defined and a centesimal grade is mapped for manual 

scoring. As the orders are fixed for CN and address translations, the criterion of the fluency 

is omitted. We focused on whether information is correctly translated, i.e. accuracy. Table 

1 gives the descriptions on the criterions of 5-score and 100-score. Note: the “error” in Table 

1 means that there are obvious incorrect translations caused by segmentation error or 

translation error but the machine-style translations are tolerant. 

5-Score 

Descriptions on 

Table 1. Descriptions on MT evaluation criterions. 

Adequacy and fluency 

100-Score 

5 All information, flawless 100 No error 

4 Most information, good 80 One error 

3 More information, non-native 60 Two errors 

2 Little information, disfluent 40 – – 

1 Almost no information 20 Otherwise 

Difficult to comprehend 

0 No output or incomprehensible 0 – – 

Description on accuracy 

The human judgments were completed by the cooperation between 2-3 system developers 

and 2-5 bilingual engineers with English major master degrees. The final evaluation 

scores for the system output came from their average scores. 

5.2. Test results 

After a series of experiments had been done on the CN and address translation system in 

the early development phase of the system, the translation KBs are expanding and the translation 

performance is being improved gradually. When the translation KBs have reached a 

certain size, the strategy of active learning was applied to extract a large amount of translation 

error candidates from millions of the system output. Such scale tests were executed 

several times during the late phase. The error candidates were checked and confirmed; for 

real errors, the corresponding translation knowledge would be added into KBs manually.


How to find translation error candidates automatically Two types of error candidates 

are defined. 

(i) a Chinese character or string without any translation or processing mark; 

(ii) 2 or more sequential segmented characters at some positions where should be occupied 

by a multi-character word in general. 

Any element satisfying the above criteria is denoted with Cx, and it is found by the 

debugging program. As the translation system ran on millions of records in tests, the number 

of error candidates was too large. A control threshold which based on Cx frequency should 

be used to limit the number of candidate output. The control threshold is represented in 

Form 2. Only the output that containing Cx with the frequency larger than T value (default 

= 2) is extracted as an error candidate. 

Freq(C x ) > T (2) 

The candidate number about 10,000 ∼ 30,000 for human verifying was controlled in 

each iteration step. During one and half year, it had been done several iterations of Test 

→ KB Enlargement → System Improvement. The output errors in the last iteration were 

erased in the next circle through the process of stepwise refinement. Meanwhile, we checked 

new output on the same records which are correct in the last iteration to avoid rule conflict 

between two iterations. Table 2 gives the test results of the translation system. During such 

iterations, more than 5,000 terms had been added to the main KB–term translation KB for 

CN translation. For the main KBs of address translation, translation rule set and local region 

KB, the entry numbers increased from about 300 to 1,050 and from less than 200 to 1,600 

respectively. 

Table 2. Test results. 

Test time Size of sampling records Translation accuracy 

Jan. 2009 CN #: 35,000 CN: 93% 

Addr. #: 34,500 Addr.: 87% 

Oct. 2009 CN #: 30,000 CN: 94% 

Addr. #: 30,000 Addr.: 89% 

Jun. 2010 CN #: 10,000 CN: 97% 

Addr. #: 3,000 Addr.: 92% 

In our tests, there are two types for most errors: for CN records, Part 3 often output 

null as missing translations in term translation KB; for address records, there are many 

segmentation errors in Part 2 and 3 because of difficulties mentioned in Section 4.2 which 

made the accuracy of address translation lower than the one of CN translation.


239 

6. Conclusion 

There are different challenges for C-E translation for CN names and addresses in a largescale 

database compared to general NE translation: correctly analyzing complex structures 

of these specific NEs, and accurately translating the large amount of special phrases including 

terms, local region names, etc. In this paper we put forward a rule-based approach with 

novel applications of active learning and stepwise refinement, and carry out co-operation 

between the system developers and language engineers, all which have achieved promising 

results. 

The lesson we has learned from this translation engineering is that the rule-based approach 

plays a very important role for some MT tasks, especially for those cases such as: 

without enough bilingual resources and with high requirements on the accuracy of the system 

output. The rule-based approach at least has two advantages here: exact translations 

through translation rules provided by human; easy and flexible control mechanism for the 

output quality. 

For application, the CN and address C-E translation system still needs further improvement. 

Continued checks to the system output and extensions to translation KBs will be 

scheduled in the near future. 

Acknowledgments 

The authors give sincere thanks to the master students from Harbin Institute of Technology 

who worked on the development of the NE and address translation system since 

the autumn of 2007. They are Song Wang, Kaiming Lin, Qingxuan Chen, Wenyan Miao, 

Xianggen Zhou and Shasha Liu. Their continuous efforts contributed to the success of the 

system. The fundamental research related to this work is supported partly by the National 

Natural Science Foundation of China (Grant No. 60736014 and 60773069) and the State 

High-Tech Development Plan of China (Grant No. 2006AA010108). 

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Tiejun Zhao (Member) 

He received the Ph.D. degree in 1997 from Dept. of Computer 

Science and Engineering, Harbin Institute of Technology (HIT), 

China. He worked at School of Computer Science and Technology, 

HIT since 1990 as a lecturer, an associate professor, and a 

professor. His research interests include Natural Language Processing, 

Applied Artificial Intelligence. He is the Council member 

of Chinese Information Processing Scociety China (CIPSC), 

the vice director of Machine Translation Subject Committee of 

CIPSC, the member of editorial board of Journal of Chinese Information 

Processing (JCIP). 

Conghui Zhu (Member) 

He received the Ph.D. degree in 2009 from Dept. of Computer 

Science and Engineering, Harbin Institute of Technology (HIT), 

China. He worked at School of Computer Science and Technology, 

HIT since 2009 as a lecturer. His research interests include 

Natural Language Processing and Machine Learning. 

Muyun Yang (Member) 

He received the Ph.D. degree in computer science and engineering 

from Harbin Institute of Technology in 2003. He is an 

associate professor in the School of Computer Science and Technology, 

Harbin Institute of Technology. He is a member of IEEE, 

CCF (Chinese Computer Federation) and CIPSC (Chinese Information 

Processing Society China). His research interests include 

machine translation, information retrieval and e-learning.


241 

Yingjian Gu (Member) 

As a research fellow and the director of the National Administration 

for Code Allocation to Organizations (NACAO), he is responsible 

for establishing and implementing the network system 

of the National Organization Code (NOC) with almost 3000 local 

branches in China. He devotes himself as the group leader of 

the project of the Information Database of Legal Entities (IDLE), 

contributing to the development of national credit system. He has 

successfully promoted the NOC’s application among more than 

20 governmental agencies. Books he wrote and compiled include 

the Practical Course of IC Card of the NOC Certificate, the System 

Engineering of NOC, the Guidelines for the Preparation of 

the Quality Management Documents in NOC Management Departments 

etc. 

Gang Zhou (Member) 

He achieved the graduate degree from the Social Research Institute 

of China. He is a senior engineer of NACAO, works as the 

director of the Department of Application and Strategic Development 

of NACAO. He involves mainly in the promotion and 

strategic research in fields of E-government, E-commerce and 

international cooperation, and he is also in charge of promoting 

the IDLE project. 

Youyou Hong (Member) 

She received graduate degree in project management in 2008 

from the Beihang University; and she received bachelor degree 

in English Literature in 2002 from the Sichuan University. 

She works as an engineer in the Department of Application and 

Strategic Development in NACAO, being responsible for international 

cooperation, involving in fields such as NOC’s application 

and promotion, C-E translation project, IDLE etc.

Chinese-English Translation of Company Names and Addresses in a

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