Relevance-based language models : new estimations and applications

[Abstratc] Relevance-Based Language Models introduced in the Language Modelling framework the concept of relevance, which is explicit in other retrieval models such as the Probabilistic models. Relevance Models have been mainly used for a specific task within Information Retrieval called Pseudo-Relevance Feedback, a kind of local query expansion technique where relevance is assumed over a top of documents from the initial retrieval and where those documents are used to select expansion terms for the original query and produce a, hopefully more effective, second retrieval. In this thesis we investigate some new estimations for Relevance Models for both Pseudo-Relevance Feedback and other tasks beyond retrieval, particularly, constrained text clustering and item recommendation in Recommender Systems. We study the benefits of our proposals for those tasks in comparison with existing estimations. This new modellings are able not only to improve the effectiveness of the existing estimations and methods but also to outperform their robustness, a critical factor when dealing with Pseudo-Relevance Feedback methods. These objectives are pursued by different means: promoting divergent terms in the estimation of the Relevance Models, presenting new cluster-based retrieval models, introducing new methods for automatically determine the size of the pseudo-relevant set on a query-basis, and originally producing new modellings under the Relevance-Based Language Modelling framework for the constrained text clustering and the item recommendation problems

A Latent Dirichlet Framework for Relevance Modeling

Abstract. Relevance-based language models operate by estimating the probabilities of observing words in documents relevant (or pseudo relevant) to a topic. However, these models assume that if a document is relevant to a topic, then all tokens in the document are relevant to that topic. This could limit model robustness and effectiveness. In this study, we propose a Latent Dirichlet relevance model, which relaxes this assumption. Our approach derives from current research on Latent Dirichlet Allocation (LDA) topic models. LDA has been extensively explored, especially for generating a set of topics from a corpus. A key attraction is that in LDA a document may be about several topics. LDA itself, however, has a limitation that is also addressed in our work. Topics generated by LDA from a corpus are synthetic, i.e., they do not necessarily correspond to topics identified by humans for the same corpus. In contrast, our model explicitly considers the relevance relationships between documents and given topics (queries). Thus unlike standard LDA, our model is directly applicable to goals such as relevance feedback for query modification and text classification, where topics (classes and queries) are provided upfront. Thus although the focus of our paper is on improving relevance-based language models, in effect our approach bridges relevance-based language models and LDA addressing limitations of both. Finally, we propose an idea that takes advantage of “bagof-words” assumption to reduce the complexity of Gibbs sampling based learning algorithm

Probabilistic collaborative filtering with negative cross entropy

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in RecSys '13 Proceedings of the 7th ACM conference on Recommender systems, http://dx.doi.org/10.1145/2507157.2507191.Relevance-Based Language Models are an effective IR approach which explicitly introduces the concept of relevance in the statistical Language Modelling framework of Information Retrieval. These models have shown to achieve state-of-the-art retrieval performance in the pseudo relevance feedback task. In this paper we propose a novel adaptation of this language modeling approach to rating-based Collaborative Filtering. In a memory-based approach, we apply the model to the formation of user neighbourhoods, and the generation of recommendations based on such neighbourhoods. We report experimental results where our method outperforms other standard memory-based algorithms in terms of ranking precision.This work was funded by Secretaría de Estado de Investigación, Desarrollo e Innovación from the Spanish Government under projects TIN2012-33867 and TIN2011-28538-C02

Investigating the relationship between language model perplexity and IR precision-recall measures

An empirical study has been conducted investigating the relationship between the performance of an aspect based language model in terms of perplexity and the corresponding information retrieval performance obtained. It is observed, on the corpora considered, that the perplexity of the language model has a systematic relationship with the achievable precision recall performance though it is not statistically significant

Utilizing sub-topical structure of documents for information retrieval.

Text segmentation in natural language processing typically refers to the process of decomposing a document into constituent subtopics. Our work centers on the application of text segmentation techniques within information retrieval (IR) tasks. For example, for scoring a document by combining the retrieval scores of its constituent segments, exploiting the proximity of query terms in documents for ad-hoc search, and for question answering (QA), where retrieved passages from multiple documents are aggregated and presented as a single document to a searcher. Feedback in ad hoc IR task is shown to benefit from the use of extracted sentences instead of terms from the pseudo relevant documents for query expansion. Retrieval effectiveness for patent prior art search task is enhanced by applying text segmentation to the patent queries. Another aspect of our work involves augmenting text segmentation techniques to produce segments which are more readable with less unresolved anaphora. This is particularly useful for QA and snippet generation tasks where the objective is to aggregate relevant and novel information from multiple documents satisfying user information need on one hand, and ensuring that the automatically generated content presented to the user is easily readable without reference to the original source document