Identification of Informativeness in Text using Natural Language Stylometry

In this age of information overload, one experiences a rapidly growing over-abundance of written text. To assist with handling this bounty, this plethora of texts is now widely used to develop and optimize statistical natural language processing (NLP) systems. Surprisingly, the use of more fragments of text to train these statistical NLP systems may not necessarily lead to improved performance. We hypothesize that those fragments that help the most with training are those that contain the desired information. Therefore, determining informativeness in text has become a central issue in our view of NLP. Recent developments in this field have spawned a number of solutions to identify informativeness in text. Nevertheless, a shortfall of most of these solutions is their dependency on the genre and domain of the text. In addition, most of them are not efficient regardless of the natural language processing problem areas. Therefore, we attempt to provide a more general solution to this NLP problem. This thesis takes a different approach to this problem by considering the underlying theme of a linguistic theory known as the Code Quantity Principle. This theory suggests that humans codify information in text so that readers can retrieve this information more efficiently. During the codification process, humans usually change elements of their writing ranging from characters to sentences. Examples of such elements are the use of simple words, complex words, function words, content words, syllables, and so on. This theory suggests that these elements have reasonable discriminating strength and can play a key role in distinguishing informativeness in natural language text. In another vein, Stylometry is a modern method to analyze literary style and deals largely with the aforementioned elements of writing. With this as background, we model text using a set of stylometric attributes to characterize variations in writing style present in it. We explore their effectiveness to determine informativeness in text. To the best of our knowledge, this is the first use of stylometric attributes to determine informativeness in statistical NLP. In doing so, we use texts of different genres, viz., scientific papers, technical reports, emails and newspaper articles, that are selected from assorted domains like agriculture, physics, and biomedical science. The variety of NLP systems that have benefitted from incorporating these stylometric attributes somewhere in their computational realm dealing with this set of multifarious texts suggests that these attributes can be regarded as an effective solution to identify informativeness in text. In addition to the variety of text genres and domains, the potential of stylometric attributes is also explored in some NLP application areas---including biomedical relation mining, automatic keyphrase indexing, spam classification, and text summarization---where performance improvement is both important and challenging. The success of the attributes in all these areas further highlights their usefulness

Application of Big Data Technology, Text Classification, and Azure Machine Learning for Financial Risk Management Using Data Science Methodology

Data science plays a crucial role in enabling organizations to optimize data-driven opportunities within financial risk management. It involves identifying, assessing, and mitigating risks, ultimately safeguarding investments, reducing uncertainty, ensuring regulatory compliance, enhancing decision-making, and fostering long-term sustainability. This thesis explores three facets of Data Science projects: enhancing customer understanding, fraud prevention, and predictive analysis, with the goal of improving existing tools and enabling more informed decision-making. The first project examined leveraged big data technologies, such as Hadoop and Spark, to enhance financial risk management by accurately predicting loan defaulters and their repayment likelihood. In the second project, we investigated risk assessment and fraud prevention within the financial sector, where Natural Language Processing and machine learning techniques were applied to classify emails into categories like spam, ham, and phishing. After training various models, their performance was rigorously evaluated. In the third project, we explored the utilization of Azure machine learning to identify loan defaulters, emphasizing the comparison of different machine learning algorithms for predictive analysis. The results aimed to determine the best-performing model by evaluating various performance metrics for the dataset. This study is important because it offers a strategy for enhancing risk management, preventing fraud, and encouraging innovation in the financial industry, ultimately resulting in better financial outcomes and enhanced customer protection

Probing the topological properties of complex networks modeling short written texts

In recent years, graph theory has been widely employed to probe several language properties. More specifically, the so-called word adjacency model has been proven useful for tackling several practical problems, especially those relying on textual stylistic analysis. The most common approach to treat texts as networks has simply considered either large pieces of texts or entire books. This approach has certainly worked well -- many informative discoveries have been made this way -- but it raises an uncomfortable question: could there be important topological patterns in small pieces of texts? To address this problem, the topological properties of subtexts sampled from entire books was probed. Statistical analyzes performed on a dataset comprising 50 novels revealed that most of the traditional topological measurements are stable for short subtexts. When the performance of the authorship recognition task was analyzed, it was found that a proper sampling yields a discriminability similar to the one found with full texts. Surprisingly, the support vector machine classification based on the characterization of short texts outperformed the one performed with entire books. These findings suggest that a local topological analysis of large documents might improve its global characterization. Most importantly, it was verified, as a proof of principle, that short texts can be analyzed with the methods and concepts of complex networks. As a consequence, the techniques described here can be extended in a straightforward fashion to analyze texts as time-varying complex networks

