1. STAT Technical Report Version 0.1
by the Stat Team
Mehrbod Sharifi
Jing Yang
The Stat Project, guided by
Professor Eric Nyberg and Anthony Tomasic
March. 5, 2009

2. Chapter 1
Introduction to STAT
In this chapter, we give an brief introduction to the Stat project to audience reading this document.
We explain the background, the motivation, the scope, and the stakeholders of this project so that
audience can understand why we are doing so, what we are going to do, and who may be interested
in our project.
1.1 Overview
Stat is an open source machine learning framework in Java for text analysis with focus on semi-
supervised learning algorithms. Its main goal is to facilitate common textual data analysis tasks
for researcher and engineers, so that they can get their works done straightforwardly and efficiently.
Applying machine learning approaches to extract information and uncover patterns from tex-
tual data has become extremely popular in recent years. Accordingly, many software have been
developed to enable people to utilize machine learning for text analytics and automate such pro-
cess. Users, however, find many of these existing software difficult to use, even if they just want
to carry out a simple experiment; they have to spend much time learning those software and may
finally find out they still need to write their own programs to preprocess data to get their target
software running.
We notice this situation and observe that many of these can be simplified. A new software
framework should be developed to ease the process of doing text analytics; we believe researchers
or engineering using our framework for textual data analysis would feel the process convenient,
conformable, and probably, enjoyable.
1.2 Purpose
Existing software with regard to using machine learning for linguistic analysis have tremendously
helped researchers and engineers make new discoveries based on textual data, which is unarguably
one of the most form of data in the real world.
As a result, many more researchers, engineers, and possibly students are increasingly interested
in using machine learning approaches in their text analytics. However, the bar for entering this
area is not low. Those people, some of which even being experienced users, find existing software
packages are not generally easy to learn and convenient to use.
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3. For example, although Weka has a comprehensive suite of machine learning algorithms, it is
not designed for text analysis, lacking of naturally supported capabilities for linguistic concepts
representation and processing. MinorThird, on the other hand, though designed specifically as a
package for text analysis, turns out to be rather complicated and difficult to learn. It also does not
support semi-supervised and unsupervised learning, which are becoming increasingly important
machine learning approaches.
Another problem for many existing packages is that they often adopt their own specific input
and output format. Real-world textual data, however, are generally in other formats that are not
readily understood by those packages. Researchers and engineers who want to make use of those
packages often find themselves spending much time seeking or writing ad hoc format conversion
code. These ad hoc code, which could have been reusable, are often written over and over again
by different users.
Researchers and engineers, when presented common text analysis tasks, usually want a text-
specific, lightweight, reusable, understandable, and easy-to-learn package that help them get their
works done efficiently and straightforwardly. Stat is designed to meet their requirements. Moti-
vated by the needs of users who want to simplify their work and experiment related to textual data
learning, we initiate the Stat project, dedicating to provide them suitable toolkits to facilitate
their analytics task on textual data.
In a nutshell, Stat is an open source framework aimed at providing researchers and en-
gineers with a integrated set of simplified, reusable, and convenient toolkits for textual
data analysis. Based on this framework, researchers can carry out their machine learning
experiments on textual data conveniently and comfortably, and engineers can build their
own small applications for text analytics straightforwardly and efficiently.
1.3 Scope
This project involves developing a simplified and reusable framework (a collection of foundation
classes) in Java that provides basic and common capabilities for people to easily perform machine
learning analysis on various kind of textual data.
The previous section may give an impression for an impossible task. In this section, we clearly
state what is and is not included in this project.
The main deliverable for this project is a set of specifications, which defines a simplified frame-
work for text analysis based on NLP and machine learning. We explain how succinctly the frame-
work should be used and how easily it can be extended.
We also provide introductory implementations of the framework, including tools and packages
serving foundation classes of the framework. They are
• Dataset and framework object adaptors: A set of classes that will allow reading and
writing files in various formats, supporting importing and exporting dataset as well as loading
and saving framework objects.
• Linguistic and machine learning packages wrappers: A set of classes that integrate
existing tools for NLP and Machine Learning and can be used within the framework. These
wrappers hides the implementation and variation details of these packages to provide a set
of simplified and unified interfaces to framework users.
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4. • Semi-Supervised algorithms: Implementation of certain Semi-Supervised learning algo-
rithms that are not available from the existing packages.
