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We present WebNLP, a web-based tool that combines natural language processing (NLP) functionality from Python NLTK and text visualizations from Voyant in an integrated interface. Language data can be uploaded via the website. The results of the processed data are displayed as plain text, XML markup, or Voyant visualizations in the same website. WebNLP aims at facilitating the usage of NLP tools for users without technical skills and experience with command line interfaces. It also makes up for the shortcomings of the popular text analysis tool Voyant, which, up to this point, is lacking basic NLP features such as lemmatization or POS tagging.
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WebNLP – An Integrated Web-Interface
for Python NLTK and Voyant
Manuel Burghardt, Julian P¨
orsch, Bianca Tirlea, & Christian Wolff
Media Informatics Group, University of Regensburg
We present WebNLP, a web-based tool
that combines natural language process-
ing (NLP) functionality from Python NLTK
and text visualizations from Voyant in an
integrated interface. Language data can be
uploaded via the website. The results of
the processed data are displayed as plain
text, XML markup, or Voyant visualiza-
tions in the same website. WebNLP aims
at facilitating the usage of NLP tools for
users without technical skills and experi-
ence with command line interfaces. It also
makes up for the shortcomings of the pop-
ular text analysis tool Voyant, which, up
to this point, is lacking basic NLP features
such as lemmatization or POS tagging.
1 Introduction
Modern corpus linguistics has been on the rise
since the late 1980s (Hardie, 2012), largely be-
cause of the availability of vast amounts of dig-
ital texts and computer tools for processing this
kind of data. Since then, corpus linguistics has
produced a number of important subfields, such
as web as a corpus (cf. Kilgarriff and Grefen-
stette, 2003; Baroni et al., 2009), language in the
social media (cf. Beißwenger and Storrer, 2009)
or using language data for sentiment and opinion
mining (cf. Pak and Paroubek, 2010). More re-
cently it has been claimed that the mass of dig-
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ital text available for automatic analysis consti-
tutes a new research paradigm called culturomics
(Michel et al., 2010) and that the recent arrival of
the digital humanities opens up additional fields
of application for corpus linguistics and text min-
ing. Taking the increased amount of digital text
data which is readily available into consideration,
Gregory Crane has asked the well justified ques-
tion “what to do with a million books” (Crane,
2006). The question is partially answered by
Moretti (2013), who introduces the idea of dis-
tant reading of texts, as opposed to the more tradi-
tional, hermeneutic close reading, which is partic-
ularly popular in the field of literary studies. The
idea of distant reading suggests to interpret liter-
ary texts on a more generic level by aggregating
and analyzing vast amounts of literary data.
All these novel types of applications require ba-
sic NLP analysis such as tokenization, lemmatiza-
tion, POS tagging, etc. Currently, there is no lack
of adequate tools than can be used to process large
amounts of text in different languages. Promi-
nent examples are GATE (General Architecture
for Text Engineering)1or the UIMA framework
(Unstructured Information Management Infras-
tructure)2. However, most of these tools can be
characterized as having a fairly high entry bar-
rier3, confronting non-linguists or non-computer
scientists with a steep learning curve, due to the
1Available at; all web re-
sources described in this article were last accessed on May
4, 2014.
2Available at
3Hardie (2012) gives a short overview of the develop-
ment of corpus analysis tools while at the same time dis-
cussing their usability requirements.
fact that available tools are far from offering a
smooth user experience (UX). This may possibly
be caused by complex interaction styles typically
encountered in command line interfaces, by sub-
optimal interface design for graphical user inter-
faces (GUIs) or by the necessity of bringing to-
gether disparate tools for a specific task.
Nowadays, a decent UX is a basic requirement
for the approval of any application such as of-
fice tools or smartphone apps (Nielsen and Budiu,
2013). At the same time, a large and well ac-
cepted body of knowledge on usability and user
centered design (cf. Shneiderman, 2014) is at our
disposal. However, tools developed for scientific
purposes like corpus linguistics or text mining do
not seem to take advantage of these knowledge
sets: It appears that many tools are designed by
scientists who may have acquired the necessary
programming and software engineering skills, but
who are lacking experience and training in user
interface design and usability engineering. As a
result, many tools are functionally perfect, but an
obvious mess as far as usability aspects are con-
In the following, we will not introduce yet an-
other tool, but we rather try to provide an inte-
grated, easy-to-use interface to existing NLP and
text analysis tools.
