Impact of document representation on neural ad hoc retrieval

© 2018 Association for Computing Machinery. Neural embeddings have been effectively integrated into information retrieval tasks including ad hoc retrieval. One of the benefits of neural embeddings is they allow for the calculation of the similarity between queries and documents through vector similarity calculation methods. While such methods have been effective for document matching, they have an inherent bias towards documents that are sized relatively similarly. Therefore, the difference between the query and document lengths, referred to as the query-document size imbalance problem, becomes an issue when incorporating neural embeddings and their associated similarity calculation models into the ad hoc document retrieval process. In this paper, we propose that document representation methods need to be used to address the size imbalance problem and empirically show their impact on the performance of neural embedding-based ad hoc retrieval. In addition, we explore several types of document representation methods and investigate their impact on the retrieval process. We conduct our experiments on three widely used standard corpora, namely Clueweb09B, Clueweb12B and Robust04 and their associated topics. Summarily, we find that document representation methods are able to effectively address the query-document size imbalance problem and significantly improve the performance of neural ad hoc retrieval. In addition, we find that a document representation method based on a simple term-frequency shows significantly better performance compared to more sophisticated representation methods such as neural composition and aspect-based methods

Parsimonious Language Models for a Terabyte of Text

The aims of this paper are twofold. Our first aim\ud is to compare results of the earlier Terabyte tracks\ud to the Million Query track. We submitted a number\ud of runs using different document representations\ud (such as full-text, title-fields, or incoming\ud anchor-texts) to increase pool diversity. The initial\ud results show broad agreement in system rankings\ud over various measures on topic sets judged at both\ud Terabyte and Million Query tracks, with runs using\ud the full-text index giving superior results on\ud all measures, but also some noteworthy upsets.\ud Our second aim is to explore the use of parsimonious\ud language models for retrieval on terabyte-scale\ud collections. These models are smaller thus\ud more efficient than the standard language models\ud when used at indexing time, and they may also improve\ud retrieval performance. We have conducted\ud initial experiments using parsimonious models in\ud combination with pseudo-relevance feedback, for\ud both the Terabyte and Million Query track topic\ud sets, and obtained promising initial results

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

Topic based language models for ad hoc information retrieval

We propose a topic based approach lo language modelling for ad-hoc Information Retrieval (IR). Many smoothed estimators used for the multinomial query model in IR rely upon the estimated background collection probabilities. In this paper, we propose a topic based language modelling approach, that uses a more informative prior based on the topical content of a document. In our experiments, the proposed model provides comparable IR performance to the standard models, but when combined in a two stage language model, it outperforms all other estimated models

Validating simulated interaction for retrieval evaluation

A searcher’s interaction with a retrieval system consists of actions such as query formulation, search result list interaction and document interaction. The simulation of searcher interaction has recently gained momentum in the analysis and evaluation of interactive information retrieval (IIR). However, a key issue that has not yet been adequately addressed is the validity of such IIR simulations and whether they reliably predict the performance obtained by a searcher across the session. The aim of this paper is to determine the validity of the common interaction model (CIM) typically used for simulating multi-query sessions. We focus on search result interactions, i.e., inspecting snippets, examining documents and deciding when to stop examining the results of a single query, or when to stop the whole session. To this end, we run a series of simulations grounded by real world behavioral data to show how accurate and responsive the model is to various experimental conditions under which the data were produced. We then validate on a second real world data set derived under similar experimental conditions. We seek to predict cumulated gain across the session. We find that the interaction model with a query-level stopping strategy based on consecutive non-relevant snippets leads to the highest prediction accuracy, and lowest deviation from ground truth, around 9 to 15% depending on the experimental conditions. To our knowledge, the present study is the first validation effort of the CIM that shows that the model’s acceptance and use is justified within IIR evaluations. We also identify and discuss ways to further improve the CIM and its behavioral parameters for more accurate simulations

Embedding Web-based Statistical Translation Models in Cross-Language Information Retrieval

Although more and more language pairs are covered by machine translation services, there are still many pairs that lack translation resources. Cross-language information retrieval (CLIR) is an application which needs translation functionality of a relatively low level of sophistication since current models for information retrieval (IR) are still based on a bag-of-words. The Web provides a vast resource for the automatic construction of parallel corpora which can be used to train statistical translation models automatically. The resulting translation models can be embedded in several ways in a retrieval model. In this paper, we will investigate the problem of automatically mining parallel texts from the Web and different ways of integrating the translation models within the retrieval process. Our experiments on standard test collections for CLIR show that the Web-based translation models can surpass commercial MT systems in CLIR tasks. These results open the perspective of constructing a fully automatic query translation device for CLIR at a very low cost.Comment: 37 page

Looking at Vector Space and Language Models for IR using Density Matrices

In this work, we conduct a joint analysis of both Vector Space and Language Models for IR using the mathematical framework of Quantum Theory. We shed light on how both models allocate the space of density matrices. A density matrix is shown to be a general representational tool capable of leveraging capabilities of both VSM and LM representations thus paving the way for a new generation of retrieval models. We analyze the possible implications suggested by our findings.Comment: In Proceedings of Quantum Interaction 201

TopSig: Topology Preserving Document Signatures

Performance comparisons between File Signatures and Inverted Files for text retrieval have previously shown several significant shortcomings of file signatures relative to inverted files. The inverted file approach underpins most state-of-the-art search engine algorithms, such as Language and Probabilistic models. It has been widely accepted that traditional file signatures are inferior alternatives to inverted files. This paper describes TopSig, a new approach to the construction of file signatures. Many advances in semantic hashing and dimensionality reduction have been made in recent times, but these were not so far linked to general purpose, signature file based, search engines. This paper introduces a different signature file approach that builds upon and extends these recent advances. We are able to demonstrate significant improvements in the performance of signature file based indexing and retrieval, performance that is comparable to that of state of the art inverted file based systems, including Language models and BM25. These findings suggest that file signatures offer a viable alternative to inverted files in suitable settings and from the theoretical perspective it positions the file signatures model in the class of Vector Space retrieval models.Comment: 12 pages, 8 figures, CIKM 201