Finally, we provide a set documents that reflect our development process and give guidance to
users of how to use our framework. These documents are
• Technical report: An report summarizing major artifacts we have and documenting vision,
goals, motivation, and decisions. It also includes result of requirements and design phase,
and results of final evaluation. This report gives overall comprehension to whom want to
understand our package as well as software development process well.
• Executive summary: An summary that gives brief introduction to users of our framework,
explaining benefits that this framework can bring to them in text analysis.
• JavaDocs, tutorials, and examples: JavaDocs about APIs specifications extracted from
comments in the code. Tutorials and concretes examples are also provided to ease the process
learning this framework.
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5. 1.4 Stakeholders
Below is the list of stakeholder and how this project will affect them:
• Researchers, particularly in language technology but also in other fields, would be able
to save time by focusing on their experiments instead of dealing with various input/output
format which is routinely necessary in text processing. They can also easily switch between
various tools available and even contribute to STAT so that others can save time by using
their adaptors and algorithms.
• Software engineers, who are not familiar with the machine learning can start using the
package in their program with a very short learning phase. STAT can help them develop clear
concepts of machine learning quickly. They can build their applications using functionality
provided STAT easily and achieve high level performance.
• Developers of learning package, can provide plug-ins for STAT to allow ease of integration
of their package. They can also delegate some of the interoperability needs through this
program (some of which may be more time consuming to be addressed within their own
package).
• Beginners to text processing and mining, who want fundamental and easy to learn
capabilities involving discovering patterns from text. They will be benefited from this project
by saving their time, facilitating their learning process, and sparking their interests to the
area of language technology.
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6. Chapter 2
Survey Analysis
This project was faced with many challenges from the beginning. There are many question, some
of subjective nature, that really needs to be addresses by our target audience. For this reason, we
designed a survey to obtain a better understanding and provide a more suitable solution to this
problem. In this chapter, we explain the process of designing the survey, collecting information
and some analysis of the collected data.
2.1 Designing the Survey
The primary goals of doing a survey was the following:
• Understanding the potential users of the package: their programming habit, problem solving
strategies, experience in various area and tools, etc.
• Setting priority for which criteria to focus on for our design and implementation
The survey needed to be short and question to be very specific to get better responses. The
maximum number of question was set at 10 questions. Several draft of the questions was reviewing
within the STAT group and the software engineering class students and instructors several times
until finalize. We also obtained and incorporate some advices from other departments. The final
survey was designed on the SurveyMonkey.com.
2.2 Distribution
The target users of STAT are two main groups with different needs: researchers and industry
programmer. The survey contains questions to distinguish there two group but the final framework
should address the needs from both groups. After conducting a test run with this the STAT group
and the class, we sent the survey out to the Language Technology Institute student mailing list
(representing researchers) and also to student in iLab (Prof, Ramayya Krishnan, Heinz School of
Business) representing industry programmers.
2.3 Analysis of Results
As of 2/25/09, we have received 23 responses and they are individually reviewed by STAT members
and also in aggregate. Below we summarized the finding of the survey result and some charts:
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7. • While many different programming language are used (Python, R, C++) but over 90%
mentioned Java as one of the languages (25% consider themselves expert). The programming
hours range from 2-60.
• Users don’t seem to distinguish much between industry and research applications and this is
perhaps more research for the different to be transparent.
• Most users are not familiar with Operation Research but everyone is somewhat familiar with
Machine Learning (if not specifically text classification or data mining).
• Data type expectedly were mostly textual (plain, XML, HTML, etc. as opposed to Excel,
though it was mentioned) and sources were files, databases and web.
• Over 50% chose: ”I write a program to preprocess data and then use an external machine
learning package.”
• Easy of API use, Performance and Extensibility were the top three choice in design but in
addition to those in textual descriptions user pointed out mostly problems with input and
output formats.
Figure 2.1: Distribution of familiarity with packages
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8. Figure 2.2: Distribution of design preference
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9. Chapter 3
Analysis of Related Packages
In this chapter, we analyze a few main competitors of our projects. We focus on two academic
toolkits – Weka and MinorThird. We comment on their strengths and explore their limitations, and
discuss why and how we can do better than these competitors.
3.1 Weka
Weka is a comprehensive collection of machine learning algorithms for solving data mining problems
in Java and open sourced under the GPL.
3.1.1 Strengths of Weka
Weka is a very popular software for machine learning, due to the its main strengths:
• Provide comprehensive machine learning algorithms. Weka supports most current
machine learning approaches for classification, clustering, regression, and association rules.
