Voting for candidates: adapting data fusion techniques for an expert search task

In an expert search task, the users' need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users' query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query. We see the problem of ranking experts as a voting problem, which we model by adapting eleven data fusion techniques.We investigate the effectiveness of the voting approach and the associated data fusion techniques across a range of document weighting models, in the context of the TREC 2005 Enterprise track. The evaluation results show that the voting paradigm is very effective, without using any collection specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the data fusion techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the adapted data fusion techniques for the proposed approach is stable regardless of the used weighting models

Light syntactically-based index pruning for information retrieval

Most index pruning techniques eliminate terms from an index on the basis of the contribution of those terms to the content of the documents. We present a novel syntactically-based index pruning technique, which uses exclusively shallow syntactic evidence to decide upon which terms to prune. This type of evidence is document-independent, and is based on the assumption that, in a general collection of documents, there exists an approximately proportional relation between the frequency and content of ‘blocks of parts of speech’ (<i>P</i><i>O</i><i>S</i> <i>blocks</i>) [5]. POS blocks are fixed-length sequences of nouns, verbs, and other parts of speech, extracted from a corpus. We remove from the index, terms that correspond to low-frequency POS blocks, using two different strategies: (i) considering that low-frequency POS blocks correspond to sequences of content-poor words, and (ii) considering that low-frequency POS blocks, which also contain ‘non content-bearing parts of speech’, such as prepositions for example, correspond to sequences of content-poor words. We experiment with two TREC test collections and two statistically different weighting models. Using full indices as our baseline, we show that syntactically-based index pruning overall enhances retrieval performance, in terms of both average and early precision, for light pruning levels, while also reducing the size of the index. Our novel low-cost technique performs at least similarly to other related work, even though it does not consider document-specific information, and as such it is more general

Using relevance feedback in expert search

In Enterprise settings, expert search is considered an important task. In this search task, the user has a need for expertise - for instance, they require assistance from someone about a topic of interest. An expert search system assists users with their "expertise need" by suggesting people with relevant expertise to the topic of interest. In this work, we apply an expert search approach that does not explicitly rank candidates in response to a query, but instead implicitly ranks candidates by taking into account a ranking of document with respect to the query topic. Pseudo-relevance feedback, aka query expansion, has been shown to improve retrieval performance in adhoc search tasks. In this work, we investigate to which extent query expansion can be applied in an expert search task to improve the accuracy of the generated ranking of candidates. We define two approaches for query expansion, one based on the initial of ranking of documents for the query topic. The second approach is based on the final ranking of candidates. The aims of this paper are two-fold. Firstly, to determine if query expansion can be successfully applied in the expert search task, and secondly, to ascertain if either of the two forms of query expansion can provide robust, improved retrieval performance. We perform a thorough evaluation contrasting the two query expansion approaches in the context of the TREC 2005 and 2006 Enterprise tracks

Extending weighting models with a term quality measure

Weighting models use lexical statistics, such as term frequencies, to derive term weights, which are used to estimate the relevance of a document to a query. Apart from the removal of stopwords, there is no other consideration of the quality of words that are being ‘weighted’. It is often assumed that term frequency is a good indicator for a decision to be made as to how relevant a document is to a query. Our intuition is that raw term frequency could be enhanced to better discriminate between terms. To do so, we propose using non-lexical features to predict the ‘quality’ of words, before they are weighted for retrieval. Specifically, we show how parts of speech (e.g. nouns, verbs) can help estimate how informative a word generally is, regardless of its relevance to a query/document. Experimental results with two standard TREC collections show that integrating the proposed term quality to two established weighting models enhances retrieval performance, over a baseline that uses the original weighting models, at all times

Combining fields in known-item email search

Emails are examples of structured documents with various fields. These fields can be exploited to enhance the retrieval effectiveness of an Information Retrieval (IR) system that mailing list archives. In recent experiments of the TREC2005 Enterprise track, various fields were applied to varying degrees of success by the participants. In his work, using a field-based weighting model, we investigate the retrieval performance attainable by each field, and examine when fields evidence should be combined or not

Setting per-field normalisation hyper-parameters for the named-page finding search task

