When a Web user's underlying information need is not clearly specified from the initial query, an effective approach is to diversify the results retrieved for this query. In this paper, we introduce a novel probabilistic framework for Web search result diversification, which explicitly accounts for the various aspects associated to an underspecified query. In particular, we diversify a document ranking by estimating how well a given document satisfies each uncovered aspect and the extent to which different aspects are satisfied by the ranking as a whole. We thoroughly evaluate our framework in the context of the diversity task of the TREC 2009 Web track. Moreover, we exploit query reformulations provided by three major Web search engines (WSEs) as a means to uncover different query aspects. The results attest the effectiveness of our framework when compared to state-of-the-art diversification approaches in the literature. Additionally, by simulating an upper-bound query reformulation mechanism from official TREC data, we draw useful insights regarding the effectiveness of the query reformulations generated by the different WSEs in promoting diversity.

Exploiting query reformulations for web search result diversification

Opinions are ubiquitous in text, and readers of on-line text — from consumers to sports fans to news addicts to governments — can benefit from automatic methods that synthesise useful opinion-orientated information from the sea of data In this chapter on opinion mining and sentiment analysis, we introduce an idealised, end-to-end opinion analysis system and describe its components, including constructing opinion lexica, performing sentiment analysis, and producing opinion summaries

/pdf/39-opinion-mining-and-sentiment-analysis-2wo5n16jvv.pdf

39. Opinion mining and sentiment analysis

The TREC 2009 web ad hoc and relevance feedback tasks used a new document collection, the ClueWeb09 dataset, which was crawled from the general web in early 2009. This dataset contains 1 billion web pages, a substantial fraction of which are spam--pages designed to deceive search engines so as to deliver an unwanted payload. We examine the effect of spam on the results of the TREC 2009 web ad hoc and relevance feedback tasks, which used the ClueWeb09 dataset. We show that a simple content-based classifier with minimal training is efficient enough to rank the "spamminess" of every page in the dataset using a standard personal computer in 48 hours, and effective enough to yield significant and substantive improvements in the fixed-cutoff precision (estP10) as well as rank measures (estR-Precision, StatMAP, MAP) of nearly all submitted runs. Moreover, using a set of "honeypot" queries the labeling of training data may be reduced to an entirely automatic process. The results of classical information retrieval methods are particularly enhanced by filtering--from among the worst to among the best.

pdf/efficient-and-effective-spam-filtering-and-re-ranking-for-21nw7abicd.pdf

Efficient and effective spam filtering and re-ranking for large web datasets

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.

/pdf/voting-for-candidates-adapting-data-fusion-techniques-for-an-239z8gtan4.pdf

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

Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this work, we investigate how two pretrained contextualized language models (ELMo and BERT) can be utilized for ad-hoc document ranking. Through experiments on TREC benchmarks, we find that several existing neural ranking architectures can benefit from the additional context provided by contextualized language models. Furthermore, we propose a joint approach that incorporates BERT's classification vector into existing neural models and show that it outperforms state-of-the-art ad-hoc ranking baselines. We call this joint approach CEDR (Contextualized Embeddings for Document Ranking). We also address practical challenges in using these models for ranking, including the maximum input length imposed by BERT and runtime performance impacts of contextualized language models.

CEDR: Contextualized Embeddings for Document Ranking.

In TREC 2007, we participate in four tasks of the Blog and Enterprise tracks. We continue experiments using Terrier 1 [14], our modular and scalable Information Retrieval (IR) platform, and the Divergence From Randomness (DFR) framework. In particular, for the Blog track opinion finding task, we propose a statisti cal term weighting approach to identify opinionated documents. An alternative approach based on an opinion identification too l is also utilised. Overall, a 15% improvement over a non-opinionated baseline is observed in applying the statistical term weighting approach. In the Expert Search task of the Enterprise track, we investi gate the use of proximity between query terms and candidate name occurrences in documents.

