 From Text to Exhibitions: A New Approach for E-Learning on Language and 
Literature based on Text Mining 
Qiaozhu Mei 
Department of Electrical Engineering and 
Computer Science 
Vanderbilt University  
Box 1679 Station B  
Nashville, TN 37235 USA 
qiaozhu.mei@vanderbilt.edu 
Junfeng Hu 
Department of Computer Science  
Institute of Computational Linguistics 
Peking University 
100871, Beijing, China 
hujf@pku.edu.cn 
 
Abstract 
Unlike many well established approaches for 
E-Learning on science fields, there isn’t a 
commonly accepted approach of E-Learning 
on humanities fields, especially language and 
literature. Because the knowledge on language 
and literature depends too much on texts, 
advanced text processing has become a 
bottleneck for E-Learning on these domains. 
In traditional learning frameworks learners 
would easily get boring with mass pure texts. 
This article introduces a new approach for E-
Learning on language and literature, by 
intelligently extracting real or virtual objects 
from texts and integrating them as exhibitions 
in a digital museum system. This article also 
discussed how to generate exhibitions from 
texts with computational linguistics methods 
as well as how this E-Learning framework 
pushes the research of computational 
linguistics. The discussion of E-Learning by 
Digital Museum is based on the design of 
Digital Museum of Chinese Ancient Poetry, 
by Peking University. 
1 Introduction 
Computer based Education has become a very 
hot and productive topic in recent years. However, 
most of the existing methodology and models are 
based on science domain. This is because the 
teaching and learning on science domain relies 
much on the ability of reasoning and computation, 
which directly utilizes the advantage of computer. 
The most important carriers of Knowledge on 
humanities domain, especially literature and 
language are textual materials. Therefore, unlike E-
Learning on science and technical fields, a more 
intelligent way of using computer to deal with texts 
is required. Traditional E-Learning models on 
language and literature rely too much on pure text. 
Relevant frameworks include Digital-Archives, 
Digital-Libraries and Digital publications. Most of 
them are just “gathering mass text materials and 
providing them online”, thus the interface between 
system and learners is onefold, non-interactive and 
lack of guidance. Learners easily get missed in 
excessive bald texts without a “docent [2]” to 
advise them how to select a well organized 
knowledge structure and a learning pathway. 
Searching and retrieving modules are provided in 
those models to various extents, which provide a 
knowledge retriever. However, it still cannot go 
beyond texts. 
Recently, Digital Museum systems are believed 
to be able to provide a vivid interface which carries 
educational uses to participants. Teaching and 
learning becomes much easier from the special 
circumstance of learning in the presence of real 
objects, which inspires curiosity and creative 
thinking, and gives museums the potential to 
develop distinctive and meaningful educational 
experiences [5].  
There are many good examples that approach E-
learning on humanities fields with a system similar 
to a Digital Museum. The National Palace Museum 
system in Taiwan offers 14 courses on the cultural 
relics of China [3]. Digital Museums on more than 
10 major fields in nature and culture have been 
designed along with Taiwan’s nation wide Digital 
Museum plan. Lo, Feng-ju et’ al have designed a 
digital museum of Chinese Ancient Literature, 
which provides some sub-exhibitions of poetry and 
fictions in formats of photocopy of the actual paper 
edition of ancient texts.[7] These works have been 
well exploring the primitive application of Digital 
Museum in E-Learning on Humanities Fields. 
