4,191 research outputs found

    An investigation of term weighting approaches for microblog retrieval

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    The use of effective term frequency weighting and document length normalisation strategies have been shown over a number of decades to have a significant positive effect for document retrieval. When dealing with much shorter documents, such as those obtained from microblogs, it would seem intuitive that these would have less benefit. In this paper we investigate their effect on microblog retrieval performance using the Tweets2011 collection from the TREC 2011 Microblog Track

    Voting for candidates: adapting data fusion techniques for an expert search task

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    In an expert search task, the users' need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users' query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query. We see the problem of ranking experts as a voting problem, which we model by adapting eleven data fusion techniques.We investigate the effectiveness of the voting approach and the associated data fusion techniques across a range of document weighting models, in the context of the TREC 2005 Enterprise track. The evaluation results show that the voting paradigm is very effective, without using any collection specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the data fusion techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the adapted data fusion techniques for the proposed approach is stable regardless of the used weighting models

    A model for structured document retrieval : empirical investigations

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    Documents often display a structure, e.g., several sections, each with several subsections and so on. Taking into account the structure of a document allows the retrieval process to focus on those parts of the document that are most relevant to an information need. In previous work, we developed a model for the representation and the retrieval of structured documents. This paper reports the first experimental study of the effectiveness and applicability of the model

    TNO/UT at TREC-9: How different are Web documents?

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    Although at first sight, the web track might seem a copy of the ad hoc track, we discovered that some small adjustments had to be made to our systems to run the web evaluation. As we expected, the basic language model based IR model worked effectively on this data. Blind feedback methods however, seem less effective on web data. We also experimented with rescoring the documents based on several algorithms that exploit link information. These methods yielded no positive result

    Adaptive image retrieval using a graph model for semantic feature integration

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    The variety of features available to represent multimedia data constitutes a rich pool of information. However, the plethora of data poses a challenge in terms of feature selection and integration for effective retrieval. Moreover, to further improve effectiveness, the retrieval model should ideally incorporate context-dependent feature representations to allow for retrieval on a higher semantic level. In this paper we present a retrieval model and learning framework for the purpose of interactive information retrieval. We describe how semantic relations between multimedia objects based on user interaction can be learnt and then integrated with visual and textual features into a unified framework. The framework models both feature similarities and semantic relations in a single graph. Querying in this model is implemented using the theory of random walks. In addition, we present ideas to implement short-term learning from relevance feedback. Systematic experimental results validate the effectiveness of the proposed approach for image retrieval. However, the model is not restricted to the image domain and could easily be employed for retrieving multimedia data (and even a combination of different domains, eg images, audio and text documents)

    Hybrid XML Retrieval: Combining Information Retrieval and a Native XML Database

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    This paper investigates the impact of three approaches to XML retrieval: using Zettair, a full-text information retrieval system; using eXist, a native XML database; and using a hybrid system that takes full article answers from Zettair and uses eXist to extract elements from those articles. For the content-only topics, we undertake a preliminary analysis of the INEX 2003 relevance assessments in order to identify the types of highly relevant document components. Further analysis identifies two complementary sub-cases of relevance assessments ("General" and "Specific") and two categories of topics ("Broad" and "Narrow"). We develop a novel retrieval module that for a content-only topic utilises the information from the resulting answer list of a native XML database and dynamically determines the preferable units of retrieval, which we call "Coherent Retrieval Elements". The results of our experiments show that -- when each of the three systems is evaluated against different retrieval scenarios (such as different cases of relevance assessments, different topic categories and different choices of evaluation metrics) -- the XML retrieval systems exhibit varying behaviour and the best performance can be reached for different values of the retrieval parameters. In the case of INEX 2003 relevance assessments for the content-only topics, our newly developed hybrid XML retrieval system is substantially more effective than either Zettair or eXist, and yields a robust and a very effective XML retrieval.Comment: Postprint version. The editor version can be accessed through the DO
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