An experimental comparison of a genetic algorithm and a hill-climber for term selection

Purpose – The term selection problem for selecting query terms in information filtering and routing has been investigated using hill-climbers of various kinds, largely through the Okapi experiments in the TREC series of conferences. Although these are simple deterministic approaches which examine the effect of changing the weight of one term at a time, they have been shown to improve the retrieval effectiveness of filtering queries in these TREC experiments. Hill-climbers are, however, likely to get trapped in local optima, and the use of more sophisticated local search techniques for this problem that attempt to break out of these optima are worth investigating. To this end, we apply a genetic algorithm (GA) to the same problem. Design/Methodology/Approach – We use a standard TREC test collection from the TREC-8 filtering track, recording mean average precision and recall measures to allow comparison between the hillclimber and GA algorithms. We also vary elements of the GA, such as probability of a word being included, probability of mutation and population size in order to measure the effect of these variables. Different strategies such as Elitist and Non-Elitist methods are used, as well as Roulette Wheel and Rank selection GA algorithms. Findings – The results of tests suggest that both techniques are, on average, better than the baseline, but the implemented GA does not match the overall performance of a hill-climber. The Rank selection algorithm does better on average than the Roulette Wheel algorithm. There is no evidence in this study that varying word inclusion probability, mutation probability or Elitist method make much difference to the overall results. Small population sizes do not appear to be as effective as larger population sizes. Research limitations/implications – The evidence provided here would suggest that being stuck in a local optima for the term selection optimization problem does not appear to be detrimental to the overall success of the hill-climber. The evidence from term rank order would appear to provide extra useful evidence which hill-climbers can use efficiently and effectively to narrow the search space. Originality/Value – The paper represents the first attempt to compare hill-climbers with GAs on a problem of this type

An investigation into weighted data fusion for content-based multimedia information retrieval

Content Based Multimedia Information Retrieval (CBMIR) is characterised by the combination of noisy sources of information which, in unison, are able to achieve strong performance. In this thesis we focus on the combination of ranked results from the independent retrieval experts which comprise a CBMIR system through linearly weighted data fusion. The independent retrieval experts are low-level multimedia features, each of which contains an indexing function and ranking algorithm. This thesis is comprised of two halves. In the first half, we perform a rigorous empirical investigation into the factors which impact upon performance in linearly weighted data fusion. In the second half, we leverage these finding to create a new class of weight generation algorithms for data fusion which are capable of determining weights at query-time, such that the weights are topic dependent

Acceleration of ListNet for ranking using reconfigurable architecture

Document ranking is used to order query results by relevance with ranking models. ListNet is a well-known ranking approach for constructing and training learning-to-rank models. Compared with traditional learning approaches, ListNet delivers better accuracy, but is computationally too expensive to learn models with large data sets due to the large number of permutations and documents involved in computing the gradients. Currently, the long training time limits the practicality of ListNet in ranking applications such as breaking news search and stock prediction, and this situation is getting worse with the increase in data-set size. In order to tackle the challenge of long training time, this thesis optimises the ListNet algorithm, and designs hardware accelerators for learning the ListNet algorithm using Field Programmable Gate Arrays (FPGAs), making the algorithm more practical for real-world application. The contributions of this thesis include: 1) A novel computation method of the ListNet algorithm for ranking. The proposed computation method exposes more fine-grained parallelism for FPGA implementation. 2) A weighted sampling method that takes into account the ranking positions, along with an effective quantisation method based on FPGA devices. The proposed design achieves a 4.42x improvement over GPU implementation speed, while still guaranteeing the accuracy. 3) A full reconfigurable architecture for the ListNet training using multiple bitstream kernels. The proposed method achieves a higher model accuracy than pure fixed point training, and a better throughput than pure floating point training. This thesis has resulted in the acceleration of the ListNet algorithm for ranking using FPGAs by applying the above techniques. Significant improvements in speed have been achieved in this work against CPU and GPU implementations.Open Acces

