The novelty task consists of finding relevant and novel sentences in a ranking of documents given a query. In the literature, different techniques have been applied to address this problem. Nevertheless, little is known about Language Models for novelty detection and, especially, the effect of smoothing on the selection of novel sentences. Language Models can be used to study novelty and relevance in a principled way. These statistical models have been shown to perform well empirically in many Information Retrieval tasks. In this work we study formally the effects of smoothing on novelty detection. To this aim, we compare different techniques based on the Kullback-Leibler divergence and we analyze the sensitivity of retrieval performance to the smoothing parameters. The ability of Language Modeling estimation methods to handle quantitatively the uncertainty associated to the use of natural language is a powerful tool that can drive the future development of noveltybased mechanisms.

Abstract: A new event tracking approach is proposed based on the identification of named entity (NE) patterns such as Who, What, Where and When, and their relationship with news domains such as Politics, Economy, Government and Entertainment. This research comprises three parts. The first part uses a set of user studies to identify NE patterns and their relationship with news domains. Second part is to design a prototype system based on NE patterns. The final part evaluates the prototype event tracking system. This paper described the first part which is to evaluate the importance of NE across news domains. We have achieved a better understanding on NE patterns by identifying the distribution of NE across news domains.

Current Information Retrieval systems are often based on topicality. They estimate relevance by comparing the similarity between the user query and each document. These systems do not take into account important contextual information. More specifically, they do not often apply mechanisms to filter out redundant information. We interpret context here as the set of chunks of text from the ranked set of documents that the user has already seen. This is a valuable contextual information to guide the retrieval processes in a way that avoids redundancy. It is desirable that the ranking of results is composed by relevant but also novel material. This means that each document must provide to the user unseen information which is related to his need. In this work we study different novelty detection approaches that make good use of this contextual information. We show that these techniques can be applied effectively and efficiently at the sentence level. Symposium Themes: Context-aware retrieval models. 1.

2010 ‘This thesis is the result of the author’s original research. It has been composed by the author and has not been previously submitted for examination which has led to the award of a degree.’ 'The copyright of this thesis belongs to the author under the terms of the United Kingdom Copyright Acts as qualified by University of Strathclyde Regulation 3.50. Due acknowledgement must always be made of the use of any material contained in, or derived from, this thesis.’ Signed: Date: Acknowledgements My PhD is a challenging journey with wonderful experiences. Having two maternity leaves is one of it and I’m very lucky to be surrounded by truly lovely people. Early in the process of completing this project, it became quite clear to me that a researcher cannot complete a PhD thesis alone. Therefore I would like to thank the following persons for their dedication, prayers and support. I would like to express my deep and sincere gratitude to my supervisors, Professor Fabio Crestani and Professor Ian Ruthven. I am grateful to them for their commitment, the freedom they gave me to pursue my ideas, the encouragement they provided when I succeeded, the patience they demonstrated when I failed, the wide range of problems they exposed me to, and the direction they consistently provided. Some people wondered as to how I completed my PhD during my supervisor’s absence. Although Fabio is currently at University of Lugano, Switzerland, but I never felt like struggling alone. Thank you for being a great supervisor. I would also like to thank Dr. Crawford Revie for his useful comments and encouragement at my annual reviews. I would like to thank members of the i-lab group for making my journey a pleasant one.