Connecting Text Mining and Natural Language Processing in a Humanistic Context

  1. 1. Xin Xiang

    Graduate School of Library and Information Science (GSLIS) - University of Illinois, Urbana-Champaign

  2. 2. John Unsworth

    University of Illinois, Urbana-Champaign, Graduate School of Library and Information Science - University of Virginia

Work text
This plain text was ingested for the purpose of full-text search, not to preserve original formatting or readability. For the most complete copy, refer to the original conference program.

Recent integration of advanced information technology and humanistic research has seen many interesting
results that are brand new to traditional humanistic
research. In the NORA project this integration was largely
exemplified. In an effort to produce software for discovering,
visualizing and exploring significant patterns across
large collections of full-text humanities resources in
digital libraries and collections, NORA project features the powerful D2K data mining toolkit developed by NCSA
at University of Illinois, and the creative Tamarind
preprocessing package developed by University of Georgia.
In NORA project, D2K and Tamarind need to talk to each other, among other relevant components. The idea behind this connection is as follows:
- Make use of existing efforts and try to avoid
duplication. Natural language processing, such as part-of-speech tagging, word sense disambiguation, and bilingual dictionary creation, has long been
recognized as an important technique for text data mining (Amstrong, 1994). D2K has proved to be an effective and comprehensive data mining toolkit, and Tamarind prepares data gleaned from large-scale full text archives. Getting them work together is an easy and time-saving way of achieving the goal of NORA.
- Separate different tasks according to institution makes the multi-institutional project easier. D2K has been developed and used in several institutions
within University of Illinois, and Tamarind was
developed in University of Georgia for simplifying
primary text analysis tasks. This separation keeps each institution focusing on a relatively independent module that they have the most experience with.
- Prepare information about tokens once and for all. Natural language processing tasks prove time-
consuming and computation-intensive. Separating
these tasks from data mining part of this project
obviates D2K toolkit from performing basic data analysis every time it runs, thus streamlining the whole process.
The problem, however, is that D2K and Tamarind are developed using different programming languages and have different communication mechanisms. To put them together requires reconcilement and restructuring at both sides. This has eventually been achieved in a prototype application, where a collection of Emily Dickinson’s poems is classified as either “hot” (erotic) or “not hot” based on the language used (Kirschenbaum, 2006).
As the size of data increases, the problem of scalability emerges. The huge size of many humanistic collections will make unrealistic the solution of storing all the tables in the database. A perfect method to address this problem has not been found, and content presented here demonstrates
how we approach the text mining problem in the
prototype when the size of collections is not very large.
2 The D2K Toolkit
D2K - Data to Knowledge is a flexible data mining and machine learning system that integrates analytical
data mining methods for prediction, discovery, and
deviation detection, with information visualization tools
(D2K). It provides a graphic-based environment where users with no knowledge in computation and programming can easily bring together software functional modules and make an itinerary, in which a unique data flow and a task are performed. These modules and the entire D2K environment are written in Java for maximum flexibility and portability.
The data mining and machine learning techniques that have been implemented in D2K include association rule, Bayes rule, support vector machine, decision tree, etc. These techniques provide many possibilities of classifying collections available to this project, like hundreds of Emily Dickinson’s poems.
Although D2K has the ability of performing basic natural language processing tasks, it is still beneficial to delegate those tasks to a toolkit that is specifically designed to do this, i.e., Tamarind. Both D2K and Tamarind use Gate as their fundamental natural language processing toolkit. Gate has been in development at the University of Sheffield since 1995
and has been used in a wide variety of research and
development projects (Gate).
Tamarind is a text mining preprocessing toolkit built on Gate, analyzing XML-based text collections and putting the results into database tables (Downie 2005). It serves
as a bridge between Gate and D2K, and connects them through the use of persistent database. It supports
