Finding of People in Economic Field based on Personal Profile
Lei Chun
1
, Yang Fengfan
2
and Lou Liang
2
1
School of Economics and Management, Southwest Jiaotong University, Chengdu, China
2
College of Electronics and Information Engineering, Sichuan University, Chengdu, China
Keywords: People in Economic Field, Field Trie Tree, Chinese Text Classification, Bayesian Classifier.
Abstract: There are a lot of personal profile information on the Internet. If we can automatically collect these
information on the Internet, classify them and extracting personnel information from them, it can provide
some help for the finding of people in specific field. In the research, we focus on study of Finding of People
in Economic Field based on Personal Profile from the Internet. We proposed a field dictionary building
method base on the paper database, and make an improvement to the text segmentation technology used in
people classification. We designed a hybrid segmentation method based on Field Trie Tree and HMM-
Viterbi model. On the basis of Bayesian classifier, we build a model to find people in Economic field based
on Personal Profile. Experiments shows that the method has a high rate of the recognition of people in
economic field.
1 INTRODUCTION
Field people recognition means judging the people’s
working field by the tabs of people or description
text of the people and select these people working in
specific areas. For the information of people on the
Internet are various, it is not easy to find the people
working in economic field. It not only require proper
data source, but also need to make comprehensive
analysis of these information and determine the
likelihood of people belonging to the target fields.
The current method used for field people recognition
is mainly based on text classification. Text
classification is the process to analyze the text and
then put the text into the pre-defined categories
according to the results of the analysis (K. Aas, L.
Eikvial, 1990). Text categorization research
originated in the 1950s, H. P. Luhn is the first
researchers in the field, he realized the classification
of the text by word frequency statistics method,
followed by the emergence of automatic text
categorization and classification method based on
knowledge engineering (Fabrizio Sebastiani, 2002).
After the 1990s, with the development of knowledge
of statistics and machine learning, classification
based on knowledge engineering had gradually been
replaced by some new classification algorithm based
on machine learning which mainly including
Rocchio algorithm, Bayesian classifier algorithm, K-
nearest neighbor algorithm, support vector machine
algorithm and etc. (N. Fuhr, S. Hartmanna, G.
Lustig, M. Schwantner, K. Tzeras, 1991). Chinese
text classification started in the 1980s, mainly based
on the study and improvement of English text
classification, applying the English text
classification algorithm to Chinese text and forming
the Chinese text classification. The acquisition and
pre-processing of personal profile information on the
Internet can be completed by the crawlers. The
research mainly focused on the method of field
classification of collected personal profile
information.
2 IMPROVEMENT OF CHINESE
WORD SEGMENTATION
TECHNOLOGY
Workflow of text classification based on machine
learning has three main processes: text
preprocessing, text categorization classifier training
and text classification. Text preprocessing process is
to make text segmentation, remove stop words,
extract feature words and etc. (Feng Guo-he, 2007).
The training process is to input a number of artificial
accurate classified documents into the computer,
allowing the computer to analyze each category
436
436
Fengfan Y., Chun L. and Liang L.
Finding of People in Economic Field based on Personal Profile.
DOI: 10.5220/0006028004360439
In Proceedings of the Information Science and Management Engineering III (ISME 2015), pages 436-439
ISBN: 978-989-758-163-2
Copyright
c
 2015 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

document and extract some characteristics and rules
which can effectively distinguish among each
category. The features and rules are used by the
classifier. The classification process is to classify the
input text, the classification is determined by the
trained classifier (Yang Wen-chuan, 2013) Fig. 1 is
a complete text classification process.
Figure 1: The basic process flow of text classification.
Text segmentation is the basis of Chinese text
classification process, which directly impacts on the
choosing of the characteristics, and thus affects the
results of classification. There are some professional
terms in the personnel profile, which are essential
characteristics to field recognition. According to
these characteristics, we propose a hybrid
segmentation method based on field Trie tree
dictionary and HMM models, which can improve
the recognition rate of professional terms in the text.
3 FIELD DICTIONARY
BUILDING METHOD BASE ON
THE PAPER LIBRARY
WA NFA NG
To construct specialized field dictionary, we
must first get the specific field professional terms.
This paper presents a growing method based on
word seeds. The method is mainly depend on the
keywords searching ability provided by the paper
database WanFang (http://www.wanfangdata.com.
cn). First, build some professional term lists as seed
lists manually. It is much better to choose these
professional terms which can distinguish themselves
from other field professional terms most. In the
research, we focus on the economic field, so we
choose "Macroeconomic science", "audit",
"finance", "insurance" and other words can be
identified in the field. Then, we use the crawler to
crawl the papers with the keywords in seed list and
extract the keyword in the paper which has same
keywords in the seed list. The extracted keywords
can be used as new keywords in the targeted field.
With the process goes over and over, we can get
more and more specific field professional terms
dictionary. The whole acquisition process shown in
Fig. 2.
Figure 2: Automatic field dictionary building method.
4 A HYBRID SEGMENTATION
METHOD BASED ON FIELD
TRIE TREE AND HMM-
VITERBI MODEL
Trie tree, also known as dictionary tree, prefix tree,
is a tree structure. The statistics, sorting and
searching operation of strings stored with the Tire
structure is very fast (Shang Wen-qian, 2007). Its
advantages are: strings with the same prefix share
the same storage for prefix, so not only can reduce
storage costs with the same prefix of the string, but
can also locate the matching string of characters
directly from the individual, thus effectively
reducing the number of matches, shorten find time.
