Document retrieval

About: Document retrieval is a research topic. Over the lifetime, 6821 publications have been published within this topic receiving 214383 citations.

Efficient passage ranking for document databases

Abstract: Queries to text collections are resolved by ranking the documents in the collection and returning the highest-scoring documents to the user. An alternative retrieval method is to rank passages, that is, short fragments of documents, a strategy that can improve effectiveness and identify relevant material in documents that are too large for users to consider as a whole. However, ranking of passages can considerably increase retrieval costs. In this article we explore alternative query evaluation techniques, and develop new tecnhiques for evaluating queries on passages. We show experimentally that, appropriately implemented, effective passage retrieval is practical in limited memory on a desktop machine. Compared to passage ranking with adaptations of current document ranking algorithms, our new “DO-TOS” passage-ranking algorithm requires only a fraction of the resources, at the cost of a small loss of effectiveness.

Variations on language modeling for information retrieval

Abstract: Search engine technology builds on theoretical and empirical research results in the area of information retrieval (IR). This dissertation makes a contribution to the field of language modeling (LM) for IR, which views both queries and documents as instances of a unigram language model and defines the matching function between a query and each document as the probability that the query terms are generated by the document language model. The work described is concerned with three research issues.

Subjective versus Objective Relevance in Bibliographic Retrieval Systems.

Abstract: The distinction between subjective and objective relevance of a document to a request, although heretofore given little explicit recognition, is of importance to information retrieval as well as to certain broader issues in librarianship. Objective relevance is crucial to the design and testing of bibliographic retrieval systems, while subjective relevance is paramount in the operation and use of such systems. The distinction provides a rationale for a trial-and-error mode of interaction with libraries, online databases, and other information systems. The concept of objective relevance introduced here is illuminated by earlier work on logical relevance by Patrick Wilson and by William Cooper.

Classification of document pages using structure-based features

Abstract: Searching for documents by their type or genre is a natural way to enhance the effectiveness of document retrieval. The layout of a document contains a significant amount of information that can be used to classify it by type in the absence of domain-specific models. Our approach to classification is based on “visual similarity” of layout structure and is implemented by building a supervised classifier, given examples of each class. We use image features such as percentages of text and non-text (graphics, images, tables, and rulings) content regions, column structures, relative point sizes of fonts, density of content area, and statistics of features of connected components which can be derived without class knowledge. In order to obtain class labels for training samples, we conducted a study where subjects ranked document pages with respect to their resemblance to representative page images. Class labels can also be assigned based on known document types, or can be defined by the user. We implemented our classification scheme using decision tree classifiers and self-organizing maps.