JDBC-based data retrieval, as well as SOAP-based
language-independent APIs.
Table 1 shows a typical table in Tamarind database. The “xpath” field contains the location of a token in the TEI document in terms of XPath expression, “doc_id” is the unique ID of the TEI
document, while “t_type_id” is the part-of-speech tag. Based on this table, some statistical characteristics of tokens, like term occurrence (term frequency), co-
occurrence and document frequency, could be generated,
thereby obviating the data mining toolkit (D2K) from performing the data-preparing task.After the whole collections is parsed and analyzed, the information related to the position, part of speech and type of each token is stored in a PostgreSQL database for future access. The Tamarind application exposes these
information so that D2K as a client can connect and
retrieve them through JDBC (Java Database Connectivity)
or SOAP (Simple Object Access Protocol).
4 NORA Architecture
Several issues were raised as to how to effectively and efficiently connect physically and institutionally
distributed components in the NORA project. For example,
should Tamarind expose its data to client through Java API (as a Java JAR file) or SOAP API (through Web
service)? Should Tamarind just provide raw data like that in the previous table or something more advanced and complicated like the frequently used TF-IDF value? Is D2K responsible for converting the database table to a data structure more convenient for D2K to handle, like D2K table? How can the user requests be conveyed to D2K in a user-friendly and compact fashion?
Experiments and discussion eventually led to the adoption of JDBC-based data retrieval and SOAP-based Web service
for user request delivery. Although SOAP-based Web service providing more advanced and platform-independent
API interface is a good choice for delivering Web-based requests, it seems inefficient to transmit large amount of data, like the occurrences of all tokens in the whole collection, through HTTP protocol, especially when the data
store and the text mining application do not reside on the same host. This, however, does not exclude the
possibility of implementing some not-so-data-intensive APIs, like metadata retrieval, through SOAP in the future.
Table 2 gives sample data pairs pulled out of Tamarind
database. It is a list of which token occurs in which
document and is generated by a join of several tables in the Tamarind database.
Table2 : Data (document-token pairs) from Tamarind Database
After data about tokens is pulled out of the Tamarind
database, it is converted to a structure called “D2K
table” which is convenient for the D2K toolkit to handle.
Actually the D2K table is the restructuring of the
token-document pairs taken from the database as a
matrix containing the occurrences of each token in each
document. Table 3 gives an example. Depending on the
collection, it could contain hundreds of rows and thousands
of columns machine learning techniques, like naive Bayes and
support vector machine. For the Dickinson prototype, naive Bayes algorithm is used and an overall classification
accuracy of over 70% is achieved. In the prototype, the D2K toolkit is launched by Infovis, an information
visualization toolkit, through Web service.
Figure 1 depicts the control flow of the whole prototype system.
Figure 1. NORA Architecture
Armstrong, S. (1994). Using Large Corpora. MIT Press.
Kirschenbaum, M. Plaisant, C. Smith, M. Auvil, L. Rose, J. Yu, B. and Clement, T. (2006) Undiscovered
public knowledge: Mining for patterns of erotic
language in Emily Dickinson’s correspondence with Susan (Gilbert) Dickinson. ACH/ALLC 2006.
Downie, S. Unsworth, J. Yu, B. Tcheng, D. Rockwell, G. and Ramsay S. (2005) A revolutionary approach to humanities computing?: Tools development and the D2K data-mining framework. ACH/ALLC 2005.

If this content appears in violation of your intellectual property rights, or you see errors or omissions, please reach out to Scott B. Weingart to discuss removing or amending the materials.

Conference Info



Hosted at Université Paris-Sorbonne, Paris IV (Paris-Sorbonne University)

Paris, France

July 5, 2006 - July 9, 2006

151 works by 245 authors indexed

The effort to establish ADHO began in Tuebingen, at the ALLC/ACH conference in 2002: a Steering Committee was appointed at the ALLC/ACH meeting in 2004, in Gothenburg, Sweden. At the 2005 meeting in Victoria, the executive committees of the ACH and ALLC approved the governance and conference protocols and nominated their first representatives to the ‘official’ ADHO Steering Committee and various ADHO standing committees. The 2006 conference was the first Digital Humanities conference.

Conference website:

Series: ACH/ICCH (26), ACH/ALLC (18), ALLC/EADH (33), ADHO (1)

Organizers: ACH, ADHO, ALLC

  • Keywords: None
  • Language: English
  • Topics: None