Combining Terrier with Apache Spark to Create Agile Experimental Information Retrieval Pipelines

Experimentation using IR systems has traditionally been a procedural and laborious process. Queries must be run on an index, with any parameters of the retrieval models suitably tuned. With the advent of learning-to-rank, such experimental processes (including the appropriate folding of queries to achieve cross-fold validation) have resulted in complicated experimental designs and hence scripting. At the same time, machine learning platforms such as Scikit Learn and Apache Spark have pioneered the notion of an experimental pipeline , which naturally allows a supervised classification experiment to be expressed a series of stages, which can be learned or transformed. In this demonstration, we detail Terrier-Spark, a recent adaptation to the Terrier Information Retrieval platform which permits it to be used within the experimental pipelines of Spark. We argue that this (1) provides an agile experimental platform for information retrieval, comparable to that enjoyed by other branches of data science; (2) aids research reproducibility in information retrieval by facilitating easily-distributable notebooks containing conducted experiments; and (3) facilitates the teaching of information retrieval experiments in educational environments

Upper Bound Approximations for BlockMaxWand

BlockMaxWand is a recent advance on the Wand dynamic pruning technique, which allows efficient retrieval without any effectiveness degradation to rank K. However, while BMW uses docid-sorted indices, it relies on recording the upper bound of the term weighting model scores for each block of postings in the inverted index. Such a requirement can be disadvantageous in situations such as when an index must be updated. In this work, we examine the appropriateness of upper-bound approximation – which have previously been shown suitable for Wand– in providing efficient retrieval for BMW. Experiments on the ClueWeb12 category B13 corpus using 5000 queries from a real search engine’s query log demonstrate that BMW still provides benefits w.r.t. Wand when approximate upper bounds are used, and that, if approximations on upper bounds are tight, BMW with approximate upper bounds can provide efficiency gains w.r.t.Wand with exact upper bounds, in particular for queries of short to medium length

Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation

Venue recommendation aims to assist users by making personalised suggestions of venues to visit, building upon data available from location-based social networks (LBSNs) such as Foursquare. A particular challenge for this task is context-aware venue recommendation (CAVR), which additionally takes the surrounding context of the user (e.g. the user’s location and the time of day) into account in order to provide more relevant venue suggestions. To address the challenges of CAVR, we describe two approaches that exploit word embedding techniques to infer the vector-space representations of venues, users’ existing preferences, and users’ contextual preferences. Our evaluation upon the test collection of the TREC 2015 Contextual Suggestion track demonstrates that we can significantly enhance the effectiveness of a state-of-the-art venue recommendation approach, as well as produce context-aware recommendations that are at least as effective as the top TREC 2015 systems

Efficient & Effective Selective Query Rewriting with Efficiency Predictions

To enhance effectiveness, a user's query can be rewritten internally by the search engine in many ways, for example by applying proximity, or by expanding the query with related terms. However, approaches that benefit effectiveness often have a negative impact on efficiency, which has impacts upon the user satisfaction, if the query is excessively slow. In this paper, we propose a novel framework for using the predicted execution time of various query rewritings to select between alternatives on a per-query basis, in a manner that ensures both effectiveness and efficiency. In particular, we propose the prediction of the execution time of ephemeral (e.g., proximity) posting lists generated from uni-gram inverted index posting lists, which are used in establishing the permissible query rewriting alternatives that may execute in the allowed time. Experiments examining both the effectiveness and efficiency of the proposed approach demonstrate that a 49% decrease in mean response time (and 62% decrease in 95th-percentile response time) can be attained without significantly hindering the effectiveness of the search engine

Analysing Compression Techniques for In-Memory Collaborative Filtering

Following the recent trend of in-memory data processing, it is a usual practice to maintain collaborative filtering data in the main memory when generating recommendations in academic and industrial recommender systems. In this paper, we study the impact of integer compression techniques for in-memory collaborative filtering data in terms of space and time efficiency. Our results provide relevant observations about when and how to compress collaborative filtering data. First, we observe that, depending on the memory constraints, compression techniques may speed up or slow down the performance of state-of-the art collaborative filtering algorithms. Second, after comparing different compression techniques, we find the Frame of Reference (FOR) technique to be the best option in terms of space and time efficiency under different memory constraints

Using Word Embeddings in Twitter Election Classification

Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to train and generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, the context window size and the dimensionality of word embeddings on the classification performance. By comparing the classification results of two word embedding models, which are trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data type should align with the Twitter classification dataset to achieve a better performance. Moreover, by evaluating the results of word embeddings models trained using various context window sizes and dimensionalities, we found that large context window and dimension sizes are preferable to improve the performance. Our experimental results also show that using word embeddings and CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings

Crowdsourced Rumour Identification During Emergencies

When a significant event occurs, many social media users leverage platforms such as Twitter to track that event. Moreover, emergency response agencies are increasingly looking to social media as a source of real-time information about such events. However, false information and rumours are often spread during such events, which can influence public opinion and limit the usefulness of social media for emergency management. In this paper, we present an initial study into rumour identification during emergencies using crowdsourcing. In particular, through an analysis of three tweet datasets relating to emergency events from 2014, we propose a taxonomy of tweets relating to rumours. We then perform a crowdsourced labeling experiment to determine whether crowd assessors can identify rumour-related tweets and where such labeling can fail. Our results show that overall, agreement over the tweet labels produced were high (0.7634 Fleiss Kappa), indicating that crowd-based rumour labeling is possible. However, not all tweets are of equal difficulty to assess. Indeed, we show that tweets containing disputed/controversial information tend to be some of the most difficult to identify

Towards Maximising Openness in Digital Sensitivity Review using Reviewing Time Predictions

The adoption of born-digital documents, such as email, by governments, such as in the UK and USA, has resulted in a large backlog of born-digital documents that must be sensitivity reviewed before they can be opened to the public, to ensure that no sensitive information is released, e.g. personal or confidential information. However, it is not practical to review all of the backlog with the available reviewing resources and, therefore, there is a need for automatic techniques to increase the number of documents that can be opened within a fixed reviewing time budget. In this paper, we conduct a user study and use the log data to build models to predict reviewing times for an average sensitivity reviewer. Moreover, we show that using our reviewing time predictions to select the order that documents are reviewed can markedly increase the ratio of reviewed documents that are released to the public, e.g. +30% for collections with high levels of sensitivity, compared to reviewing by shortest document first. This, in turn, increases the total number of documents that are opened to the public within a fixed reviewing time budget, e.g. an extra 200 documents in 100 hours reviewing

Active Learning Strategies for Technology Assisted Sensitivity Review

Government documents must be reviewed to identify and protect any sensitive information, such as personal information, before the documents can be released to the public. However, in the era of digital government documents, such as e-mail, traditional sensitivity review procedures are no longer practical, for example due to the volume of documents to be reviewed. Therefore, there is a need for new technology assisted review protocols to integrate automatic sensitivity classification into the sensitivity review process. Moreover, to effectively assist sensitivity review, such assistive technologies must incorporate reviewer feedback to enable sensitivity classifiers to quickly learn and adapt to the sensitivities within a collection, when the types of sensitivity are not known a priori. In this work, we present a thorough evaluation of active learning strategies for sensitivity review. Moreover, we present an active learning strategy that integrates reviewer feedback, from sensitive text annotations, to identify features of sensitivity that enable us to learn an effective sensitivity classifier (0.7 Balanced Accuracy) using significantly less reviewer effort, according to the sign test (p < 0.01 ). Moreover, this approach results in a 51% reduction in the number of documents required to be reviewed to achieve the same level of classification accuracy, compared to when the approach is deployed without annotation features