Search

In this article, we present a novel principled approach to passage-based (document) retrieval using fuzzy set theory. The approach formulates passage score combination according to general relevance decision principles. By operationalizing these principles using aggregation operators of fuzzy set theory, our approach justifies the common heuristics of taking the maximum constituent passage score as the overall document score. Experiments show that this heuristics is only the near best, with some fuzzy set aggregation operators stipulated in our approach being better methods. The significance of our principled approach is the applicability of many passage score combination methods, potentially bringing further performance enhancement. Experiments on several text retrieval conference collections demonstrate that our approach performs significantly better than document-based retrieval. While recent works in the literature mostly employ document-based rather than passage-based retrieval due to the common conception that document length normalization solves the problem of varying document lengths, our results show that document length normalization alone is not sufficient, especially in pseudo-relevance feedback retrieval.

Feature engineering is one aspect of knowledge engineering. Besides feature selection, the appropriate assignment of feature values is also crucial to the performance of many software applications, such as text categorization (TC) and speech recognition. In this work, we develop a general method to enhance TC performance by the use of context-dependent feature values (aka term weights), which are obtained by a novel adaptation of a context-dependent adjustment procedure previously shown to be effective in information retrieval. The motivation of our approach is that the general method can be used with different text representations and in combination of other TC techniques. Experiments on several test collections show that our context-dependent feature values can improve TC over traditional context-independent unigram feature values, using a strong classifier like Support Vector Machine (SVM), which past works have found to be hard to improve. We also show that the relative performance improvement of our method over the context-independent baseline is comparable to the levels attained by recent word embedding methods in the literature, while an advantage of our approach is that it does not require the substantial training needed to learn word embedding representations.

It is relatively easy to state that information retrieval (IR) is a scientific discipline but it is rather difficult to understand why it is science because what is science is still under debate in the philosophy of science. To be able to convince others that IR is science, our ability to explain why is crucial. To explain why IR is a scientific discipline, we use a theory and a model of scientific study, which were proposed recently. The explanation involves mapping the knowledge structure of IR to that of well-known scientific disciplines like physics. In addition, the explanation involves identifying the common aim, principles and assumptions in IR and in well-known scientific disciplines like physics, so that they constrain the scientific investigation in IR in a similar way as in physics. Therefore, there are strong similarities in terms of the knowledge structure and the constraints of the scientific investigations between IR and scientific disciplines like physics. Based on such similarities, IR is considered a scientific discipline.

A crucial aspect of scientific realism is what do we mean by true. In Luk’s theory and model of scientific study, a theory can be believed to be “true” but a model is only accurate. Therefore, what do we mean by a “true” theory in scientific realism? Here, we focus on exploring the notion of truth by some thought experiments and we come up with the idea that truth is related to what we mean by the same. This has repercussion to the repeatability of the experiments and the predictive power of scientific knowledge. Apart from sameness, we also found that truth is related to the granularity of the observation, the limit of detection, the distinguishability of the objects in theory, the simultaneous measurements of objects/processes, the consistencies of the theory and the one-to-one correspondence between terms/events and objects/processes, respectively. While there is no guarantee that we can arrive at the final “true” theory, we have a process/procedure with more and more experiments together with our own ingenuity, to direct us towards such a “true” theory. For quantum mechanics, since a particle is also regarded as a wave, quantum mechanics cannot be considered as a true theory based on the correspondence theory of truth. Failing this, truth may be defined by the coherence theory of truth which is similar to the coherence of beliefs. However, quantum mechanics may not be believed to be a true theory based on the coherence theory of truth because wave properties and particle properties may contradict. Further research is needed to address this problem if we want to regard quantum mechanics as a “true” theory.

Model construction is a kind of knowledge engineering, and building retrieval models is critical to the success of search engines. This article proposes a new (retrieval) language model, called binary independence language model (BILM). It integrates two document-context based language models together into one by the log-odds ratio where these two are language models applied to describe document-contexts of query terms. One model is based on relevance information while the other is based on the non-relevance information. Each model incorporates link dependencies and multiple query term dependencies. The probabilities are interpolated between the relative frequency and the background probabilities. In a simulated relevance feedback environment of top 20 judged documents, our BILM performed statistically significantly better than the other highly effective retrieval models at 95% confidence level across four TREC collections using fixed parameter values for the mean average precision. For the less stable performance measure (i.e. precision at the top 10), no statistical significance is shown between the different models for the individual test collections although numerically our BILM is better than two other models with a confidence level of 95% based on a paired sign test across the test collections of both relevance feedback and retrospective experiments.