Online Forum Thread Retrieval using Pseudo Cluster Selection and Voting Techniques

Online forums facilitate knowledge seeking and sharing on the Web. However, the shared knowledge is not fully utilized due to information overload. Thread retrieval is one method to overcome information overload. In this paper, we propose a model that combines two existing approaches: the Pseudo Cluster Selection and the Voting Techniques. In both, a retrieval system first scores a list of messages and then ranks threads by aggregating their scored messages. They differ on what and how to aggregate. The pseudo cluster selection focuses on input, while voting techniques focus on the aggregation method. Our combined models focus on the input and the aggregation methods. The result shows that some combined models are statistically superior to baseline methods.Comment: The original publication is available at http://www.springerlink.com/. arXiv admin note: substantial text overlap with arXiv:1212.533

Semantic distillation: a method for clustering objects by their contextual specificity

Techniques for data-mining, latent semantic analysis, contextual search of databases, etc. have long ago been developed by computer scientists working on information retrieval (IR). Experimental scientists, from all disciplines, having to analyse large collections of raw experimental data (astronomical, physical, biological, etc.) have developed powerful methods for their statistical analysis and for clustering, categorising, and classifying objects. Finally, physicists have developed a theory of quantum measurement, unifying the logical, algebraic, and probabilistic aspects of queries into a single formalism. The purpose of this paper is twofold: first to show that when formulated at an abstract level, problems from IR, from statistical data analysis, and from physical measurement theories are very similar and hence can profitably be cross-fertilised, and, secondly, to propose a novel method of fuzzy hierarchical clustering, termed \textit{semantic distillation} -- strongly inspired from the theory of quantum measurement --, we developed to analyse raw data coming from various types of experiments on DNA arrays. We illustrate the method by analysing DNA arrays experiments and clustering the genes of the array according to their specificity.Comment: Accepted for publication in Studies in Computational Intelligence, Springer-Verla

Efficient Document Re-Ranking for Transformers by Precomputing Term Representations

Deep pretrained transformer networks are effective at various ranking tasks, such as question answering and ad-hoc document ranking. However, their computational expenses deem them cost-prohibitive in practice. Our proposed approach, called PreTTR (Precomputing Transformer Term Representations), considerably reduces the query-time latency of deep transformer networks (up to a 42x speedup on web document ranking) making these networks more practical to use in a real-time ranking scenario. Specifically, we precompute part of the document term representations at indexing time (without a query), and merge them with the query representation at query time to compute the final ranking score. Due to the large size of the token representations, we also propose an effective approach to reduce the storage requirement by training a compression layer to match attention scores. Our compression technique reduces the storage required up to 95% and it can be applied without a substantial degradation in ranking performance.Comment: Accepted at SIGIR 2020 (long

DE-PACRR: Exploring Layers Inside the PACRR Model

Recent neural IR models have demonstrated deep learning's utility in ad-hoc information retrieval. However, deep models have a reputation for being black boxes, and the roles of a neural IR model's components may not be obvious at first glance. In this work, we attempt to shed light on the inner workings of a recently proposed neural IR model, namely the PACRR model, by visualizing the output of intermediate layers and by investigating the relationship between intermediate weights and the ultimate relevance score produced. We highlight several insights, hoping that such insights will be generally applicable.Comment: Neu-IR 2017 SIGIR Workshop on Neural Information Retrieva

External query reformulation for text-based image retrieval

In text-based image retrieval, the Incomplete Annotation Problem (IAP) can greatly degrade retrieval effectiveness. A standard method used to address this problem is pseudo relevance feedback (PRF) which updates user queries by adding feedback terms selected automatically from top ranked documents in a prior retrieval run. PRF assumes that the target collection provides enough feedback information to select effective expansion terms. This is often not the case in image retrieval since images often only have short metadata annotations leading to the IAP. Our work proposes the use of an external knowledge resource (Wikipedia) in the process of refining user queries. In our method, Wikipedia documents strongly related to the terms in user query (" definition documents") are first identified by title matching between the query and titles of Wikipedia articles. These definition documents are used as indicators to re-weight the feedback documents from an initial search run on a Wikipedia abstract collection using the Jaccard coefficient. The new weights of the feedback documents are combined with the scores rated by different indicators. Query-expansion terms are then selected based on these new weights for the feedback documents. Our method is evaluated on the ImageCLEF WikipediaMM image retrieval task using text-based retrieval on the document metadata fields. The results show significant improvement compared to standard PRF methods

Cross-lingual Distillation for Text Classification

Cross-lingual text classification(CLTC) is the task of classifying documents written in different languages into the same taxonomy of categories. This paper presents a novel approach to CLTC that builds on model distillation, which adapts and extends a framework originally proposed for model compression. Using soft probabilistic predictions for the documents in a label-rich language as the (induced) supervisory labels in a parallel corpus of documents, we train classifiers successfully for new languages in which labeled training data are not available. An adversarial feature adaptation technique is also applied during the model training to reduce distribution mismatch. We conducted experiments on two benchmark CLTC datasets, treating English as the source language and German, French, Japan and Chinese as the unlabeled target languages. The proposed approach had the advantageous or comparable performance of the other state-of-art methods.Comment: Accepted at ACL 2017; Code available at https://github.com/xrc10/cross-distil