In this paper, we the find the professional terms in
the text with Trie tree.
HMM-Viterbi (Hidden Markov Model)
segmentation can improve the recognition rate of
professional terms, place names, organization names
and etc. But the figures introductory text is relatively
short, with small vocabulary, recognition rates
vocabulary, which can directly affects the classified
by field. The study designed a hybrid segmentation
method based on word dictionary and probability.
First, obtain the dictionary of the target areas and
build the Trie tree with the dictionary. Second, using
a forward maximum matching method to extract the
terms of specific field with Trie tree. Then, use
HMM-Veterbi model to handle the remained text for
the terms of specific field. The entire segmentation
process consists of pretreatment and segmentation
Finding of People in Economic Field based on Personal Profile
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Finding of People in Economic Field based on Personal Profile
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process. The process of hybrid segmentation method
is shown in Fig. 3.
Figure 3: The process of hybrid segmentation method.
5 ECONOMIC FIELD PEOPLE
FINDING BASED ON
BAYESIAN CLASSIFIER
After the actual test and compare, chi-square test
was selected as a text feature selection algorithm,
and the number of features was set to 2000. The
Bayesian classification has some advantages that the
necessary parameters is calculated with a small
amount of training data, it runs fast and it is easy to
implement. So, the Bayesian classification algorithm
is introduced.
5.1 Field Classification Standard
According to the regular research or working field
classification, treat the similar fields as a big
category. First, people’s professional field was
classified into "style art", "computer
communication", "natural science", "economics
area" and other big categories, and then classified
into sub-category like "political economy"
"management economics", "money and Banking",
"finance" and others, as shown in Fig.4. The
advantage of such a classification is that
classification with big category can put all the text
into proper category and have a better correct rate,
and with a sub-category it can make the
classification more accurate.
Field
Art … Economics … Science
...
managerial
economics
political
economy
currency
banking
finance
Figure 4: Field Classification.
5.2 Framework of Economic Field
People Recognization
Recognition framework of the economic field people
contains a total staff of four modules: data
acquisition sub-module (DAC), pretreatment sub-
module (PRE), field identification sub-module
(REC) and database operations sub-module (DBT).
The four modules collaborate with each other to
finish the process of the collection of information
and the field recognition. PRE and REC are called
by DAC, and DBT is calls by REC. Framework of
economic field people identification is shown in Fig.
5. DAC module mainly work as a data collector and
preprocessor, and read the configuration file of the
framework. PRE mainly reads the word and text
classification required training sample, as well as
dictionaries and other documents from the main
module, and training. It finally builds the HMM &
NBM model used for segmentation and
classification. The HMM & NBM model is called by
the REC. PRE module runs only one time at the start
time of the system. After generating the model, PRE
notice REC modules to take field reorganization.
REC handles the clean personal profiles received
from the master module and perform classification
recognition with the HMM & NBM model.
Figure 5: Framework of economic field people’s
identification.
6 EXPERIMENT AND ANALYSIS
6.1 Data Source
The experiment of reorganization of people in
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ISME 2015 - International Conference on Information System and Management Engineering
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economic field uses the data from Baidu
Encyclopedia and profiles of University Teachers'
Introduction web page. The training samples and
testing sample are those with good quality.
6.2 Training Nums
According to the classification criteria above, for
sub-category of major category we collected 100
Biography text as the initial classification of training
samples. Entire distribution shown in Fig 6.
Figure 6: The classification.
6.3 Method
To test the accuracy of the method, we use
confirmatory test method, which is to select a certain
amount of the economic field profile texts as a basis
for expansion, and randomly selected text not in
economic field. The selected profile texts formed the
experimental data.
6.4 Result and Analysis
The recognition of people in economic field
conducted three experiments with a different number
of economic field samples and a different total
number of samples. The experiments results
obtained are shown in Table 1.
The results showed that the reorganization
method works well with the experiment samples and
it is possible to recognize a large number of people
in the economic field with the personnel profile. The
recognition rate is about 80%.
7 CONCLUSIONS
The improvement of Chinese word segmentation
technology proposed in this paper can improve the
tech adapted in the field reorganization based on text
classification. The framework proposed in this paper
works well and has a good recognition rate. In the
future study, and we will do further improvement in
the recognition rate and the unstructured information
extraction from the profile and form the structured
information of the people.
REFERENCES
K. Aas, L. Eikvial. Text Categorization: a survey.
Technical Report, Norwegian Computing Center, June
1999.
Fabrizio Sebastiani. Machine learning in automated text
categorization [J]. ACM Computing Surveys, 2002,
34(1): 1-47.
N. Fuhr, S. Hartmanna, G. Lustig, M. Schwantner, K.
Tzeras. AIRX--a rule-based multistage indexing
systems for large subject fields[C], Proceedings of
RIAO’91 Conference, 1991:606-623.
Feng Guo-he. Automatic text classification technology
research [J]. Journal of Information, 2007(12): 108-
111.
Shang Wen-qian. Text classification and related
technology research [D]. Beijing Jiaotong University,
2007
Yang Wen-chuan. Research on Chinese Text
Segmentation based on double-arry Trie Tree [J].
Journal of Computer Engineering and Science. 2013,
35(9): 127-130.
Table 1: Result list.
Economic
samples
Total
samples
Recognized Correct
Accuracy
rate
Recall rate F1
Group 1 20 100 17 15 0.8823 0.7500 0.8107
Group 2 40 150 46 36 0.7826 0.9000 0.8372
Group 3 60 200 53 48 0.9056 0.8000 0.8495
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