Exploring Topic-based Language Models for Effective Web Information Retrieval

The main obstacle for providing focused search is the relative opaqueness of search request -- searchers tend to express their complex information needs in only a couple of keywords. Our overall aim is to find out if, and how, topic-based language models can lead to more effective web information retrieval. In this paper we explore retrieval performance of a topic-based model that combines topical models with other language models based on cross-entropy. We first define our topical categories and train our topical models on the .GOV2 corpus by building parsimonious language models. We then test the topic-based model on TREC8 small Web data collection for ad-hoc search.Our experimental results show that the topic-based model outperforms the standard language model and parsimonious model

Combining concepts and language models for information access

Since the middle of last century, information retrieval has gained an increasing interest. Since its inception, much research has been devoted to finding optimal ways of representing both documents and queries, as well as improving ways of matching one with the other. In cases where document annotations or explicit semantics are available, matching algorithms can be informed using the concept languages in which such semantics are usually defined. These algorithms are able to match queries and documents based on textual and semantic evidence. Recent advances have enabled the use of rich query representations in the form of query language models. This, in turn, allows us to account for the language associated with concepts within the retrieval model in a principled and transparent manner. Developments in the semantic web community, such as the Linked Open Data cloud, have enabled the association of texts with concepts on a large scale. Taken together, these developments facilitate a move beyond manually assigned concepts in domain-specific contexts into the general domain. This thesis investigates how one can improve information access by employing the actual use of concepts as measured by the language that people use when they discuss them. The main contribution is a set of models and methods that enable users to retrieve and access information on a conceptual level. Through extensive evaluations, a systematic exploration and thorough analysis of the experimental results of the proposed models is performed. Our empirical results show that a combination of top-down conceptual information and bottom-up statistical information obtains optimal performance on a variety of tasks and test collections

Investigation of the Lambda Parameter for Language Modeling Based Persian Retrieval

Language modeling is one of the most powerful methods in information retrieval. Many language modeling based retrieval systems have been developed and tested on English collections. Hence, the evaluation of language modeling on collections of other languages is an interesting research issue. In this study, four different language modeling methods proposed by Hiemstra [1] have been evaluated on a large Persian collection of a news archive. Furthermore, we study two different approaches that are proposed for tuning the Lambda parameter in the method. Experimental results show that the performance of language models on Persian text improves after Lambda Tuning. More specifically Witten Bell method provides the best results

Handling Massive N-Gram Datasets Efficiently

This paper deals with the two fundamental problems concerning the handling of large n-gram language models: indexing, that is compressing the n-gram strings and associated satellite data without compromising their retrieval speed; and estimation, that is computing the probability distribution of the strings from a large textual source. Regarding the problem of indexing, we describe compressed, exact and lossless data structures that achieve, at the same time, high space reductions and no time degradation with respect to state-of-the-art solutions and related software packages. In particular, we present a compressed trie data structure in which each word following a context of fixed length k, i.e., its preceding k words, is encoded as an integer whose value is proportional to the number of words that follow such context. Since the number of words following a given context is typically very small in natural languages, we lower the space of representation to compression levels that were never achieved before. Despite the significant savings in space, our technique introduces a negligible penalty at query time. Regarding the problem of estimation, we present a novel algorithm for estimating modified Kneser-Ney language models, that have emerged as the de-facto choice for language modeling in both academia and industry, thanks to their relatively low perplexity performance. Estimating such models from large textual sources poses the challenge of devising algorithms that make a parsimonious use of the disk. The state-of-the-art algorithm uses three sorting steps in external memory: we show an improved construction that requires only one sorting step thanks to exploiting the properties of the extracted n-gram strings. With an extensive experimental analysis performed on billions of n-grams, we show an average improvement of 4.5X on the total running time of the state-of-the-art approach.Comment: Published in ACM Transactions on Information Systems (TOIS), February 2019, Article No: 2

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