• Cover most aspects for performing a full data mining process. In addition to learn-
ing, Weka supports common data preprocessing methods, feature selection, and visualization.
• Freely available. Weka is open source released under GNU General Public License.
• Cross-platform. Weka is cross-platform fully implemented in Java.
Because of its supports of comprehensive machine learning algorithm, Weka is often used for
analytics in many form of data, including textual data.
3.1.2 Limitations of using Weka for text analysis
However, Weka is not designed specifically for textual data analysis. The most critical drawback
of using Weka for processing text is that Weka does not provide “built-in” constructs for natural
representation of linguistics concepts1 . Users interested in using Weka for text analysis often find
themselves need to write some ad-hoc programs for text preprocessing and conversion to Weka
representation.
• Not good at understanding various text format. Weka is good at understanding its
standard .arff format, which is however not a convenient way of representation text. Users
have to worry about how can they convert textual data in various original format such as
1
Though there are classes in Weka supporting basic natural language processing, they are viewed as auxiliary
utilities. They make performing basic textual data processing using Weka possible, but not conveniently and straight-
forwardly
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10. raw plain text, XML, HTML, CSV, Excel, PDF, MS Word, Open Office document, etc. to
be understandable by Weka. As a result, they need to spend time seeking or writing external
tools to complete this task before performing their actual analysis.
• Unnecessary data type conversion. Weka is superior in processing nominal (aka, categor-
ical) and numerical type attributes, but not string type. In Weka, non-numerical attributes
are by default imported as nominal attributes, which usually is not a desirable type for text
(imagine treating different chunks of text as different values of a categorical attribute). One
have to explicitly use filters to do a conversion, which could have been done automatically if
it knows you are importing text.
• Lack of specialized supported for linguistics preprocessing. Linguistics preprocessing
is a very important aspect of textual data analysis but not a concern of Weka. Weka does
not (at least, not dedicated to) take care this issue very seriously for users. Weka has a
StringToWordVector class that performs all-in-one basic linguistics preprocessing, including
tokenization, stemming, stopword removal, tf-idf transformation, etc. However, it is less
flexible and lack of other techniques (such as part-of-speech tagging and n-gram processing)
for users who want fined grain and advanced linguistics controls.
• Unnatural representation of textual data learning concepts. Weka is designed for
general purpose machine learning tasks so have to protect too many variations. As a results,
domain concepts in Weka are abstract and high-level, package hierarchy is deep, and the
number of classes explodes. For example, we have to use Instance rather than Document and
Instances rather than Corpus. Concepts in Weka such as Attribute is obscure in meaning
for text processing. First adding many Attribute to a cryptic FastVector which then passed
to a Instances in order to construct a dataset appears very awkward to users processing
text. Categorize filters first according to attribute/instance then supervised /unsupervised
make non-expert users feel confusing and hard to find their right filters. Many users may feel
unconformable programmatically using Weka to carry out their experiments related to text.
In summary, for users who want enjoyable experience at performing text analysis, they need
built-in capabilities to naturally support representing and processing text. They need specialized
and convenient tools that can help them finish most common text analysis tasks straightforwardly
and efficiently. This cannot be done by Weka due to its general-purpose nature, despite its com-
prehensive tools.
Figure 3.1 shows the domain model we extracted for Weka for basic text analysis.
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11. Partial UML Domain Model of Weka (Preliminary)
evaluate
1
1
Evaluation Classifier
1 1
1
built-from classify
evaluate-on
1
1 1
1 tranform-attribute contain
StringToWordVector Instances Instance
1
1 attributeValues
*
1
contain
*
Attribute
NominalToString transform-type 1
1
possibleValues
Note: when you see ClassA quot;containsquot; a number of ClassB,
it is probably that Weka implements it as ClassA maintains a
quot;FastVectorquot; whose elements are instances of ClassB.
Figure 3.1: Partial domain model for Weka for basic text analysis
3.2 MinorThird
Figure 3.2 shows the domain model we extracted for MinorThird for basic text analysis.
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12. Figure 3.2: Partial domain model for MinorThird for basic text analysis
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13. Chapter 4
Requirements specifications
Here we first explain in detail the major features of our framework.
• Simplified. APIs are clear, consistent, and straightforward. Users with reasonable Java
programming knowledge can learn our package without much efforts, understand its logical
flow quickly, be able to get started within a small amount of time, and finish the most common
tasks with a few lines of code. Since our framework is not designed for general purposes and
for including comprehensive features, there are space for us to simplify the APIs to optimize
for those most typical and frequent operations.
