Making a Better Query: Find Good Feedback Documents and Terms via Semantic Associations

When people search, they always input several keywords as an input query. While current information retrieval (IR) systems are based on term matching, documents will not be considered as relevant if they do not have the exact terms as in the query. However, it is common that these documents are relevant if they contain terms semantically similar to the query. To retrieve these documents, a classic way is to expand the original query with more related terms. Pseudo relevance feedback (PRF) has proven to be effective to expand origin queries and improve the performance of IR. It assumes the top k ranked documents obtained through the first round retrieval are relevant as feedback documents, and expand the original queries with feedback terms selected from these feedback documents. However, applying PRF for query expansion must be very carefully. Wrongly added terms can bring noisy information and hurt the overall search experiences extensively. The assumption of feedback documents is too strong to be completely true. To avoid noise import and make significant improvements simultaneously, we solve the significant problem through four ways in this dissertation. Firstly, we assume the proximity information among terms as term semantic associations and utilize them to seek new relevant terms. Next, to obtain good and robust performance for PRF via adapting topic information, we propose a new concept named topic space and present three models based on it. Topics obtained through topic modeling do help identify how relevant a feedback document is. Weights of candidate terms in these more relevant feedback documents will be boosted and have higher probabilities to be chosen. Furthermore, we apply machine learning methods to classify which feedback documents are effective for PRF. To solve the problem of lack-of-training-data for the application of machine learning methods in PRF, we improve a traditional co-training method and take the quality of classifiers into account. Finally, we present a new probabilistic framework to integrate existing effective methods like semantic associations as components for further research. All the work has been tested on public datasets and proven to be effective and efficient

Relevance-based language models : new estimations and applications

[Abstratc] Relevance-Based Language Models introduced in the Language Modelling framework the concept of relevance, which is explicit in other retrieval models such as the Probabilistic models. Relevance Models have been mainly used for a specific task within Information Retrieval called Pseudo-Relevance Feedback, a kind of local query expansion technique where relevance is assumed over a top of documents from the initial retrieval and where those documents are used to select expansion terms for the original query and produce a, hopefully more effective, second retrieval. In this thesis we investigate some new estimations for Relevance Models for both Pseudo-Relevance Feedback and other tasks beyond retrieval, particularly, constrained text clustering and item recommendation in Recommender Systems. We study the benefits of our proposals for those tasks in comparison with existing estimations. This new modellings are able not only to improve the effectiveness of the existing estimations and methods but also to outperform their robustness, a critical factor when dealing with Pseudo-Relevance Feedback methods. These objectives are pursued by different means: promoting divergent terms in the estimation of the Relevance Models, presenting new cluster-based retrieval models, introducing new methods for automatically determine the size of the pseudo-relevant set on a query-basis, and originally producing new modellings under the Relevance-Based Language Modelling framework for the constrained text clustering and the item recommendation problems

Toward higher effectiveness for recall-oriented information retrieval: A patent retrieval case study

Research in information retrieval (IR) has largely been directed towards tasks requiring high precision. Recently, other IR applications which can be described as recall-oriented IR tasks have received increased attention in the IR research domain. Prominent among these IR applications are patent search and legal search, where users are typically ready to check hundreds or possibly thousands of documents in order to find any possible relevant document. The main concerns in this kind of application are very different from those in standard precision-oriented IR tasks, where users tend to be focused on finding an answer to their information need that can typically be addressed by one or two relevant documents. For precision-oriented tasks, mean average precision continues to be used as the primary evaluation metric for almost all IR applications. For recall-oriented IR applications the nature of the search task, including objectives, users, queries, and document collections, is different from that of standard precision-oriented search tasks. In this research study, two dimensions in IR are explored for the recall-oriented patent search task. The study includes IR system evaluation and multilingual IR for patent search. In each of these dimensions, current IR techniques are studied and novel techniques developed especially for this kind of recall-oriented IR application are proposed and investigated experimentally in the context of patent retrieval. The techniques developed in this thesis provide a significant contribution toward evaluating the effectiveness of recall-oriented IR in general and particularly patent search, and improving the efficiency of multilingual search for this kind of task