Selective web information retrieval

This thesis proposes selective Web information retrieval, a framework formulated in terms of statistical decision theory, with the aim to apply an appropriate retrieval approach on a per-query basis. The main component of the framework is a decision mechanism that selects an appropriate retrieval approach on a per-query basis. The selection of a particular retrieval approach is based on the outcome of an experiment, which is performed before the final ranking of the retrieved documents. The experiment is a process that extracts features from a sample of the set of retrieved documents. This thesis investigates three broad types of experiments. The first one counts the occurrences of query terms in the retrieved documents, indicating the extent to which the query topic is covered in the document collection. The second type of experiments considers information from the distribution of retrieved documents in larger aggregates of related Web documents, such as whole Web sites, or directories within Web sites. The third type of experiments estimates the usefulness of the hyperlink structure among a sample of the set of retrieved Web documents. The proposed experiments are evaluated in the context of both informational and navigational search tasks with an optimal Bayesian decision mechanism, where it is assumed that relevance information exists. This thesis further investigates the implications of applying selective Web information retrieval in an operational setting, where the tuning of a decision mechanism is based on limited existing relevance information and the information retrieval system’s input is a stream of queries related to mixed informational and navigational search tasks. First, the experiments are evaluated using different training and testing query sets, as well as a mixture of different types of queries. Second, query sampling is introduced, in order to approximate the queries that a retrieval system receives, and to tune an ad-hoc decision mechanism with a broad set of automatically sampled queries

Community and Thread Methods for Identifying Best Answers in Online Question Answering Communities

Much research has recently investigated the measurement of quality answers in Question Answering (Q&A) communities in the form of automatic best answer identification. Previous approaches have focused on manual user annotations and diverse features based on intuition for identifying best answers and proved relatively successful despite considering best answer identification as a general classification problem. Best answer modelling is generally distanced from community studies about what users regard as important for identifying quality content. In particular, previous research tends to only focus on the automatic aspects of best answers identification model by applying generic learning algorithms. This thesis introduces the concepts of qualitative and structural design in order to investigate if features derived from community questionnaires can enrich the understanding of best answer identification in Q&A communities and if the thread-like structure of Q&A communities can be exploited for better results. Two different approaches for exploiting the thread structure of Q&A communities are proposed and two new, previously unstudied, features are introduced. First, a measure of question complexity is introduced as a proxy measure of answerer knowledge. Second, different models of contribution effort are proposed for representing the answering reactivity of contributors. The experiments are systematically conducted on datasets issued from three different communities that vary in size, content and structure. The results show that the newly proposed features allow for better understanding of what constitute best answers. The findings also reveal that the thread-wise algorithms and optimisation techniques created from the structural design methodology correlate with best answers. In general both structural and qualitative design appear to improve best answer identification meaning that structural and qualitative methods may improve unrelated classification tasks