• Extensible and Reusable. Built-in modular supports are provided the core routines across
various phases in text analysis, including text format transformation, linguistic processing,
machine learning, and experimental evaluation. Additional functionalities can be extended
on top of the core framework easily and user-defined specifications are pluggable. Existing
code can be used cross environment and interoperate with external related packages, such as
Weka, MinorThird, and OpenNLP.
• High performance. Performance in terms of speed of algorithms we wrap and we imple-
ment should acceptable for typical experiment and dataset. Specifically, there should not be
significant degrade in performance in using our framework with capabilities provided by ex-
ternal packages; performance of algorithm we implemented should be should not be degrade
from its best complexity much because of our implementation flaws.
4.1 Functional Requirements
In this section, we define most common use cases of our framework and address them in the degree
of detail of casual use case. The “functional requirements” of this project are that the users can
use libraries provided by our framework to complete these use cases more easily and comfortably
than not use.
Actors
Since our framework assumes that all users of interests are programming using our APIs, there is
only one role of human actor, namely the programmer. This human actor is always the primary
actor. There are some possible secondary and system actors, namely the external packages our
framework integrates, depending on what specific use cases the primary actor is performing.
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14. Fully-dressed Use Cases
Use Case UC1: Document Classification Experiment
Scope: Text analysis application using STAT framework
Level: User goal
Primary Actor: Researcher
Stakeholder and Interests:
• Researcher: Want to test and evaluate a classification algorithm (supervised, semi-
supervised or unsupervised) by applying it on a (probably well-known) corpus; the task
needs to be done efficiently with easy and straightforward coding
Preconditions:
• STAT framework is correctly installed and configured
• The corpus is placed on a source readable by STAT framework
Postconditions:
• A model is trained and test documents in the corpus are classified. Evaluation results
are displayed
Main Success Scenario:
1. Researcher imports the corpus from its source into memory. Specifically, the system
reads data from the source, parses the raw format, extracts information according to
the schema, and constructs an in-memory object to store the corpus
2. Researcher performs preprocessing on the corpus. Specifically, for each document, the
researcher tokenizes the text, removes the stopwords, performs stemming on the tokens,
performs filtering, and/or other potential preprocessing on body text and meta data
3. Researcher converts the corpus into the feature vectors needed for machine learning.
The feature vectors are created by analyzing the documents in the corpus, deriving or
filtering features, adding or removing documents, sampling documents, handling missing
entries, normalizing features, selecting features, and/or other potential processing
4. Researcher splits the processed corpus into training and testing set
5. Researcher chooses a machine learning algorithm, set its parameters, and uses it to train
a model from the training set
6. Researcher classifies the documents in the test set based on the model trained
7. Researcher evaluates the classification based on classification results obtained on the
test set and its true labels. Classification is evaluated mainly on classification accuracy
and classification time or if it is unsupervised, on other unsupervised metrics such as
Adjusted Rand Index.
8. Researcher displays the final evaluation result
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15. Use Case UC1: Document Classification Experiment (cont.)
Extensions:
1a. The framework is unable to find the specified source.
1. Throw source not found exception
1b. Researcher loads a previously saved corpus in native format from a file on the disk directly
to memory object, thus researcher does not handle source, format, or schema explicitly.
1a. File not found:
1.Throw file not found exception
1b. Malformed native format:
1.Throw malformed native format exception
4a. Researcher specify a parameter k larger than the number of document or smaller than 1
1. Throw invalid argument exception
1-3, 5a. Researcher saves the in-memory objects of different level of processed corpus rep-
resentation to disk in native format which can be loaded back lately, after finishing each
step.
1-3, 5b. Research exports the in-memory objects of different processed corpus representation
to disk in external formats (e.g., weka arff, csv) which can be processed by external software.
6a. Researcher saves the in-memory model object to disk, which can be loaded back lately.
6b. Researcher loads a previously saved model in native format from a file on the disk directly
to memory object.
1a. File not found:
1. Throw file not found exception
1b. Malformed native format:
1.Throw malformed native format exception
4-8b. To perform k-fold cross validation, the corpus is split to k parts in step 4, and steps
5-8 are repeated k-times by switching each split a testing split and the rest as training.