2 Tools for NLP and text analysis
There are a number of available tools that can be
used for NLP tasks and quantitative text analy-
sis (cf. the notion of distant reading). This sec-
tion introduces some of the most prominent tools,
and also makes the case for the newly created
WebNLP prototype.
2.1 Python NLTK
Python NLTK4(Bird, 2006) is a widely used
toolkit that allows the user to perform sophisti-
cated NLP tasks on textual data and to visual-
ize the results. One drawback of NLTK, how-
ever, is its command line interface. Also, a ba-
sic understanding of the programming language
Python is necessary for using it. Depending on
the target platform, setting up the NLTK environ-
ment can be rather cumbersome. For these rea-
4Available at
sons, many humanities scholars who are lacking
technical skills in Python and command line in-
terfaces may refrain from using NLTK as a means
for NLP.
2.2 TreeTagger
TreeTagger5(Schmid, 1994), another widely used
NLP tool, tries to address this issue by providing
a GUI (only available for Microsoft Windows)6.
The output of the tool can however not be visual-
ized in the same GUI.
2.3 Voyant Tools
Voyant7(cf. Ruecker et al., 2011) is a web-based
tool that is very popular in the digital humanities
community. It allows the user to import text doc-
uments and performs basic quantitative analysis
of the data (word count, term frequency, concor-
dances, etc.). The results of this analysis are vi-
sualized in the browser, e.g. as KWIC lists, word
clouds or collocation graphs. While the tool is
easy to use via a modern web browser, Voyant is
lacking a feature to perform basic NLP operations
(e.g. lemmatization) on the data before it is ana-
2.4 The case for WebNLP
It shows that many of the existing tools are ei-
ther not accessible to non-technical users due to
their technical complexity, or that they are lack-
ing important functionality. The goal of this work
is to provide an easy-to-use interface for the im-
port and processing of natural language data that,
at the same time, allows the user to visualize the
results in different ways. We suggest that NLP
and data analysis should be combined in a sin-
gle interface, as this enables the user to experi-
ment with different NLP parameters while being
able to preview the outcome directly in the visu-
alization component of the tool. We believe that
the immediate visualization of the results of NLP
operations makes the procedure more transparent
for non-technical users, and will encourage them
to utilize NLP methods for their research.
5Available at http://www.cis.uni-muenchen.
6Available at
7Available at
Figure 1: WebNLP architecture and main components.
In order to achieve this goal, we integrate two
existing tools (Python NLTK and Voyant) in a
combined user interface named WebNLP8.
3 WebNLP
In this section we describe the basic architecture
of WebNLP and explain the main functions and
interface components of the tool.
3.1 Tool architecture
We decided to implement the interface as a web
service for several reasons:
No installation or setup of Python NLTK and
related Python modules by the user is re-
Previous experience and familiarity of non-
technical users with web services and inter-
active elements such as form fields,radio
buttons, etc.
8WebNLP is currently available as a prototype at
Seamless integration of the existing web tool
Voyant, which allows the user to quickly
analyze and visualize language data in the
Opportunities for future enhancements of the
tool, e.g. collaboration with other users,
sharing of data and results, etc.
WebNLP uses a client-server architecture to pro-
vide an easy-to-use interface via modern web
browsers, while the NLP functions are executed
on our server (cf. Figure 1). The interface on
the client side is structured in three main areas
(cf. Figure 2) which will be explained in more
detail in the next section. All interface logic is
implemented by means of JavaScript, the page
layout utilizes a template from the popular front-
end framework Bootstrap9. The communication
between client and server is realized by means of
9Bootstrap is available at http://getbootstrap.