Detecting Deception in Text: A Corpus-Driven Approach

Deception is a pervasive psycholinguistic phenomenon---from lies during legal trials to fabricated online reviews. Its identification has been studied for centuries---from the ancient Chinese method of spitting dry rice to the modern polygraph. The recent proliferation of deceptive online reviews has increased the need for automatic deception filtering systems. Although human performance is in general at chance, previous research suggests that the linguistic signals resulting from conscious deception are sufficient for building automatic systems capable of distinguishing deceptive documents from truthful ones. Our interest is in identifying the invariant traits of deception in text, and we argue that these encouraging results in automatic deception detection are mainly due to the side effects of corpus-specific features. This poses no harm to practical applications, but it does not foster a deeper investigation of deception. To demonstrate this and to allow researchers and practitioners to share results, we have developed the largest publicly available shared multidimensional deception corpus for online reviews, the BLT-C (Boulder Lies and Truths Corpus). In an attempt to overcome the inherent lack of ground truth, we have also developed a set of semi-automatic techniques to ensure corpus validity. This thesis shows that detecting deception using supervised machine learning methods is brittle. Experiments conducted using this corpus show that accuracy changes across different kinds of deception (e.g., lying vs. fabrication) and text content dimensions (e.g., sentiment), demonstrating the limitations of previous studies. Preliminary results confirm statistical separation between fabricated and truthful reviews (although not as large as in other studies), but we do not observe any separation between truths and lies, which suggests that lying is a much more difficult class of deception to identify than fabricated spam reviews

Analyzing User Comments On YouTube Coding Tutorial Videos

Video coding tutorials enable expert and novice programmers to visually observe real developers write, debug, and execute code. Previous research in this domain has focused on helping programmers find relevant content in coding tutorial videos as well as understanding the motivation and needs of content creators. In this thesis, we focus on the link connecting programmers creating coding videos with their audience. More specifically, we analyze user comments on YouTube coding tutorial videos. Our main objective is to help content creators to effectively understand the needs and concerns of their viewers, thus respond faster to these concerns and deliver higher-quality content. A dataset of 6000 comments sampled from 12 YouTube coding videos is used to conduct our analysis. Important user questions and concerns are then automatically classified and summarized. The results show that Support Vector Machines can detect useful viewers\u27 comments on coding videos with an average accuracy of 77%. The results also show that SumBasic, an extractive frequency-based summarization technique with redundancy control, can sufficiently capture the main concerns present in viewers\u27 comments

Enrichment of Wind Turbine Health History for Condition-Based Maintenance

This research develops a methodology for and shows the benefit of linking records of wind turbine maintenance. It analyses commercially sensitive real-world maintenance records with the aim of improving the productivity of offshore wind farms. The novel achievements of this research are that it applies multi-feature record linkage techniques to maintenance data, that it applies statistical techniques for the interval estimation of a binomial proportion to record linkage techniques and that it estimates the distribution of the coverage error of statistical techniques for the interval estimation of a binomial proportion. The main contribution of this research is a process for the enrichment of offshore wind turbine health history. The economic productivity of a wind farm depends on the price of electricity and on the suitability of the weather, both of which are beyond the control of a maintenance team, but also on the cost of operating the wind farm, on the cost of maintaining the wind turbines and on how much of the wind farm’s potential production of electricity is lost to outages. Improvements in maintenance scheduling, in condition-based maintenance, in troubleshooting and in the measurement of maintenance effectiveness all require knowledge of the health history of the plant. To this end, this thesis presents new techniques for linking together existing records of offshore wind turbine health history. Multi-feature record linkage techniques are used to link records of maintenance data together. Both the quality of record linkage and the uncertainty of that quality are assessed. The quality of record linkage was measured by comparing the generated set of linked records to a gold standard set of linked records identified in collaboration with offshore wind turbine maintenance experts. The process for the enrichment of offshore wind turbine health history developed in this research requires a vector of weights and thresholds. The agreement and disagreement weights for each feature indicate the importance of the feature to the quality of record linkage. This research uses differential evolution to globally optimise this vector of weights and thresholds. There is inevitably some uncertainty associated with the measurement of the quality of record linkage, and consequently with the optimum values for the weights and thresholds; this research not only measures the quality of record linkage but also identifies robust techniques for the estimation of its uncertainty.

A real-time system for abusive network traffic detection

Abusive network traffic--to include unsolicited e-mail, malware propagation, and denial-of-service attacks--remains a constant problem in the Internet. Despite extensive research in, and subsequent deployment of, abusive-traffic detection infrastructure, none of the available techniques addresses the problem effectively or completely. The fundamental failing of existing methods is that spammers and attack perpetrators rapidly adapt to and circumvent new mitigation techniques. Analyzing network traffic by exploiting transport-layer characteristics can help remedy this and provide effective detection of abusive traffic. Within this framework, we develop a real-time, online system that integrates transport layer characteristics into the existing SpamAssasin tool for detecting unsolicited commercial e-mail (spam). Specifically, we implement the previously proposed, but undeveloped, SpamFlow technique. We determine appropriate algorithms based on classification performance, training required, adaptability, and computational load. We evaluate system performance in a virtual test bed and live environment and present analytical results. Finally, we evaluate our system in the context of Spam Assassin's auto-learning mode, providing an effective method to train the system without explicit user interaction or feedback.http://archive.org/details/arealtimesystemf109455754Outstanding ThesisApproved for public release; distribution is unlimited