To satisfy the needs of E-Learning on Language 
and Literature fields, a modern digital museum 
should have some specific features. It should 
provide a mechanism to process texts, which 
would be able to integrate some computational 
linguistics methods. It should also provide a way to 
organize knowledge beyond the texts, and be able 
to provide guidance for learning. This can be 
achieved by generating objects out from texts and 
organizing them into interactive exhibitions that 
can be personalized. Moreover, the digital museum 
framework should be reusable to different scope of 
background knowledge. Such a modern digital 
museum associating text processing mechanism is 
believed to be a sound approach of E-Learning on 
Language and Literature.  
This article discussed this approach on the 
Digital Museum framework design, how it is 
associated with Computational Linguistics, and 
how to integrate knowledge to maximize the E-
Learning efficiency. These discussions will be 
based on an example of the Digital Museum of 
Chinese Ancient Poetry Art, by Peking University 
2003. [10] The following section will discuss the 
general framework design of digital museum. We 
will discuss text processing work behind the 
Digital Museum in Section 3, and Knowledge 
Processing and integration in Section 4. Some 
more discussion and future work will be provided 
at the conclusion section.  
2 The Digital Museum Framework 
Instead of digital library and traditional digital 
museum systems, which provide single function of 
exhibition, a modern digital museum provides 
multidimensional functions. Generally, a modern 
digital museum has three key functions, exhibition, 
education and research. In our design of Digital 
Museum for Language and Literature, the three 
dimansion would be: interacting theme based 
exhibitions from texts, E-Learning modules on 
language and literature, and related research on 
Computational Linguistics. 
2.1 Digital Museum and E-Learning on 
Language and Literature 
Digital Museum systems have gone beyond 
exhibitions of digital collections. Instead, they 
would increasingly emphasize educational uses 
rather than traditional exhibitions. It provides users 
with educational and well-motivated exhibitions 
[13]. UK-wide Digital Museum linked exhibitions 
connected by subject and theme with an integrated 
learning environment [6]. By 2000, the National 
Science Plan of Digital Museums of Taiwan has 
defined a specific and integrated program on how 
to utilize scientific technology, especially 
information technology, and how to digitalize the 
archives in both cultural and natural fields, with 
significant humanistic meaning. It has conducted 
further discussions on how to apply these kinds of 
digital projects and productions to education, 
research and industrialization, for the sake of 
conserving culture, promoting education, inspiring 
research and increment of industrialization. [3]. 
Knowledge on a learning topic should be 
organized  as an exhibition theme, which is 
represented by a series of real or virtual objects 
and detailed descriptions. Exhibitions of various 
themes are linked together corresponding to the 
relativity of their themes. Learners can participate 
in the Digital Museum by choosing a pathway of 
linked exhibitions with a typical topic. Special 
modules will also be provided for participants to 
interacting with the system, which will be 
discussed in section 4.  
2.2 General Architecture Design of a Digital 
Museum 
The life cycle of a modern digital museum looks 
like a fountain model  [11]. There are feedbacks 
from each design phase to previous phases. There 
are several milestones in the life cycle, each of 
which acts as a knowledge container and a 
foundation of knowledge processing on upper 
levels. [14]. These knowledge containers are as 
follows: 
Milestones Functionality 
 