Canadian Audiovisual Archives: The Politics of Preservation and Access

In 2005, in the spirit of Canadas total archives philosophy, the Western University Archives in London, Ontario acquired over ninety regional films on 8mm. Archival staff digitized the films in a Do-It-Yourself (DIY) fashion: they were simply repaired, projected, and captured off the wall with a digital camera. The raw files were then processed and given basic titling before being exported onto DVDs for public and institutional sale. While digitization was quite rudimentary, the public has access to a forgotten regional history. This dissertation analyzes the tensions and politics of audiovisual acquisition, preservation, and dissemination by recounting steps taken by DIY archivists to bring films from a personal archive to an institutional archive. I trace this collection of amateur itinerant films as they move from the filmmakers home in Dundee, New York, to the Western Archives. Reverend Leroy (Roy) Massecar (1918-2003) was a Baptist Minister and itinerant filmmaker who between 1947-1949 visited over ninety towns throughout Central and Southwestern Ontario, documenting daily life, screening films in these towns as Stars of the Town See Yourself and Your Friends on the Screen! and capturing the fleeting energy of small town rural Ontario. The dissertation mobilizes what Canadian archivist Terry Cook calls, archival contextual knowledge, a history from the bottom-up, and uses this case study to highlight larger issues facing Canadian audiovisual collections in the early 21st century: the shifting value in antiquated audiovisual formats and marginal film collections; the tension between professional preservation and public access; the hidden labour of audiovisual archivists; and the politics of DIY audiovisual discourse. I make the labour and bureaucracy of traditional archives visible by examining the discourses of the Archive not only within a theoretical space, but also in actual archive spaces whether physical or digital. I argue that bringing transparency to the roles and actions of donors, artists, archivists, scholars, and the public will allow for the larger ecology of Canadian audiovisual preservation to be activated, allowing actors in each point of the cycle to collectively move towards a holistic and networked audiovisual preservation strategy

A Right to Access Implies A Right to Know: An Open Online Platform for Research on the Readability of Law

The widespread availability of legal materials online has opened the law to a new and greatly expanded readership. These new readers need the law to be readable by them when they encounter it. However, the available empirical research supports a conclusion that legislation is difficult to read if not incomprehensible to most citizens. We review approaches that have been used to measure the readability of text including readability metrics, cloze testing and application of machine learning. We report the creation and testing of an open online platform for readability research. This platform is made available to researchers interested in undertaking research on the readability of legal materials. To demonstrate the capabilities ofthe platform, we report its initial application to a corpus of legislation. Linguistic characteristics are extracted using the platform and then used as input features for machine learning using the Weka package. Wide divergences are found between sentences in a corpus of legislation and those in a corpus of graded reading material or in the Brown corpus (a balanced corpus of English written genres). Readability metrics are found to be of little value in classifying sentences by grade reading level (noting that such metrics were not designed to be used with isolated sentences)

An investigation into weighted data fusion for content-based multimedia information retrieval

Content Based Multimedia Information Retrieval (CBMIR) is characterised by the combination of noisy sources of information which, in unison, are able to achieve strong performance. In this thesis we focus on the combination of ranked results from the independent retrieval experts which comprise a CBMIR system through linearly weighted data fusion. The independent retrieval experts are low-level multimedia features, each of which contains an indexing function and ranking algorithm. This thesis is comprised of two halves. In the first half, we perform a rigorous empirical investigation into the factors which impact upon performance in linearly weighted data fusion. In the second half, we leverage these finding to create a new class of weight generation algorithms for data fusion which are capable of determining weights at query-time, such that the weights are topic dependent

Adaptive hypertext and hypermedia : workshop : proceedings, 3rd, Sonthofen, Germany, July 14, 2001 and Aarhus, Denmark, August 15, 2001

This paper presents two empirical usability studies based on techniques from Human-Computer Interaction (HeI) and software engineering, which were used to elicit requirements for the design of a hypertext generation system. Here we will discuss the findings of these studies, which were used to motivate the choice of adaptivity techniques. The results showed dependencies between different ways to adapt the explanation content and the document length and formatting. Therefore, the system's architecture had to be modified to cope with this requirement. In addition, the system had to be made adaptable, in addition to being adaptive, in order to satisfy the elicited users' preferences

Adaptive hypertext and hypermedia : workshop : proceedings, 3rd, Sonthofen, Germany, July 14, 2001 and Aarhus, Denmark, August 15, 2001

This paper presents two empirical usability studies based on techniques from Human-Computer Interaction (HeI) and software engineering, which were used to elicit requirements for the design of a hypertext generation system. Here we will discuss the findings of these studies, which were used to motivate the choice of adaptivity techniques. The results showed dependencies between different ways to adapt the explanation content and the document length and formatting. Therefore, the system's architecture had to be modified to cope with this requirement. In addition, the system had to be made adaptable, in addition to being adaptive, in order to satisfy the elicited users' preferences