Information Retrieval, as a field, has long subscribed to an orthodox evaluation approach known as the Cranfield paradigm. This approach and the assumptions that underpin it have been essential to building the traditional search engine infrastructure that drives today’s modern information economy. In order to build the information economy of tomorrow, however, we must be prepared to reexamine these assumptions and create new, more sophisticated standards of evaluation to match the more complex information retrieval systems on the horizon. In this thesis, we begin this introspective process and launch our own evaluation method for one of these complex IR systems, entity-relationship recommenders. We will begin building a new user model adapted to the needs of a different user experience. To support these endeavors, we will also conduct a study with a mockup of our complex system to collect real behavior data and evaluation results. By the end of this work, we shall present a new evaluative approach for one kind of entity-relationship system and point the way for other advanced systems to come.

Stuttgart, Univ., Fakultät Informatik, Diplomarb., 1998.

In text mining, being able to recognize and extract named entities, e.g. Locations, Persons, Organizations, is very useful in many applications. This is usually referred to named entity recognition (NER). This thesis presents a cascaded framework for extracting named entities from text documents. We automatically derive features on a set of documents from different feature templates. To avoid high computational cost incurred by a single-phase approach, we divide the named entity extraction task into a segmentation task and a classification task, reducing the computational cost by an order of magnitude. / To handle cascaded errors that often occur in a sequence of tasks, we investigate and develop three models: maximum-entropy margin-based (MEMB) model, isomeric conditional random field (ICRF) model, and online cascaded reranking (OCR) model. MEMB model makes use of the concept of margin in maximizing log-likelihood. Parameters are trained in a way that they can maximize the "margin" between the decision boundary and the nearest training data points. ICRF model makes use of the concept of joint training. Instead of training each model independently, we design the segmentation and classification models in a way that they can be efficiently trained together under a soft constraint. OCR model is developed by using an online training method to maximize a margin without considering any probability measures, which greatly reduces the training time. It reranks all of the possible outputs from a previous stage based on a total output score. The best output with the highest total score is the final output. / We report experimental evaluations on the GENIA Corpus available from the BioNLP/NLPBA (2004) shared task and the Reuters Corpus available from the CoNLL-2003 shared tasks, which demonstrate the state-of-the-art performance achieved by the proposed models. / Chan, Shing Kit. / Advisers: Wai Lam; Kai Pui Lam. / Source: Dissertation Abstracts International, Volume: 73-06, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 126-133). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Thesis (M. Phil.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

published_or_final_version / abstract / Computer Science / Master / Master of Philosophy

Nowadays, an increasing amount of media platforms provide the users with opportunities for sharing their opinions about products, companies or people. In order to support users accessing opinion-based information, and to support engineers building systems that require opinion-aware reasoning, intelligent opinion-aware tools and techniques are needed. This thesis contributes methods and technology for opinion-aware information management from two different perspectives, namely document summarisation and knowledge representation. Document summarisation has been widely investigated as a mean to reduce information overload. This thesis focuses on statistical models for summarisation, with a particular attention to divergence-based models, within the context of opinions. Firstly, topic-based document summarisation is addressed, contributing a study on divergence-based document to summary similarity and the definition of a novel algorithm for summarisation based on sentence removal. Secondly, summarisation models are tailored to opinion-oriented content and shown to be useful also when exploited for different tasks such as sentiment classification. Thirdly, summarisation models are applied to knowledge-oriented data, in order to tackle tasks such as entity summarisation. The comprehensive task addressed is the knowledge-based opinion-aware summarisation of content (free text, facts). This thesis also contributes a broad discussion on knowledge representation of opinions. A thorough study on how to model opinions using traditional techniques, such as Entity-Relationship (ER) modelling, underlines that a high-level, opinion-aware layer of conceptual modelling is useful since it hides away implementation details. A conceptual and logical knowledge representation methodology for modelling opinions is hence proposed, with the purpose of guiding engineers towards the use of best practices during the development of sentiment analysis applications. Specifically, an extension of the traditional ER modelling and the definition of an automatic mapping procedure, to translate opinion-aware components of the conceptual model into a relational model, help achieving a clear separation between conceptual and logical modelling. The mapping procedure yields an automatic and replicable methodology to design applications which require opinion-aware reasoning.

Zugl.: München, Univ., Diss., 2008

Thesis (M.Sc.)--University of Alberta, 2010. / Title from PDF file main screen (viewed on Apr. 16, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta. Includes bibliographical references.

Zugl.: Magdeburg, Universiẗat, Habil.-Schr., 2005.

Technische University, Diss., 2000--Cottbus. / Ep/St.