Modeling latent topic interactions using quantum interference for information retrieval

Sordoni, Alessandro; He, Jifeng; Nie, Jian‐Yun

doi:10.1145/2505515.2507854

Cited by 21 publications

(23 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Note however that we have not attempted here any evaluation of what are the pros and cons, differences and similarities, of our modeling and the other existing approaches, also integrating quantum features. Let us just mention, to give a few examples, Foskett's work in the eighties of last century [29], Agosti et al work in the nineties [30,31], and Sordoni et al more recent work, where the double-slit experiment analogy is also used to investigate quantum interference effects for topic models such as LDA [32]. 11 To conclude, let us observe that in the same way the quantum cognition program, and its effectiveness, does not require the existence of microscopic quantum processes in the human brain [27], the path "towards a quantum Web" that we have sketched here, and in [14], where the Web of written documents is viewed as a "collection of traces" left by an abstract meaning entity -the QWeb, -should not be confused with the path "towards a quantum Internet" [28], which is about constructing an Internet able to transmit "quantum information," instead of just "classical information," that is, information carried by entities allowing quantum superposition to also take place and be fully exploited.…”

Section: Resultsmentioning

confidence: 99%

Modeling Meaning Associated with Documental Entities: Introducing the Brussels Quantum Approach

Aerts¹,

Bianchi²,

Sozzo

et al. 2019

STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics &Amp; Health

View full text Add to dashboard Cite

We show that the Brussels operational-realistic approach to quantum physics and quantum cognition offers a fundamental strategy for modeling the meaning associated with collections of documental entities. To do so, we take the World Wide Web as a paradigmatic example and emphasize the importance of distinguishing the Web, made of printed documents, from a more abstract meaning entity, which we call the Quantum Web, or QWeb, where the former is considered to be the collection of traces that can be left by the latter, in specific measurements, similarly to how a non-spatial quantum entity, like an electron, can leave localized traces of impact on a detection screen. The double-slit experiment is extensively used to illustrate the rationale of the modeling, which is guided by how physicists constructed quantum theory to describe the behavior of the microscopic entities. We also emphasize that the superposition principle and the associated interference effects are not sufficient to model all experimental probabilistic data, like those obtained by counting the relative number of documents containing certain words and co-occurrences of words. For this, additional effects, like context effects, must also be taken into consideration.

show abstract

Section: Resultsmentioning

confidence: 99%

Modeling Meaning Associated with Documental Entities: Introducing the Brussels Quantum Approach

Aerts¹,

Bianchi²,

Sozzo

et al. 2019

STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics &Amp; Health

View full text Add to dashboard Cite

show abstract

“…The results suggest that incorporating topic similarity helps improve document retrieval performance. One reason why topic models help improve document retrieval performance as we compare the similarity between the document and the query based on latent factors rather than just the words (Wei and Croft 2006;Sordoni et al 2013). Hence, this feature which our model computes is extremely important for document retrieval learning task.…”

Section: Quantitative Resultsmentioning

confidence: 99%

Supervised topic models with word order structure for document classification and retrieval learning

Jameel

Lam

2015

Inf Retrieval J

View full text Add to dashboard Cite

One limitation of most existing probabilistic latent topic models for document classification is that the topic model itself does not consider useful side-information, namely, class labels of documents. Topic models, which in turn consider the side-information, popularly known as supervised topic models, do not consider the word order structure in documents. One of the motivations behind considering the word order structure is to capture the semantic fabric of the document. We investigate a low-dimensional latent topic model for document classification. Class label information and word order structure are integrated into a supervised topic model enabling a more effective interaction among such information for solving document classification. We derive a collapsed Gibbs sampler for our model. Likewise, supervised topic models with word order structure have not been explored in document retrieval learning. We propose a novel supervised topic model for document retrieval learning which can be regarded as a pointwise model for tackling the learning-to-rank task. Available relevance assessments and word order structure are integrated into the topic model itself. We conduct extensive experiments on several publicly available benchmark datasets, and show that our model improves upon the state-of-the-art models.

show abstract

“…Quantum-inspired information retrieval (IR) modeling has been an emerging topic ever since the pioneering work by Van Rijsbergen [7]. Motivated by this work, researchers have been trying to get inspiration from various quantum concepts for addressing IR tasks, such as quantum interference [14], quantum entanglement [9], quantum measurement [13], quantum detection [5] and quantum probability theory [4,6].…”

Section: Introductionmentioning

confidence: 99%

Quantum Language Model-based Query Expansion

Melucci

Tiwari

2018

Proceedings of the 2018 ACM SIGIR International Conference on Theory of Information Retrieval

View full text Add to dashboard Cite

The analogy between words, documents and queries and the Quantum Mechanics (QM) concepts gives rise to various quantum-inspired Information Retrieval (IR) models. As one of the most successful applications among them, Quantum Language Model (QLM) achieves superior performances compared to various classical models on ad-hoc retrieval tasks. However, the EM-based estimation strategy for QLM is limited in that it cannot efficiently converge to global optimum. As a result, subsequent QLM-based models are more or less restricted to a limited vocabulary. In order to ease this limitation, this study investigates a query expansion framework on the QLM basis. Essentially, the additional terms are selected from the constructed QLM of top-K returned documents in the initial ranking, and a re-ranking is conducted on the expanded query to generate the final ranks. Experiments on TREC 2013 and 2014 session track datasets demonstrate the effectiveness of our model.

show abstract

Modeling latent topic interactions using quantum interference for information retrieval

Cited by 21 publications

References 9 publications

Modeling Meaning Associated with Documental Entities: Introducing the Brussels Quantum Approach

Modeling Meaning Associated with Documental Entities: Introducing the Brussels Quantum Approach

Supervised topic models with word order structure for document classification and retrieval learning

Quantum Language Model-based Query Expansion

Contact Info

Product

Resources

About