Per-field normalisation has been shown to be effective for Web search tasks, e.g. named-page finding. However, per-field normalisation also suffers from having hyper-parameters to tune on a per-field basis. In this paper, we argue that the purpose of per-field normalisation is to adjust the linear relationship between field length and term frequency. We experiment with standard Web test collections, using three document fields, namely the body of the document, its title, and the anchor text of its incoming links. From our experiments, we find that across different collections, the linear correlation values, given by the optimised hyper-parameter settings, are proportional to the maximum negative linear correlation. Based on this observation, we devise an automatic method for setting the per-field normalisation hyper-parameter values without the use of relevance assessment for tuning. According to the evaluation results, this method is shown to be effective for the body and title fields. In addition, the difficulty in setting the per-field normalisation hyper-parameter for the anchor text field is explained

Performance comparison of clustered and replicated information retrieval systems

The amount of information available over the Internet is increasing daily as well as the importance and magnitude of Web search engines. Systems based on a single centralised index present several problems (such as lack of scalability), which lead to the use of distributed information retrieval systems to effectively search for and locate the required information. A distributed retrieval system can be clustered and/or replicated. In this paper, using simulations, we present a detailed performance analysis, both in terms of throughput and response time, of a clustered system compared to a replicated system. In addition, we consider the effect of changes in the query topics over time. We show that the performance obtained for a clustered system does not improve the performance obtained by the best replicated system. Indeed, the main advantage of a clustered system is the reduction of network traffic. However, the use of a switched network eliminates the bottleneck in the network, markedly improving the performance of the replicated systems. Moreover, we illustrate the negative performance effect of the changes over time in the query topics when a distributed clustered system is used. On the contrary, the performance of a distributed replicated system is query independent

On the Impact of Entity Linking in Microblog Real-Time Filtering

Microblogging is a model of content sharing in which the temporal locality of posts with respect to important events, either of foreseeable or unforeseeable nature, makes applica- tions of real-time filtering of great practical interest. We propose the use of Entity Linking (EL) in order to improve the retrieval effectiveness, by enriching the representation of microblog posts and filtering queries. EL is the process of recognizing in an unstructured text the mention of relevant entities described in a knowledge base. EL of short pieces of text is a difficult task, but it is also a scenario in which the information EL adds to the text can have a substantial impact on the retrieval process. We implement a start-of-the-art filtering method, based on the best systems from the TREC Microblog track realtime adhoc retrieval and filtering tasks , and extend it with a Wikipedia-based EL method. Results show that the use of EL significantly improves over non-EL based versions of the filtering methods.Comment: 6 pages, 1 figure, 1 table. SAC 2015, Salamanca, Spain - April 13 - 17, 201

Sparse spatial selection for novelty-based search result diversification

Abstract. Novelty-based diversification approaches aim to produce a diverse ranking by directly comparing the retrieved documents. However, since such approaches are typically greedy, they require O(n 2) documentdocument comparisons in order to diversify a ranking of n documents. In this work, we propose to model novelty-based diversification as a similarity search in a sparse metric space. In particular, we exploit the triangle inequality property of metric spaces in order to drastically reduce the number of required document-document comparisons. Thorough experiments using three TREC test collections show that our approach is at least as effective as existing novelty-based diversification approaches, while improving their efficiency by an order of magnitude.

University of Glasgow at WebCLEF 2005: experiments in per-field normalisation and language specific stemming

We participated in the WebCLEF 2005 monolingual task. In this task, a search system aims to retrieve relevant documents from a multilingual corpus of Web documents from Web sites of European governments. Both the documents and the queries are written in a wide range of European languages. A challenge in this setting is to detect the language of documents and topics, and to process them appropriately. We develop a language specific technique for applying the correct stemming approach, as well as for removing the correct stopwords from the queries. We represent documents using three fields, namely content, title, and anchor text of incoming hyperlinks. We use a technique called per-field normalisation, which extends the Divergence From Randomness (DFR) framework, to normalise the term frequencies, and to combine them across the three fields. We also employ the length of the URL path of Web documents. The ranking is based on combinations of both the language specific stemming, if applied, and the per-field normalisation. We use our Terrier platform for all our experiments. The overall performance of our techniques is outstanding, achieving the overall top four performing runs, as well as the top performing run without metadata in the monolingual task. The best run only uses per-field normalisation, without applying stemming