Contextual retrieval of single Wikipedia articles to support the reading of academic abstracts

Google style search engines are currently some of the most popular tools that people use when they are looking for information. There are a variety of reasons that people can have for conducting a search, although, these reasons can generally be distilled down to users being engaged in a task and developing an information need that impedes them from completing that task at a level which is satisfactory to them. The Google style search engine, however, is not always the most appropriate tool for every user task. In this thesis, our approach to search differs from the traditional search engine as we focus on providing support to users who are reading academic abstracts. When people do not understand a passage in the abstract they are reading, they often look for more detailed information or a definition. Presenting them with a list of possibly relevant search results, as a Google style search would, may not immediately meet this information need. In the case of reading, it is logical to hypothesize that users would prefer to receive a single document containing the information that they need. Developed in this thesis are retrieval algorithms that use the abstract being read along with the passage that the user is interested in to retrieve a single highly related article from Wikipedia. The top performing algorithm from the experiments conducted in this thesis is able to retrieve an appropriate article 77% of the time. This algorithm was deployed in a prototype reading support tool. LiteraryMark, in order to investigate the usefulness of such a tool. The results from the user experiment conducted in this thesis indicate that LiteraryMark is able to significantly improve the understanding and confidence levels of people reading abstracts.