Researcher combines the evaluations on different test sets obtained in the previous steps and
forms a final classification evaluations
6c. Unsupported learning parameters (the learning algorithm cannot handle the combination
of parameters the researcher specifies)
1. Throw unsupported learning parameters exception
6d. Unsupported learning capability (the learning algorithm cannot handle the format and
data in training set, potentially caused by unsupported feature type, class type, missing
values, etc).
1. Identify exception cause(s)
2. Throw corresponding exception(s)
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16. Use Case UC1: Document Classification Experiment (cont.)
8a. Incompatible between test set and classification (potentially caused by difference in schema
between training set and test set)
1. Throw incompatible evaluation exception
10a. The researcher customizes the display instead of using the default display format.
1.The researcher obtains specific fields of the evaluations via interfaces provided
2.The researcher constructs a customized format using the fields he/she extracts
3.The researcher display it customized format and/or write to a destination
Special Requirements:
• Pluggable preprocessors in step 2-3
• Pluggable learning algorithm in step 6
• Learning algorithm should be scalable to deal with large corpus
• Researcher should be able to visualize results after various steps to trace the state of
different objects (e.g., preprocessed corpus, models, classifications, evaluations)
• Researcher should be able to customize the visualization output
Open Issues:
• How to address the variations issues in reading different sources
• How to (in what form) let research specify parameters for different learning algorithms
• What specifically need to be able to export, persist, and visualize?
• How to implement the corpus splitting in an efficient way (don’t create extra objects)
• How to deal with performance issues of storing large corpora in the memory
• How to deal with internal representation of the dataset in efficient data structure
4.2 Non-functional Requirements
• Open source. It should be made available for public collaboration, allowing users to use,
change, improve, and redistribute the software.
• Portability. It should be consistently installed, configured, and run independent to different
platforms, given its design and implementation on Java runtime environment.
• Performance. It should not be the bottleneck in terms of machine learning analysis. That
is, by wrapping other existing packages, no significant reduce in performance impact would
be observed. For algorithms we implement, our coding should achieve the best performance
as the best complexity as the algorithm could be.
• Documentation. Its code should be readable, self-explained, and documented clearly and
unambiguously for critical or tricky part. It should include an introduction guide for users
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17. to get started, and preferably, provides sample dataset, tutorial, and demos for user to run
examples out of the box.
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18. 4.3 Domain model
In this section, we present the domain model diagram and some explanations about it. A lot of
time was spent on this domain model and it has been evolving to a relative stable one, which will
guide our design in the iteration I.
4.3.1 Domain model diagram
Figure 4.1 shows the domain model diagram of STAT project (for the first iteration). This
domain model intends to give the top level of understanding of concepts in the project rather
than a comprehensive one that includes every details. There are a number of concepts such as
“Annotation”, “Label”, “ProbabilityDistribution” ”DistanceMetric”, and “Partition” (or
maybe “Split”), are not shown in this diagram.
Figure 4.1: Partial domain model for Weka for basic text analysis
Note the diagram also lacks of some top level concepts related to unsupervised learning. These
are topics in milestone requirement and design iteration II. A new domain model incorporates these
concepts will be proposed at that phase. For now, we focus this domain model and give detail
clarifications of what these current concepts are.
4.3.2 Domain concepts clarifications
Mebrbod: Revise definitions of these objects here.
• CorpusReader. CorpusReader read text from a source to a Corpus. No content transfor-
mation is done and everything (label, metadata, body, etc) stay in text format.
• Corpus. Corpus is a set of Documents in text format.
• Annotator. Annotator transforms a Corpus to another Corpus by adding annotations.
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19. • FeatureExtractor. FeatureExtractor transforms a Corpus to a Dataset by converting
Documents in text format to Instances into feature representations.
• Dataset. Dataset is a set of Instances which are features representation of Documents
text.
• Learner. Learner learns a Model from a Dataset.
• Model. Model is a collection of parameters learned from a Dataset by the Learner.
• Classifier. Classifier uses the model to assign classes to Instances in a Dataset and
produces a Classification with respect to the Dataset.
• Classification. Classification contains the classification results and descriptive informa-
tion about the classification process, e.g., which model and classifier are used to produce the
classification results.
• ClassificationEvaluator. Classification computes the evaluation metrics for a classifi-
cation and produces a ClassificationEvaluation.
• ClassificationEvaluation. ClassificationEvaluation contains the evaluation results
and descriptive information about the classification and evaluation process.
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20. Bibliography
[1] TBD
[2] TBD
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