Named entity recognition using a new fuzzy support vector machine.

Recognizing and extracting exact name entities, like Persons, Locations, Organizations, Dates and Times are very useful to mining information from electronics resources and text. Learning to extract these types of data is called Named Entity Recognition(NER) task. Proper named entity recognition and extraction is important to solve most problems in hot research area such as Question Answering and Summarization Systems, Information Retrieval and Information Extraction, Machine Translation, Video Annotation, Semantic Web Search and Bioinformatics, especially Gene identification, proteins and DNAs names. Nowadays more researchers use three type of approaches namely, Rule-base NER, Machine Learning-base NER and Hybrid NER to identify names. Machine learning method is more famous and applicable than others, because it’s more portable and domain independent. Some of the Machine learning algorithms used in NER methods are, support vector machine(SVM), Hidden Markov Model, Maximum Entropy Model (MEM) and Decision Tree. In this paper, we review these methods and compare them based on precision in recognition and also portability using the Message Understanding Conference(MUC) named entity definition and its standard data set to find their strength and weakness of each these methods. We have improved the precision in NER from text using the new proposed method that calls FSVM for NER. In our method we have employed Support Vector Machine as one of the best machine learning algorithm for classification and we contribute a new fuzzy membership function thus removing the Support Vector Machine’s weakness points in NER precision and multi classification. The design of our method is a kind of One-Against-All multi classification technique to solve the traditional binary classifier in SVM

Information Extraction and Classification on Journal Papers

The importance of journals for diffusing the results of scientific research has increased considerably. In the digital era, Portable Document Format (PDF) became the established format of electronic journal articles. This structured form, combined with a regular and wide dissemination, spread scientific advancements easily and quickly. However, the rapidly increasing numbers of published scientific articles requires more time and effort on systematic literature reviews, searches and screens. The comprehension and extraction of useful information from the digital documents is also a challenging task, due to the complex structure of PDF. To help a soil science team from the United States Department of Agriculture (USDA) build a queryable journal paper system, we used web crawler to download articles on soil science from the digital library. We applied named entity recognition and table analysis to extract useful information including authors, journal name and type, publish date, abstract, DOI, experiment location in papers and highlight the paper characteristics in a computer queryable format in the system. Text classification is applied on to identify the parts of interest to the users and save their search time. We used traditional machine learning techniques including logistic regression, support vector machine, decision tree, naive bayes, k-nearest neighbors, random forest, ensemble modeling, and neural networks in text classification and compare the advantages of these approaches in the end. Advisor: Stephen D. Scot

Investigating Text Message Classification Using Case-based Reasoning

Text classification is the categorization of text into a predefined set of categories. Text classification is becoming increasingly important given the large volume of text stored electronically e.g. email, digital libraries and the World Wide Web (WWW). These documents represent a massive amount of information that can be accessed easily. To gain benefit from using this information requires organisation. One way of organising it automatically is to use text classification. A number of well known machine learning techniques have been used in text classification including Naïve Bayes, Support Vector Machines and Decision Trees, and the less commonly used are k-Nearest Neighbour, Neural Networks and Genetic Algorithms. One aspect of text classification is general message classification, the ability to correctly classify text messages containing text of different lengths. There are many applications that would benefit from this. An example of such applications are, personal emailing filtering, filtering email into different categories of business and personal email and spam email and email routing, e.g. routing email for a helpdesk, so that the email reaches the correct person. This thesis presents an investigation of applying a Case based Reasoning (CBR) approach to general text message classification. Case-based Reasoning was chosen as it was found to perform well for a particular type of message classification, spam filtering. CBR was found to have certain advantages over other machine learning techniques such as Naïve Bayes. It was able to handle the dynamic nature of spam better than other machine learning techniques and offered the ability for the training data to be easily updated continuously and to have new training data immediately available. The objective of this research is to extend previous work conducted on spam filtering to general message classification, which includes classifying short and long text messages into multiple categories. Short text message classification presents a particular challenge as the concept being learnt is weak. We investigated two types of similarity metrics used with CBR, feature based and featureless similarity metrics. We then compared CBR using both feature based and featureless similarity metrics with two well known machine learning techniques. Naïve Bayes (NB) and Support Vector machine (SVM). These two machine learning techniques serve as base line classifiers as they seem to be currently the classifier of choice in the text classification domain. The results of this search show that CBR using a featureless similarity metric achieves better performance than CBR using a feature base similarity metric. The results also show that when using CBR with a feature based similarity metric the classification task required different feature types and different feature representations, depending on the domain. We also investigated whether a case-base editing technique developed for spam case-bases improve the performance over unedited case-bases on different text domains. We found that the case-base editing technique used for spam filtering performs well for email based case-bases but not for other text domains of either short or long text messages