/pdf/university-of-glasgow-at-trec-2005-experiments-in-terabyte-4rusxlqtqy.pdf

University of Glasgow at TREC 2005: Experiments in Terabyte and Enterprise Tracks with Terrier

Term dependency, or co-occurrence, has been studied in language modelling, for instance by Metzler & Croft who showed that retrieval performance could be significantlyenhanced using term dependency information. In this work, weshow how term dependency can be modelled within the Divergence From Randomness (DFR) framework. We evaluate our term dependency model on the two adhoc retrieval tasks using the TREC .GOV2 Terabyte collection. Furthermore, we examine the effect of varying the term dependency window size on the retrieval performance of the proposed model. Our experiments show that term dependency can indeed besuccessfully incorporated within the DFR framework.

/pdf/incorporating-term-dependency-in-the-dfr-framework-qn0jnkocc0.pdf

Incorporating term dependency in the dfr framework

: In TREC 2009, we extend our Voting Model for the faceted blog distillation, top stories identification, and related entity finding tasks. Moreover, we experiment with our novel xQuAD framework for search result diversification. Besides fostering our research in multiple directions, by participating in such a wide portfolio of tracks, we further develop the indexing and retrieval capabilities of our Terrier Information Retrieval platform, to effectively and efficiently cope with a new generation of large-scale test collections.

/pdf/university-of-glasgow-at-trec-2009-experiments-with-terrier-6p8qm73l9f.pdf

University of Glasgow at TREC 2009: Experiments with Terrier

Learning To Rank (LTR) techniques aim to learn an effective document ranking function by combining several document features. While the function learned may be uniformly applied to all queries, many studies have shown that different ranking functions favour different queries, and the retrieval performance can be significantly enhanced if an appropriate ranking function is selected for each individual query. In this paper, we propose a novel Learning To Select framework that selectively applies an appropriate ranking function on a per-query basis. The approach employs a query feature to identify similar training queries for an unseen query. The ranking function which performs the best on this identified training query set is then chosen for the unseen query. In particular, we propose the use of divergence, which measures the extent that a document ranking function alters the scores of an initial ranking of documents for a given query, as a query feature. We evaluate our method using tasks from the TREC Web and Million Query tracks, in combination with the LETOR 3.0 and LETOR 4.0 feature sets. Our experimental results show that our proposed method is effective and robust for selecting an appropriate ranking function on a per-query basis. In particular, it always outperforms three state-of-the-art LTR techniques, namely Ranking SVM, AdaRank, and the automatic feature selection method.

/pdf/learning-to-select-a-ranking-function-36f7zyjozs.pdf

Learning to select a ranking function

: In TREC 2008, we participate in the Blog, Enterprise, and Relevance Feedback tracks. In all tracks, we continue the research and development of the Terrier platform centred around extending state-of-the-art weighting models based on the Divergence From Randomness (DFR) framework. In particular, we investigate two main themes, namely, proximity-based models, and collection and profile enrichment techniques based on several resources. In the Blog track, we aim to improve our opinion detection techniques and to integrate various new blog-specific features into our Voting Model. For the baseline ad-hoc task, we aim to build strongly performing baselines by applying two different techniques. The first one boosts documents in which query terms co-occur in a given window size, and the second one applies query expansion using collection enrichment. Non-English documents are also removed from the retrieved results. In the opinion-finding task, we experiment with two main opinion detection approaches. The first one improves our TREC 2007 dictionary-based approach by automatically building an internal opinion dictionary from the collection itself. We measure the opinionated discriminability of each term using an information-theoretic divergence measure based on the relevance assessments of previous years. The second approach is based on the OpinionFinder tool, which identifies subjective sentences in text. In particular, we introduce a novel method to measure the informativeness of query terms occurring in close proximity to subjective sentences. In the blog distillation task, we have two research themes.

/pdf/university-of-glasgow-at-trec-2008-experiments-in-blog-4gvaubrrx7.pdf

University of Glasgow at TREC 2008: Experiments in Blog, Enterprise, and Relevance Feedback Tracks with Terrier

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