- Open Access
- Total Downloads : 288
- Authors : Arlina D’Cunha, Dr. A. K. Sen
- Paper ID : IJERTV4IS090027
- Volume & Issue : Volume 04, Issue 09 (September 2015)
- DOI : http://dx.doi.org/10.17577/IJERTV4IS090027
- Published (First Online): 03-09-2015
- ISSN (Online) : 2278-0181
- Publisher Name : IJERT
- License: This work is licensed under a Creative Commons Attribution 4.0 International License
A Simple Approach for Scientific Document Categorization
St. Francis Institute of Technology Mumbai, India
Dr. A. K. Sen
St. Francis Institute of Technology Mumbai, India
Abstract Classification is the alignment of data or items in predefined labeled groups based on resemblances. Exponential progression amount of scientific documents leads to uncontrollable physical classification. Feature extraction is the crucial condition of automatic document classification. TF-IDF (term frequency-inverse document frequency) is frequently used to represent the text feature weight. This paper proposes a new yet simple feature weighting scheme by modifying TF-IDF formula. The experimental results show that the modified method improves the accuracy and other parameters.
Text mining is a flourishing new area that efforts to glean meaningful information from natural language text. It may be roughly characterized as the process of analyzing text to extract information that is useful for specific purposes. Text is amorphous, ambiguous and rough to deal with algorithmically. Yet it is the most common vehicle for the formal exchange of information. The area of text mining usually deals with texts whose function is the communication of realistic information or opinions. Information Retrieval (IR) is the science of searching for data in documents, documents itself or metadata which define documents and classification i.e. assemblage of information in predefined labelled classes based on likenesses leads to good IR. Classification of scientific documents is a task done by professional libraries where the standard for classifying
The Tools and Techniques section offers suitable structures for other scientists to reproduce the experiments presented in the paper.
The Results and Discussion sections present and converse the research results, respectively. They are frequently combined into one section, but readers can hardly make logic of results alone without additional clarification.
The Conclusion section presents the consequence of the work by concluding the findings at a higher level of abstraction and by linking these findings to the motivation stated in the Introduction.
Document Classification Overview
Document (or text) classification runs in two phases: the training phase and the testing (or classification) phase.
Through training, a feature extractor is used to transform every document to a feature set, which capture the simple details about each document that should be used to classify it. Feature collections and labels are served to the classification model to produce a model. During testing, the same feature extractor is used to alter non-classified documents to feature sets followed by the model to assign tags to input documents.
TF-IDF for Feature Extraction 
TF can be calculated as per equation (1) and equation (2) gives IDF
documents is subject to several features and attributes.
A. Overview of Scientific Document
Superlatively, scientific documents should be reasonable to
nonscientist individuals who may be involved in scientific issues, or may be in a position to backing scientific tasks. Expansion of the scientific document has been inspirational, both in diversity of content and in the complexity with which
Where td is number of times term t appears in a document
d and Td is total number of terms in the document d.
this content is discussed. Nevertheless, at origin these
documents are often crude activities. Authors who have facts and figures vital to the growth of the human race are often displeased by boundaries of time and linguistic in their efforts to be perceived. Scientific Documents that detail investigational work are often arranged chronologically in five sections: first, Introduction; then Tools and Techniques,
The feature vector of document d from collection D with n different terms is denoted as follows:
Results, and Discussion and lastly, Conclusion.
The Introduction section explains the motivation for the
dd [w1,d , w2,d ……..wn,d ]
effort presented and makes readers for the organization of the paper.
TFt ,d * IDFt
Wt,d is the weight of term t of document d.
Efficient information retrieval needs efficient classification. Classification of scientific documents is the grouping of information or documents in predefined labeled categories based on similarities. Exponential development of scientific document collection leads to unmanageable manual classification. Thus automatic classification of scientific documents into categories is an increasingly important task. Feature extraction is the central prerequisite of automatic document classification. TF-IDF (term frequency-inverse document frequency) is commonly used to express the text feature weight. This research proposes a new feature weighting method by modifying TF-IDF formula.
Literature  gives a brief overview of scientific document classification. This paper undergoes every phase of the methodology in order to be classified and instantiated in ontology that models knowledge matters. Once the ontology is populated it can be used to performed implications and obtained hidden knowledge from the papers.
The Vector Space Model (VSM) proposed by Salton  is a common method for document representation in classification where each document is represented as a vector of features. Each feature is associated with a weight. Typically these features are simple words in document. The feature weight can be just a Boolean value indicating the presence or absence of the word in document, its existence number in document or it can be calculated by a formula like the well-known TF-IDF   method which treats a document as a "bag of terms" .
After extracting all features from document and calculating their weight document vector is constructed to feed into classifier model. The NaÃ¯ve Bayes Classifier  is the simplest probabilistic classifier used to classify the text documents. It severe assumption that each feature word is independent of other feature words in a document . The idea is to use the joint probabilities of words and categories to
The traditional centroid-based method , can be observed as a specialization of Rocchio method  and used in numerous works on text classification .