Information Origin Pool: 
(Primitive Corpus) 
The mass storage of large-scale 
information from preliminary 
digitalization work. 
 
Refined Knowledge Bases 
(Refined Corpus) 
Database storage of useful and 
relevant knowledge from 
knowledge refining.  
 
Metadata for Exhibitions 
Metadata describing ontology, 
with all detailed metadata for 
knowledge flows, items and 
relations 
 
Integrated Exhibition Base 
 
Database for Exhibiting items, 
individual or integrated, for regular 
accessing by system.  
 
Reusable Tool Base for 
Functional Modules 
Tool pool for reusable module 
functions, individual or integrated 
components for various use. 
 
Multi-functional  Interface 
Web-based interface for 
exhibitions, education and 
research.  
Table 1: Milestones within the Digital Museum 
Architecture 
 
Based on these milestones, the general 
architecture of a Digital Museum on Language and 
Literature can be represented in the following 
figure:
 
 
Figure1: General Architecture of a Digital 
Museum based on language processing 
2.3 Example: Introduction to the Digital 
Museum of Chinese Ancient Poetry 
The Digital Museum of Chinese Ancient Poetry 
Art [10] is a research model by Peking University, 
Beijing, combining E-Learning, computer assisted 
research on Chinese Ancient Poetry and 
computational linguistics. A prototype of this 
Digital Museum was designed in order to meet the 
needs of exhibition, education and research on the 
art of Chinese Ancient Poetry. The analysis, design 
and implementation of this project were on a 
highly abstract level. 
2.3.1 Corpus, Design and Prototype System 
The information origin pool and the refined 
knowledge base of this project were also the 
corpus for related computational linguistics 
research. It involves Chinese Ancient Poetry across 
2,000 years, approximately 100,000 items [10]. 
Other advanced knowledge bases such as Author 
Information base, Image and media base, Location 
information base and Word lists were constructed.  
In the design of this Digital Museum system, 
knowledge mining was divided into two types, 
item entity information mining and relational 
information mining. Item entity information was 
detailed to exhibiting items, characters, images, 
media, locations and words. Relational information 
reflected all aspects of relations among items. 
Metadata for each category of instances was 
defined in the design phase. Particularly, a group 
of items with relating meaning was structured as a 
virtual item class, which was also treated as a 
specific item.  
In the prototype system, items of poetry, 
character, location and others were exhibited along 
with all related formats of knowledge. Users can 
leap from one item to its related items, and learn 
them in the context where they originally belongs. 
Sample exhibitions on specific themes, such as 
clothing, plants, food and spring were also 
designed. 
2.3.2 E-Learning and Related research from 
this Digital Museum 
In the dimension of learning, Digital Museum of 
Chinese Ancient Poetry explored the study of E-
Leaning system for the language and literature 
features of Chinese Ancient Poetry. It enabled a 
way to learn a poem in its background environment, 
with reference to its related poetry and other 
related objects in multiple formats. The system 
also presented statistical research results of the 
corpus to users, such as the words usages of 
authors, the cooccurrence of words, the likelihood 
of the hidden meanings of words, which help users 
to be well-informed and easier to understand in 
learning a poem or a word.  
In the dimension of research, the digital museum 
is closely related to specific research topics on 
computational linguistics, especially statistical  
natural language processing. We refined unknown 
words from the corpus though statistic methods 
and explored to cluster them into concepts. In this 
way, we studied the hidden meanings of words and 
poetry in context and studied the relation discovery 
among poems. We also conducted some research 
of knowledge mining and discovering from corpus, 
which can also inspire extended researches like 
Computer Assisted archaeology on Chinese 
Ancient Poetry. 
3 Language Processing behind the Digital 
Museum Framework 
Knowledge of humanities areas, especially 
language and literature, is commonly carried by 
texts. Therefore, the language processing, 
specifically the text processing will be vital for 
transforming pure texts and domain knowledge 
into abstracted exhibitions. Actually, most digital 
museums today haven't made good use of 
computational linguistics techniques. Most of them 
remain on organizing exhibitions manually and 
providing them online. Those exhibitions are 
relatively isolated from each other.  
However, there are remarkable relations among 
text units and real objects and topics, which are 
hidden in the texts. For example, the word 
“willow” seems having nothing to do with “getting 
apart” by the semantic definitions, but in the 
context, “breaking a willow branch” does indicate 
“send-off friends”, or “seeing a friend leaving” in 
Chinese Ancient Poetry.  
These meaningful entities and relations can be 
learned from the statistical analysis of large scale 
poetry texts. The use of computational linguistics 
methods here is crucial, which distinguishes it with 
traditional Digital Museum models. Statistical 
natural language processing over large scale corpus 
is the most significant approach we have adopted 
in this research.  
3.1 Construction of Corpuses and Integrated 
Knowledge bases 
The first phase of language processing is to build 
corpora and knowledge bases. Primitive corpora 
are constructed by archive digitalization. Refined 