Sentiment classification from reviews for tourism analytics

User-generated content is critical for tourism destination management as it could help them identify their customers' opinions and come up with solutions to upgrade their tourism organizations as it could help them identify customer opinions. There are many reviews on social media and it is difficult for these organizations to analyse the reviews manually. By applying sentiment classification, reviews can be classified into several classes and help ease decision-making. The reviews contain noisy contents, such as typos and emoticons, which could affect the accuracy of the classifiers. This study evaluates the reviews using Support Vector Machine and Random Forest models to identify a suitable classifier. The main phases in this study are data collection, data preparation, data labelling and modelling phases. The reviews are labelled into three sentiments; positive, neutral, and negative. During pre-processing, steps such as removing the missing value, tokenization, case folding, stop words removal, stemming, and applying n-grams are performed. The result of this research is evaluated by looking at the performance of the models based on accuracy where the result with the highest accuracy is chosen as the solution. In this study, data is data from TripAdvisor and Google reviews using web scraping tools. The findings show that the Support Vector Machine model with 5-fold cross-validation the most suitable classifier with an accuracy of 67.97% compared to Naive Bayes with 61.33% accuracy and Random Forest classifier with 63.55% accuracy. In conclusion, the result of this paper could provide important information in tourism besides determining the suitable algorithm to be used for Sentiment Analysis related to the tourism domain

Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers

Machine Learning (ML) algorithms are used to train computers to perform a variety of complex tasks and improve with experience. Computers learn how to recognize patterns, make unintended decisions, or react to a dynamic environment. Certain trained machines may be more effective than others because they are based on more suitable ML algorithms or because they were trained through superior training sets. Although ML algorithms are known and publicly released, training sets may not be reasonably ascertainable and, indeed, may be guarded as trade secrets. While much research has been performed about the privacy of the elements of training sets, in this paper we focus our attention on ML classifiers and on the statistical information that can be unconsciously or maliciously revealed from them. We show that it is possible to infer unexpected but useful information from ML classifiers. In particular, we build a novel meta-classifier and train it to hack other classifiers, obtaining meaningful information about their training sets. This kind of information leakage can be exploited, for example, by a vendor to build more effective classifiers or to simply acquire trade secrets from a competitor's apparatus, potentially violating its intellectual property rights

SQL Injection Detection Using Machine Learning Techniques and Multiple Data Sources

SQL Injection continues to be one of the most damaging security exploits in terms of personal information exposure as well as monetary loss. Injection attacks are the number one vulnerability in the most recent OWASP Top 10 report, and the number of these attacks continues to increase. Traditional defense strategies often involve static, signature-based IDS (Intrusion Detection System) rules which are mostly effective only against previously observed attacks but not unknown, or zero-day, attacks. Much current research involves the use of machine learning techniques, which are able to detect unknown attacks, but depending on the algorithm can be costly in terms of performance. In addition, most current intrusion detection strategies involve collection of traffic coming into the web application either from a network device or from the web application host, while other strategies collect data from the database server logs. In this project, we are collecting traffic from two points: the web application host, and a Datiphy appliance node located between the webapp host and the associated MySQL database server. In our analysis of these two datasets, and another dataset that is correlated between the two, we have been able to demonstrate that accuracy obtained with the correlated dataset using algorithms such as rule-based and decision tree are nearly the same as those with a neural network algorithm, but with greatly improved performance