Automatic text-based explanation of events

With the abundance of publicly available information on the Web reflecting the ever-changing nature of world events, one question naturally comes to our mind—how can we explain a specific event using this vast amount of information? This enormous information source is freely available but its unstructured nature, and inherent noise demand sophisticated techniques to understand reasons behind any event. Modern day search engines are too general in nature. They only try to find documents which contain the query keyword(s) and supposedly most relevant, but this search method has an inherent limitation. At the end, they are just query matching engines. They do not have and do not require an understanding of the domain knowledge and specialized document processing techniques to provide an explanation of any event. By the word "event" we mean an occurrence to which we can associate a time, e.g."the IBM stock price went up in the third week of June, 2004", or "More than 1.5 million people lost electricity in Florida in September 2004". Newspapers or on-line news articles are full of events like these. Though these articles are important and useful information sources, they are noisy and unstructured compared to structural information sources such as relational databases. In this thesis, I propose a novel assembly of techniques which can be applied on unstructured information sources such as news articles, news reports, or other text-based documents. These techniques, applied in a step-by-step fashion, will permit proper analysis of the information source and can provide text-based explanations for major events that have occurred during a given time-frame. Given a noisy, unstructured, weekly-organized source of information, our method can reduce noise with high precision, sort them (according to specified attributes) with high accuracy, find relevance to the domain of concern with high precision, and at the end can show a text-based explanation (in the form of keywords, phrases or even sentences) for a particular event.