corpora are constructed by applying language 
processors on the primitive corpus. We can use 
Digital Museum of Chinese Ancient Poetry for 
example.  
For the Digital Museum of Chinese Ancient 
Poetry Art,  the primitive corpora include texts of 
poems over 1, 200, 000 lines, descriptions of 4000 
authors, a name dictionary and a location 
dictionary. The refined corpora include a words 
dictionary which is thoroughly discovered from the 
texts, a concept base constructed by supervised 
word clustering and a storage of words 
cooccurances. Other knowledge bases include 
images, music, medias(reading), relics, events, and 
a series of expertise knowledge on Chinese 
Ancoent Poetry.  
The general ontology of domain knowledge was 
carefully studied. Important entities and relations 
from texts and related domains were determined. 
Consequently, we carefully designed the metadata 
and chose a database system to maintain the 
knowledge base. This knowledge base should be 
expandable so that  it can contain texts, entities 
from related domains, and relations.  
The last step of this phase is to design an 
referencing mechanism to query and get the 
answer. The outcome of this phase is an integrated 
knowledge base, the textual part of which is the 
corpus for mining and knowledge discovery. 
3.2 Text Mining: Extracting Objects from 
Texts 
As soon as the corpora and knowledge bases are 
constructed, higher level methods of natural 
language processing are applied to mine in the 
corpus. The goal is to find objects abstracted from 
texts, which are organized by individual topics. 
Statistical natural language processing plays a very 
important role in this procedure, which can be 
described in the following three levels.  
3.2.1 Extracting Direct Relevant Objects from 
Texts. 
Textual knowledge is not “dead” in the fields of 
language and literature. It is interacting with 
knowledge in other forms, by other carrier or on 
other abstract level.  Taking Chinese ancient poetry 
for example, a poem is associated to its author, its 
era and its writing background. The textual body of 
a poem also refers to certain persons, events, 
locations, plants, scenes, feelings and other entities, 
either real or virtual. In addition, there are various 
sources of objects relevant to the poem, such as 
paintings, calligraphy works, music and cultural 
relics, etc. All these entities above are so important 
to the synopsis of the poem that it is an advisable 
way to learn the poem with the appearance of these 
objects. Furthermore, relying on these directly 
relevant objects makes teaching and learning much 
more open and exciting than barely focusing on 
texts.  
In the early phase of Digital Museum design, an 
integrated exhibition base is built, in which directly 
relevant entities of the texts are refined, stored in 
relational or XML databases and associated with 
the body of texts. 
3.2.2 Discovering Hidden Entities and Relations 
Associated with Language Units. 
As the Computer assisted research develops on 
these fields, we can work on the hidden knowledge 
of texts by means of text mining and retrieval.  As 
language technology evolves, a computational age 
of language has arrived [1].  We can conduct 
computer assisted analytical research on language, 
with both linguistic and statistical approaches. In 
the research on the language of Chinese ancient 
poetry, we studied the statistical concurrences and 
meaningful units in the texts, extracted words from 
collocations and clustered words into meaningful 
concepts. In further research, we explored ways to 
study the hidden meanings of the words and 
collocations, especially those related to emotions 
of human. Consequently, expected to learn 
emotional characteristic of a poem, associating 
words, concepts and other units it refers with the 
similar characteristic.  
On the other hand, language and texts are the 
most important carriers of cultural fragments. 
Many interesting knowledge patterns are hidden in 
the texts.  There is a considerable proportion of 
Chinese ancient history and culture buried in the 
texts of Chinese ancient poetry, which evolutes 
along more than 2,000 years and involves locations 
all over China. By language techniques, fragments 
of culture can be mined from the texts, refined and 
stored, and finally integrated into interacting 
virtual scenes.   
By this we can discover hidden entities and 
relations associated with text and expand it to 
analytical meaningful segments.  
3.2.3 Expanding Indirect Relations. 
In our framework, knowledge entities are not 
living alone but interacting. Both textual entities 
and other objects are associated to its relevant 
entity set. There are two kinds of relations 
identifying that two entities are interacting, direct 
relation, which have already been discussed above, 
and indirect relation.  For instance, a poem refers 
to various knowledge objects, thus poems referring 
to the same objects are indirectly interacting with 
each other. These poems are involved in their 
relevant entity set, with “identical reference” as an 
indirect relation.  In a more intelligent level, poems 
with the similar hidden meanings or relevant 
emotions are arranged together as a set. This set 
can be associated with a topic, a subject, a scene or 
a specific semantic cluster.  
In these three approaches to expand textual 
knowledge into relevant objects, a former purely 
textual entity has been developed as involving in 
the surrounding of various relevant objects, real or 
virtual. Thus we complete the procedure of 
extracting objects for exhibitions from texts. An 
example from poems to objects is as follows: 
 Figure2: Expanding Objects Set from a Poem 
Text. 
 