Scientific Document Hierarchy Construction
We considered a scientific document as a hierarchy in which the nodes are tagged by structural labels like title, keywords, abstract, etc. The bottom-most node contains the document text.Fig.3 illustrates one such example of scientific document hierarchy.
Fig.1. An example of scientific document hierarchy
After constructing the aggregated tree, we need to extract the terms in each node. We need to apply a series of preprocessing. Pre-processing may involve text-extraction, stop-word removal, tokenization, etc.
Our assumption is that a term which appears in two different structural levels should have different importance. For example the word Enginee appears in the document title and in paragraph composes two different features and the weight of the first feature is more significant than the second. For calculating the weight of features we consider a modification of traditional tf*idf on structural element level instead of document level. So, the weight of feature will be calculated as per equation (5)
estimate the class of a given document. Given an unknown document sample D, Naive Bayesian classification will classify D as the class with the highest posterior probability
TF * IDF * ED
i.e., Bayesian classification assigns the unknown sample to the class Ci if and only if P(Ci|D) > p(Cj|D), where 1 j m, j i, the class Ci is called as the maximum posteriori
ED log Ld
assumption when P(Ci|D) is the largest. P (Ci|D) is according to Bayesian theorem .
Instead of using frequency selection method for assigning the features generated in the training stage to their correct category, FRAM assigns the features that are generated from the new given document to their categories based on the Frequency Ratio (FR) of the features that are sorted in the training stage. Assigning the features by using FRAM involves combining it with the classification process. Thus the time for the training stage will be reduced by excluding the feature selection task.
ED is element depth to judge the significance of the structural element e. Where Ld is the depth of document hierarchy, and ld,e is the depth of the node e in the document .
As per our assumption, content information has least importance. Thus even if we ignore certain numbers of terms from the content and consider only T terms from the content for weight computation, it should not affect accurate classification prediction. Terms from title, keywords and abstract should affect the classification prediction.
After weight computation, the document vector feed into classifier model.
EXPERIMENTS AND EVALUATION
We considered nine categories as shown in Table 1. Including 904 scientific documents from various open access journals  as training dataset, another 304 documents to test the system. Pretreatment involves text extraction, tokenization and remove the stop words.
Table 1: Classification Categories under consideration
and Data Mining
Computer Security and
Internet, Web Services and Cloud
Image and Video Processing
Human-Machine Interaction and Virtual
The performance evaluation of the classifier usually used evaluation indicator which are some quantitative index which used to evaluate the performance of classification in the testing process. The well-known use of performance evaluation indicators in the text classification contained Recall, Precision, F-Score, Specificity and Accuracy. The higher of this evaluation indicator value, the better performance of the classification model is. Formulas are as follows:
Table 2: Average Execution time (in ms) for 304 documents
Improved TF- IDF
Time saved (in %)
As per our previous discussion, terms occurring in title, keywords and abstract have high impact on the weight in document vector, these terms play important role in classification. Thus overall performance of the system has been improved as shown in following comparative graphs.
Fig 2: Average Precision comparison for different algorithms
Fig 2: Average Precision comparison for different algorithms
True _ Positive
True _ Positive False _ Positive
True_ Positive False _ Negative
True _ Negative
True _ Negative False _ Positive
2True _ Positive
2True _ Positive False _ Positive False _ Negative
Accuracy True _ Positive True _ Negative
Fig 3: Average Recall comparison for different algorithms Specificity (%)
True _ Positive False _ Positive False _ Negative True _ Negative
Other parameter used to evaluate the performance of the system is Execution Time.
Analysis of Results
The experiment included comparison of FRAM, Naive- Bayes and Centroid classification algorithms with TF-IDF and improved TF-IDF.
As we have considered certain predefined number of terms from content information, execution time of overall execution can be saved which is illustrated in table 2.
Algorithm Fig 4: Average Specificity comparison for different algorithms
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Fig 5: Average Specificity comparison for different algorithms
Fig 6: Average Accuracy comparison for different algorithms
The results show that using improved TF-IDF approach not only provides a more graceful and simpler solution to the classification problem, but also results in considerable performance gain in terms of classification.
Automatic document classification is a machine learning task that automatically assigns a given document to a set of pre-defined categories based on the features extracted from its textual content. Our proposed work involves modification of TF-IDF and its effects on three classification algorithms for scientific documents. Experiments were conducted to test Execution Time, Precision, Recall, Specificity, F- Score and Accuracy. Experimental results proved that the parameters tested were improved compared to the existing system.
We are grateful to Ms. Vincy Joseph and Ms.Anuradha S., Associate Professors at St. Francis Institute of Technology for their insightful comments and suggestions. We would also like to thank Mr. Abhitesh Das, Technical architect, CACTUS Communications, for his valuable guidance.
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