Toward Optimal Feature Selection in Naive Bayes for Text Categorization

Automated feature selection is important for text categorization to reduce the feature size and to speed up the learning process of classifiers. In this paper, we present a novel and efficient feature selection framework based on the Information Theory, which aims to rank the features with their discriminative capacity for classification. We first revisit two information measures: Kullback-Leibler divergence and Jeffreys divergence for binary hypothesis testing, and analyze their asymptotic properties relating to type I and type II errors of a Bayesian classifier. We then introduce a new divergence measure, called Jeffreys-Multi-Hypothesis (JMH) divergence, to measure multi-distribution divergence for multi-class classification. Based on the JMH-divergence, we develop two efficient feature selection methods, termed maximum discrimination ($MD$) and $MD-\chi^2$ methods, for text categorization. The promising results of extensive experiments demonstrate the effectiveness of the proposed approaches.Comment: This paper has been submitted to the IEEE Trans. Knowledge and Data Engineering. 14 pages, 5 figure

An Intelligent System For Arabic Text Categorization

Text Categorization (classification) is the process of classifying documents into a predefined set of categories based on their content. In this paper, an intelligent Arabic text categorization system is presented. Machine learning algorithms are used in this system. Many algorithms for stemming and feature selection are tried. Moreover, the document is represented using several term weighting schemes and finally the k-nearest neighbor and Rocchio classifiers are used for classification process. Experiments are performed over self collected data corpus and the results show that the suggested hybrid method of statistical and light stemmers is the most suitable stemming algorithm for Arabic language. The results also show that a hybrid approach of document frequency and information gain is the preferable feature selection criterion and normalized-tfidf is the best weighting scheme. Finally, Rocchio classifier has the advantage over k-nearest neighbor classifier in the classification process. The experimental results illustrate that the proposed model is an efficient method and gives generalization accuracy of about 98%

Multi modal multi-semantic image retrieval

PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ ability to find and access the specific visual information of interest to them. In recent years, ontology knowledge-based (KB) image information retrieval techniques have been adopted into in order to attempt to extract knowledge from these images, enhancing the retrieval performance. A KB framework is presented to promote semi-automatic annotation and semantic image retrieval using multimodal cues (visual features and text captions). In addition, a hierarchical structure for the KB allows metadata to be shared that supports multi-semantics (polysemy) for concepts. The framework builds up an effective knowledge base pertaining to a domain specific image collection, e.g. sports, and is able to disambiguate and assign high level semantics to ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. Local features are more useful than global features, e.g. colour, shape or texture, as they are invariant to image scale, orientation and camera angle. An innovative approach is proposed for the representation, annotation and retrieval of visual content using a hybrid technique based upon the use of an unstructured visual word and upon a (structured) hierarchical ontology KB model. The structural model facilitates the disambiguation of unstructured visual words and a more effective classification of visual content, compared to a vector space model, through exploiting local conceptual structures and their relationships. The key contributions of this framework in using local features for image representation include: first, a method to generate visual words using the semantic local adaptive clustering (SLAC) algorithm which takes term weight and spatial locations of keypoints into account. Consequently, the semantic information is preserved. Second a technique is used to detect the domain specific ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi a visual word model to resolve synonym (visual heterogeneity) and polysemy problems, is proposed. The experimental results show that this approach can discover semantically meaningful visual content descriptions and recognise specific events, e.g., sports events, depicted in images efficiently. Since discovering the semantics of an image is an extremely challenging problem, one promising approach to enhance visual content interpretation is to use any associated textual information that accompanies an image, as a cue to predict the meaning of an image, by transforming this textual information into a structured annotation for an image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct types of information representation and modality, there are some strong, invariant, implicit, connections between images and any accompanying text information. Semantic analysis of image captions can be used by image retrieval systems to retrieve selected images more precisely. To do this, a Natural Language Processing (NLP) is exploited firstly in order to extract concepts from image captions. Next, an ontology-based knowledge model is deployed in order to resolve natural language ambiguities. To deal with the accompanying text information, two methods to extract knowledge from textual information have been proposed. First, metadata can be extracted automatically from text captions and restructured with respect to a semantic model. Second, the use of LSI in relation to a domain-specific ontology-based knowledge model enables the combined framework to tolerate ambiguities and variations (incompleteness) of metadata. The use of the ontology-based knowledge model allows the system to find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the proposed framework significantly enhances image retrieval and leads to narrowing of the semantic gap between lower level machinederived and higher level human-understandable conceptualisation

Automated Analysis of Customer Contacts – a Fintech Based Case Study

Seoses infotehnoloogia arenguga tekib igapäevaselt enneolematu kogus andmeid, mille automaatne analüüsimine konkurentsieelise saavutamiseks on otsustava tähtsusega. Traditsioonilised andmekaeve meetodid on leidnud laialdaselt ärilisi rakendusi, kuid ei ole sobivad struktureerimata (näiteks tekstiliste) andmete puhul. Seevastu on valdav osa andmetest just struktureerimata kujul, mistõttu on iseäranis oluline luua lahendusi neist olulise teabe eraldamiseks. Käesolev magistritöö on praktilise loomuga ning selle eesmärk oli luua automatiseeritud tekstianalüüsi mudel, mida saab kasutada sissetulevate kliendipäringute efektiivseks prioriseerimiseks ning mõõtmiseks kasutades TransferWise Ltd. andmeid. Tulenevalt püstitatud eesmärgist teostas autor arvukalt eksperimente kasutades nii klassikalisi kui ka uudseid loomuliku keele töötluse meetodeid. Seejuures ei taganud antud ülesande puhul uudsed tehnoloogiad märgatavat paremust klassikaliste meetodite ees. Töö tulemusena valminud mudel on oluline nii ettevõttele kui ka selle klientidele – mudel võimaldab prioriseerida sissetulevaid päringuid vastavalt nende keerukusele ning pakilisusele, mis parandab kliendikogemust ning soodustab ettevõtte kasvu muutes operatsioonilisi protsesse efektiivsemaks. Peale praktilise väärtuse pakub käesolev töö ka ulatuslikku ülevaadet erinevatest loomuliku keele töötluse meetoditest, nende sobivusest ning nendega kaasnevatest võimalustest.The rapid development of information technologies has brought along abnormal amounts of data being generated on a daily basis and the need to automatically analyse it to gain a competitive advantage. Traditional data mining techniques have been efficiently applied in a variety of commercial applications, yet they are only applicable on structured data. However, an overwhelming amount of existing data is in an unstructured (e.g. textual) form, hence it is crucial for companies to build solutions to automatically extract useful information from it. Given master’s thesis is with a practical nature and its purpose was to implement an automated text analysis model using data from TransferWise Ltd. that can be used to efficiently prioritise and measure incoming customer contacts. To achieve this, the author conducted numerous experiments via employing classical as well as novel natural language processing techniques. Apropos, employing novel methods did not ensure a noticeably better outcome. The established model is important for both the company as well as its customers since it can be used to prioritise incoming contacts based on their complexity or urgency. This ensures a convenient customer experience and is likely to accelerate growth by making operational procedures more efficient. Besides its practical value, given thesis also provides an extensive comparison of numerous natural language processing techniques, their suitability and opportunities