3.3 Theme Driven Knowledge Discovery 
From the statistical analysis on character 
concurrences, we applied various methods to 
discover unknown words from the texts. Chinese 
language is different from other language because 
there isn’t natural interval from a word to another. 
We consider all words to be unknown in the 
beginning and generate a word dictionary from the 
filtering by mutual information value, µ-test and 
other statistical methods.  
Upon the word dictionary, we conducted words 
clustering by the distance of words concurrence 
vectors. This procedure has abstracted concepts 
from words. After supervised filtering, these 
concepts will indicate some hidden semantic 
meanings.  
The consecutive knowledge discovery work will 
be theme driven. First, a theme, or a learning topic 
is decided, some features and key concepts of this 
theme will be decided with the expert knowledge. 
Using statistical methods, we can find the concepts 
and words which are semantically similar or in 
some way related to this theme. Then, directly and 
indirectly related objects (discussed in section 3.2) 
will be associated with the topic. Then, reluctant 
units are eliminated. We will filter the most 
significant entities and relations, which can be 
represented by combinations of both concepts and 
words, and organize them around the theme. In this 
way, we can put the topic/theme back to its ancient 
living environment.  
Further works includes rebuilding ancient 
scenarios where the topic belongs, and mining for 
relations among topics.  
4 Knowledge Processing and Integration of 
the Digital Museum  
Knowledge processing plays a very significant 
role in the Digital Museum framework. It is 
involved as a clue throughout the life cycle of the 
digital museum. The entire design and 
implementing of the digital museum is focusing on 
language processing, knowledge discovery and 
exhibition integrating. The knowledge processing 
procedures can be represented in the following 
figure: 
 Figure3: Knowledge Processing in this digital 
museum. 
 