Learning to select for information retrieval

The effective ranking of documents in search engines is based on various document features, such as the frequency of the query terms in each document, the length, or the authoritativeness of each document. In order to obtain a better retrieval performance, instead of using a single or a few features, there is a growing trend to create a ranking function by applying a learning to rank technique on a large set of features. Learning to rank techniques aim to generate an effective document ranking function by combining a large number of document features. Different ranking functions can be generated by using different learning to rank techniques or on different document feature sets. While the generated ranking function may be uniformly applied to all queries, several studies have shown that different ranking functions favour different queries, and that the retrieval performance can be significantly enhanced if an appropriate ranking function is selected for each individual query. This thesis proposes Learning to Select (LTS), a novel framework that selectively applies an appropriate ranking function on a per-query basis, regardless of the given query's type and the number of candidate ranking functions. In the learning to select framework, the effectiveness of a ranking function for an unseen query is estimated from the available neighbouring training queries. The proposed framework employs a classification technique (e.g. k-nearest neighbour) to identify neighbouring training queries for an unseen query by using a query feature. In particular, a divergence measure (e.g. Jensen-Shannon), which determines the extent to which a document ranking function alters the scores of an initial ranking of documents for a given query, is proposed for use as a query feature. The ranking function which performs the best on the identified training query set is then chosen for the unseen query. The proposed framework is thoroughly evaluated on two different TREC retrieval tasks (namely, Web search and adhoc search tasks) and on two large standard LETOR feature sets, which contain as many as 64 document features, deriving conclusions concerning the key components of LTS, namely the query feature and the identification of neighbouring queries components. Two different types of experiments are conducted. The first one is to select an appropriate ranking function from a number of candidate ranking functions. The second one is to select multiple appropriate document features from a number of candidate document features, for building a ranking function. Experimental results show that our proposed LTS framework is effective in both selecting an appropriate ranking function and selecting multiple appropriate document features, on a per-query basis. In addition, the retrieval performance is further enhanced when increasing the number of candidates, suggesting the robustness of the learning to select framework. This thesis also demonstrates how the LTS framework can be deployed to other search applications. These applications include the selective integration of a query independent feature into a document weighting scheme (e.g. BM25), the selective estimation of the relative importance of different query aspects in a search diversification task (the goal of the task is to retrieve a ranked list of documents that provides a maximum coverage for a given query, while avoiding excessive redundancy), and the selective application of an appropriate resource for expanding and enriching a given query for document search within an enterprise. The effectiveness of the LTS framework is observed across these search applications, and on different collections, including a large scale Web collection that contains over 50 million documents. This suggests the generality of the proposed learning to select framework. The main contributions of this thesis are the introduction of the LTS framework and the proposed use of divergence measures as query features for identifying similar queries. In addition, this thesis draws insights from a large set of experiments, involving four different standard collections, four different search tasks and large document feature sets. This illustrates the effectiveness, robustness and generality of the LTS framework in tackling various retrieval applications

Efficient query processing for scalable web search

Search engines are exceptionally important tools for accessing information in today’s world. In satisfying the information needs of millions of users, the effectiveness (the quality of the search results) and the efficiency (the speed at which the results are returned to the users) of a search engine are two goals that form a natural trade-off, as techniques that improve the effectiveness of the search engine can also make it less efficient. Meanwhile, search engines continue to rapidly evolve, with larger indexes, more complex retrieval strategies and growing query volumes. Hence, there is a need for the development of efficient query processing infrastructures that make appropriate sacrifices in effectiveness in order to make gains in efficiency. This survey comprehensively reviews the foundations of search engines, from index layouts to basic term-at-a-time (TAAT) and document-at-a-time (DAAT) query processing strategies, while also providing the latest trends in the literature in efficient query processing, including the coherent and systematic reviews of techniques such as dynamic pruning and impact-sorted posting lists as well as their variants and optimisations. Our explanations of query processing strategies, for instance the WAND and BMW dynamic pruning algorithms, are presented with illustrative figures showing how the processing state changes as the algorithms progress. Moreover, acknowledging the recent trends in applying a cascading infrastructure within search systems, this survey describes techniques for efficiently integrating effective learned models, such as those obtained from learning-to-rank techniques. The survey also covers the selective application of query processing techniques, often achieved by predicting the response times of the search engine (known as query efficiency prediction), and making per-query tradeoffs between efficiency and effectiveness to ensure that the required retrieval speed targets can be met. Finally, the survey concludes with a summary of open directions in efficient search infrastructures, namely the use of signatures, real-time, energy-efficient and modern hardware and software architectures

Application of Natural Language Processing to Determine User Satisfaction in Public Services

Research on customer satisfaction has increased substantially in recent years. However, the relative importance and relationships between different determinants of satisfaction remains uncertain. Moreover, quantitative studies to date tend to test for significance of pre-determined factors thought to have an influence with no scalable means to identify other causes of user satisfaction. The gaps in knowledge make it difficult to use available knowledge on user preference for public service improvement. Meanwhile, digital technology development has enabled new methods to collect user feedback, for example through online forums where users can comment freely on their experience. New tools are needed to analyze large volumes of such feedback. Use of topic models is proposed as a feasible solution to aggregate open-ended user opinions that can be easily deployed in the public sector. Generated insights can contribute to a more inclusive decision-making process in public service provision. This novel methodological approach is applied to a case of service reviews of publicly-funded primary care practices in England. Findings from the analysis of 145,000 reviews covering almost 7,700 primary care centers indicate that the quality of interactions with staff and bureaucratic exigencies are the key issues driving user satisfaction across England