46 research outputs found
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Event-based hyperspace analogue to language for query expansion
Bag-of-words approaches to information retrieval (IR) are effective but assume independence between words. The Hyperspace Analogue to Language (HAL) is a cognitively motivated and validated semantic space model that captures statistical dependencies between words by considering their co-occurrences in a surrounding window of text. HAL has been successfully applied to query expansion in IR, but has several limitations, including high processing cost and use of distributional statistics that do not exploit syntax. In this paper, we pursue two methods for incorporating syntactic-semantic information from textual ‘events’ into HAL. We build the HAL space directly from events to investigate whether processing costs can be reduced through more careful definition of word co-occurrence, and improve the quality of the pseudo-relevance feedback by applying event information as a constraint during HAL construction. Both methods significantly improve performance results in comparison with original HAL, and interpolation of HAL and relevance model expansion outperforms either method alone
Parameterized Neural Network Language Models for Information Retrieval
Information Retrieval (IR) models need to deal with two difficult issues,
vocabulary mismatch and term dependencies. Vocabulary mismatch corresponds to
the difficulty of retrieving relevant documents that do not contain exact query
terms but semantically related terms. Term dependencies refers to the need of
considering the relationship between the words of the query when estimating the
relevance of a document. A multitude of solutions has been proposed to solve
each of these two problems, but no principled model solve both. In parallel, in
the last few years, language models based on neural networks have been used to
cope with complex natural language processing tasks like emotion and paraphrase
detection. Although they present good abilities to cope with both term
dependencies and vocabulary mismatch problems, thanks to the distributed
representation of words they are based upon, such models could not be used
readily in IR, where the estimation of one language model per document (or
query) is required. This is both computationally unfeasible and prone to
over-fitting. Based on a recent work that proposed to learn a generic language
model that can be modified through a set of document-specific parameters, we
explore use of new neural network models that are adapted to ad-hoc IR tasks.
Within the language model IR framework, we propose and study the use of a
generic language model as well as a document-specific language model. Both can
be used as a smoothing component, but the latter is more adapted to the
document at hand and has the potential of being used as a full document
language model. We experiment with such models and analyze their results on
TREC-1 to 8 datasets
Analisis Strategi Komisi Penyiaran Indonesia Konten Televisi Edisi Ramadan
This study intends to illustrate how tv settings were in the period. The tests used in this study are subjective enlightenment methodologies, information collected as encounters, perceptions, documentation and investigation of specific information using information reduction (reduced information), information (display) and decision making (end of information). The consequences of this study must be seen from the depiction through perception, documentation and meetings with respondents, the approach taken by the Indonesian Broadcasting Commission on TV content during the long stretch of Ramadan
Users and Assessors in the Context of INEX: Are Relevance Dimensions Relevant?
The main aspects of XML retrieval are identified by analysing and comparing
the following two behaviours: the behaviour of the assessor when judging the
relevance of returned document components; and the behaviour of users when
interacting with components of XML documents. We argue that the two INEX
relevance dimensions, Exhaustivity and Specificity, are not orthogonal
dimensions; indeed, an empirical analysis of each dimension reveals that the
grades of the two dimensions are correlated to each other. By analysing the
level of agreement between the assessor and the users, we aim at identifying
the best units of retrieval. The results of our analysis show that the highest
level of agreement is on highly relevant and on non-relevant document
components, suggesting that only the end points of the INEX 10-point relevance
scale are perceived in the same way by both the assessor and the users. We
propose a new definition of relevance for XML retrieval and argue that its
corresponding relevance scale would be a better choice for INEX
Relevance Search via Bipolar Label Diffusion on Bipartite Graphs
The task of relevance search is to find relevant items to some given queries which can be viewed either as an information retrieval problem or as a semi-supervised learning problem In order to combine both of their advantages we develop a new relevance search method using label diffusion on bipartite graphs And we propose a heat diffusion-based algorithm namely bipartite label diffusion BLD Our method yields encouraging experimental results on a number of relevance search problem