4.1 Knowledge Processing Hierarchy  
An intelligent platform of knowledge deals with 
knowledge in five primary hierarchies, namely, 
knowledge citation, knowledge application, 
knowledge transmitting, knowledge learning and 
knowledge developing [8]. This division of 
knowledge hierarchies remarkably adapts the 
needs of an E-Learning program. In the study of 
this article, we make a little modification to this 
division and applied it to the Digital Museum 
system as follows:  
Knowledge Citation 
Knowledge Applying 
Knowledge Learning 
Learning and Teaching  
Knowledge Mining 
Knowledge Representing 
Knowledge Representing to Users  
Information Interacting 
Knowledge Developing 
Table 1: A knowledge processing hierachy in the 
Digital Museum 
 
Poem 
Persons Locations 
Relics 
Events 
Other 
Words Concepts 
Emotions 
Cultural 
 
Fragments 
Scenes 
Relevant Entity Sets… 
Poems, Topics, Scenes, 
Texts, Concepts, Themes, 
Words, Other entities… 
Texts 
Images Medias 
Virtual 
Realities 
 
Actually, this division is somewhat relative and 
not absolute. For instance, in some activities 
defined as knowledge representation and 
knowledge developing, we may also need to do 
knowledge citation and applying. However, this 
division of knowledge hierarchy would help to 
define the functions of Knowledge Platform and 
content the needs for knowledge by systems and 
users. [8] 
The Digital Museum presents multidimensions 
according to the three functions of exhibition, 
education and research. The processing targets, 
procedures and emphases on Knowledge vary 
among dimensions.  
In the dimension of exhibition, system focuses 
on Knowledge citation and Knowledge 
representing in the hierarchy above.  
In the dimension of e-learning, system focuses 
on the hierarchy of Knowledge applying, learning 
and teaching, Knowledge Representing and 
information interaction.  
In the dimension of computational linguistics 
research, system emphasizes the hierarchy of 
Knowledge Mining and Knowledge developing.  
4.2 Two Types of Integration for Knowledge 
Objects 
After discussing the generating of objects from 
the texts, we would be interested in how to 
integrate them for E-Learning.  
Relating and interacting objects are extracted 
from texts and stored in the exhibition base. The 
next phase is to arrange exhibitions by selecting, 
dividing and integrating these objects, and 
construct the digital museum interface.  
There are two key forms of objects integration, 
tutored and theme-oriented exhibitions and virtual 
scenarios.  
In the first form, tutored theme-oriented 
exhibition, objects relevant to a specific subject or 
theme are integrated and represented in multi-
modals. This interface design provides a dynamic 
exhibition module by grouping texts and their 
relevant objects in various formats together, 
providing docent knowledge for this topic and 
links to relevant topic exhibitions. Learners 
participate in one exhibition and go through links 
fitting to their needs or under instructions, thus 
personalized learning paths are formed.   
There are two tips in tutored theme-oriented 
exhibitions. One is “multi-modal”. Personalized 
exhibitions in our framework enable learning 
through multi channels, in forms of texts, image, 
music and virtual reality, etc. Also taking Chinese 
ancient poetry for example, we first discover the 
relevant scenes and hidden emotions of a poem, 
select objects referring to similar scenes and 
emotions, provide them as background materials 
and then integrate them with the poem.  A more 
detailed instance is the Auto-matching poems and 
paintings. The other is “interactive”. In our 
framework, a learner can add his remarks or 
discuss in every exhibition topic. These remarks 
are processed and stored as new relevant objects to 
this topic. Users can also provide materials or 
background information to an object or a topic, and 
can provide their own exhibition plans of new 
organizations of objects. The system studies the 
feedbacks and provides users with personalized 
participation paths.  
The second integration form is scenarios. 
Knowledge objects were recorded in texts from 
their original living environments. By collecting 
and extracting relevant objects from texts and 
analytical researching on their relevant 
environmental elements such as emotions, we are 
able to put a textual object back to a scene 
representing its original living environment by 
rebuilding these origin scenes. Teaching and 
learning are made easier and more exciting with 
participating in the original scenes that a topic 
really lived. With the technology of multimedia 
and virtual reality, we are able to integrate objects 
and environmental elements surrounding a specific 
topic and rebuild a virtual scene, which is 
represented in our framework as multimedia 
demonstration, tests and games.  
These two key integrating patterns organize 
various formats of objects and represent these 
integrated exhibitions to users in an interactive and 
personalized way. It maximizes the educational use 
of a digital museum on language and literature 
fields.  
 Figure3:Integrating exhibits in the Digital 
Museum on Chinese Ancient Poetry. 
5 Conclusion 
Computer-based education on language and 
literature has both its advantage and difficulty. On 
one hand it provides learners with abundant 
relating materials, on the other hand it’s tedious 
and difficult for learners to acquire knowledge in 
the sea of information. The approach of extracting 
objects from texts, and integrating them to build an 
interactive and vivid exhibitions enables learners 
both to explore in broad scope of knowledge and to 
enjoy exciting and comprehensible learning. 
Computer techniques are adopted in the framework 
of Digital Museums to maximize its educational 
use. How to make use of the methods from 
computational linguistics, especially statistical 
methods is the bottleneck or the key to success of 
this e-learning approach. On the other hand, the 
needs of e-learning and the abstracting of digital 
exhibitions from texts have very positive effect on 
pushing the research of computational linguistics. 
Significant techniques include unknown word 
discovery, clustering and other issues in text 
mining. Besides the conituous work on text mining, 
future research will focus on how to personalize 
the learning paths of learners, and enable in-time 
processing of user feedbacks. Investigations and 
evaluations will be made both on the e-learning 
system and the efficiency of text mining 
techniques over typical kinds of texts, like Chinese 
ancient poetry. 
6 Acknowledgements 
The authors would thank people in Institute of 
Computational Linguistics, Peking University, who 
gave great help for this research. We will 
especially thanks Miz. Feng-ju Lo, who has given 
us great help ever since the research starts. 

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