The troubles with using a logical model of IR on a large collection of documents

This is a paper of two halves. First, a description of a logical model of IR known as imaging will be presented. Unfortunately due to constraints of time and computing resource this model was not implemented in time for this round of TREC. Therefore this paper's second half describes the more conventional IR model and system used to generate the Glasgow IR result set (glair1)

The troubles with using a logical model of IR on a large collection of documents

This is a paper of two halves. First, a description of a logical model of IR known as imaging will be presented. Unfortunately due to constraints of time and computing resource this model was not implemented in time for this round of TREC. Therefore this paper’s second half describes the more conventional IR model and system used to generate the Glasgow IR result set (glair1)

Distributional lexical semantics: toward uniform representation paradigms for advanced acquisition and processing tasks

The distributional hypothesis states that words with similar distributional properties have similar semantic properties (Harris 1968). This perspective on word semantics, was early discussed in linguistics (Firth 1957; Harris 1968), and then successfully applied to Information Retrieval (Salton, Wong and Yang 1975). In Information Retrieval, distributional notions (e.g. document frequency and word co-occurrence counts) have proved a key factor of success, as opposed to early logic-based approaches to relevance modeling (van Rijsbergen 1986; Chiaramella and Chevallet 1992; van Rijsbergen and Lalmas 1996).</jats:p

A New Lattice-Based Information Retrieval Theory

Logic-based Information Retrieval (IR) models represent the retrieval decision as an implication d → q between a document d and a query q, where d and q are logical sentences. However, d → q is a bi- nary decision, we thus need a measure to estimate the degree to which d implies q, noted P(d → q). The main problems in the logic-based IR models are the difficulties to implement the decision algorithms and to define the uncertainty measure P as a part of the logic. In this study, we chose the Propositional Logic (PL) as the underlying framework. We propose to replace the implication d → q by the material implication d ⊃ q. However, we know that there is a mapping between PL and the lattice theory. In addition, Knuth [13] introduced the notion of degree of inclusion to quantify the ordering relations defined on lattices. There- fore, we position documents and queries on a lattice, where the ordering relation is equivalent to the material implication. In this case, the impli- cation d → q is replaced by an ordering relation between documents and queries, and the uncertainty P(d → q) is redefined using the degree of inclusion measure. This new IR model is: 1- general where it is possible to instantiate most of classical IR models depending on our lattice-based model, 2- capable to formally prove the intuition of Rijsbergen about replacing P (d → q) by P (q|d), and 3- easy to implement

Towards a belief revision based adaptive and context sensitive information retrieval system

In an adaptive information retrieval (IR) setting, the information seekers' beliefs about which terms are relevant or nonrelevant will naturally fluctuate. This article investigates how the theory of belief revision can be used to model adaptive IR. More specifically, belief revision logic provides a rich representation scheme to formalize retrieval contexts so as to disambiguate vague user queries. In addition, belief revision theory underpins the development of an effective mechanism to revise user profiles in accordance with information seekers' changing information needs. It is argued that information retrieval contexts can be extracted by means of the information-flow text mining method so as to realize a highly autonomous adaptive IR system. The extra bonus of a belief-based IR model is that its retrieval behavior is more predictable and explanatory. Our initial experiments show that the belief-based adaptive IR system is as effective as a classical adaptive IR system. To our best knowledge, this is the first successful implementation and evaluation of a logic-based adaptive IR model which can efficiently process large IR collections

Theoretical evaluation of XML retrieval

This thesis develops a theoretical framework to evaluate XML retrieval. XML retrieval deals with retrieving those document parts that specifically answer a query. It is concerned with using the document structure to improve the retrieval of information from documents by only delivering those parts of a document an information need is about. We define a theoretical evaluation methodology based on the idea of `aboutness' and apply it to XML retrieval models. Situation Theory is used to express the aboutness proprieties of XML retrieval models. We develop a dedicated methodology for the evaluation of XML retrieval and apply this methodology to five XML retrieval models and other XML retrieval topics such as evaluation methodologies, filters and experimental results