1,480 research outputs found
A Re-Ranking Method Based on Irrelevant Documents in Ad-Hoc Retrieval
In this paper, we propose a novel approach for document re-ranking, which relies on the concept of negative feedback represented by irrelevant documents. In a previous paper, a pseudo-relevance feedback method is introduced using an absorbing document ~d which best fits the user\u27s need. The document ~d is orthogonal to
the majority of irrelevant documents. In this paper, this document is used to re-rank the initial set of
ranked documents in Ad-hoc retrieval. The evaluation carried out on a standard document collection shows
the effectiveness of the proposed approach
Looking at Vector Space and Language Models for IR using Density Matrices
In this work, we conduct a joint analysis of both Vector Space and Language
Models for IR using the mathematical framework of Quantum Theory. We shed light
on how both models allocate the space of density matrices. A density matrix is
shown to be a general representational tool capable of leveraging capabilities
of both VSM and LM representations thus paving the way for a new generation of
retrieval models. We analyze the possible implications suggested by our
findings.Comment: In Proceedings of Quantum Interaction 201
On the probabilistic logical modelling of quantum and geometrically-inspired IR
Information Retrieval approaches can mostly be classed into probabilistic, geometric or logic-based. Recently, a new unifying framework for IR has emerged that integrates a probabilistic description within a geometric framework, namely vectors in Hilbert spaces. The geometric model leads naturally to a predicate logic over linear subspaces, also known as quantum logic. In this paper we show the relation between this model and classic concepts such as the Generalised Vector Space Model, highlighting similarities and differences. We also show how some fundamental components of quantum-based IR can be modelled in a descriptive way using a well-established tool, i.e. Probabilistic Datalog
Exploring a Multidimensional Representation of Documents and Queries (extended version)
In Information Retrieval (IR), whether implicitly or explicitly, queries and
documents are often represented as vectors. However, it may be more beneficial
to consider documents and/or queries as multidimensional objects. Our belief is
this would allow building "truly" interactive IR systems, i.e., where
interaction is fully incorporated in the IR framework.
The probabilistic formalism of quantum physics represents events and
densities as multidimensional objects. This paper presents our first step
towards building an interactive IR framework upon this formalism, by stating
how the first interaction of the retrieval process, when the user types a
query, can be formalised. Our framework depends on a number of parameters
affecting the final document ranking. In this paper we experimentally
investigate the effect of these parameters, showing that the proposed
representation of documents and queries as multidimensional objects can compete
with standard approaches, with the additional prospect to be applied to
interactive retrieval
A literature survey of methods for analysis of subjective language
Subjective language is used to express attitudes and opinions towards things, ideas and people. While content and topic centred natural language processing is now part of everyday life, analysis of subjective aspects of natural language have until recently been largely neglected by the research community. The explosive growth of personal blogs, consumer opinion sites and social network applications in the last years, have however created increased interest in subjective language analysis. This paper provides an overview of recent research conducted in the area
Linear Operators in Information Retrieval
In this paper, we propose a pseudo-relevance feedback approach based on linear operators: vector space basis change and cross product. The aim of pseudo-relevance feedback methods based on vector space basis change IBM (Ideal Basis Method) is to optimally separate relevant and irrelevant documents. Whereas the aim of pseudo-relevance feedback method based on cross product AI (Absorption of irrelevance) is to effectively exploit irrelevant documents. We show how to combine IBM methods with AI methods. The combination methods IBM+AI are evaluated experimentally on two TREC collections (TREC-7 ad hoc and TREC-8 ad hoc). The experiments show that these methods improve previous works
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