Figure 2: WebNLP interface with three main areas: input, options, results.
A number of Python NLTK scripts (e.g. for
tokenization, lemmatization, etc.) can be called
from the client interface and are then executed
on the server. The results are displayed on the
client side by calling different visualization forms
of the web service Voyant, which is embedded in
the WebNLP interface as an HTML iframe. At
the same time, the NLTK processed data is stored
on the server as plain text or as text with XML
markup, which are both available for download
on the client side.
3.2 Input: Upload of natural language data
The input field allows the user to upload text doc-
uments to the NLP application on the server. Data
may either be entered directly in the text area form
field, or by making use of the file upload dialog.
Currently, only files in plain text format (.txt) can
be processed by the NLTK tools on our server.
Another restriction for the current implementa-
tion of the tool is concerned with the language of
the text documents: At the moment, only NLTK
scripts for processing English language data have
been integrated into the tool. However, the sys-
tem architecture is designed in a modular fash-
ion that allows the administrators to add more
NLTK scripts for other languages at a later point
in time. Once the data has been uploaded to the
server, a first NLTK pre-processing of the data is
executed, analyzing the overall number of tokens,
types and sentences in the file. This information
is displayed at the bottom of the input area after
the upload of the file has been completed.
3.3 Options: NLP and visualization
The second area in the interface contains options
for the NLP and visualization of the uploaded
data. The first set of options selects Python NLTK
scripts on the server, that are then executed on the
data. In the current tool version, the following
main functions are available:
Stop word filter; can be combined with any
other parameter (a list of all stop words may
be looked up in the interface)
Tokenizer (words and punctuation marks)
Part of speech tagger (tokenization implied)
Lemmatizer (tokenization implied)
No NLP (used if no additional NLP process-
ing is needed)
The second group of options allows the user to
select a visualization style for the processed data
from Voyant. The following visualization10 op-
tions are available in the current WebNLP proto-
Type frequency list
Collocation clusters
Terms radio
Scatter plot
Type frequency chart
No visualization
Due to the internal NLP workflow on the server,
currently only one NLP and one visualization op-
tion can be selected at a time. We are planning
to implement a more flexible solution in the next
version of WebNLP.
A short evaluation with a sample of five text
documents with different file sizes indicates an al-
most linear increase of processing time related to
text size. The smallest of the test documents had a
size of 50 kB (approx. 11.000 tokens), the largest
document had a size of 4230 kB (approx. 920.000
tokens). POS tagging for the smallest document
took 18 seconds, lemmatization took 20 seconds.
For the largest document, POS tagging took ap-
prox. 24 minutes, lemmatization took approx. 25
minutes. These results indicate that WebNLP in
its current implementation is well-suited for small
to medium sized corpora, but may be too slow for
larger text collections.
3.4 Results: Client-side visualizations and
download formats
The third interface area displays the results of the
chosen NLP options in the selected Voyant visual-
ization (e.g. word cloud view). The user may also
10A detailed description of the different Voyant visualiza-
tion types can be found at
switch to plain text or XML markup view of the
results (these formats are also available for down-
Plain text view (original NLTK output):
( VBN , come )
XML view (custom WebNLP format):
4 Conclusions
Our tool provides access to existing NLP and vi-
sualization tools via a combined interface, thus
acting as a GUI wrapper for these applications.
While a thorough usability evaluation is still
missing, we are confident that NLP functionality
from the Python NLTK becomes more accessible
through WebNLP, and that the combination with
visualizations from the Voyant set of tools will
be attractive for many applications of text tech-
nology. In its current implementation, WebNLP
should be treated as a prototype that illustrates
how a web-based interface to basic NLP and text
visualization functions can be realized by means
of standard web technologies. We are, however,
planning to implement more NLTK functions,
and to improve the performance as well as the in-
terface of the service in the future.
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Adequate treatment of cardiac failure should reflect both clinical rules and quantitative evaluation of hemodynamics. For the latter we assumed an extensive model of heart and vessels: For the left and right ventricles the EMAX models were assumed with ...