Facet-based opinion retrieval from blogs

The paper presents methods of retrieving blog posts containing opinions about an entity expressed in the query. The methods use a lexicon of subjective words and phrases compiled from manually and automatically developed resources. One of the methods uses the Kullback–Leibler divergence to weight subjective words occurring near query terms in documents, another uses proximity between the occurrences of query terms and subjective words in documents, and the third combines both factors. Methods of structuring queries into facets, facet expansion using Wikipedia, and a facet-based retrieval are also investigated in this work. The methods were evaluated using the TREC 2007 and 2008 Blog track topics, and proved to be highly effective.     

A retrospective study of a hybrid document-context based retrieval model

This paper describes our novel retrieval model that is based on contexts of query terms in documents (i.e., document contexts). Our model is novel because it explicitly takes into account of the document contexts instead of implicitly using the document contexts to find query expansion terms. Our model is based on simulating a user making relevance decisions, and it is a hybrid of various existing effective models and techniques. It estimates the relevance decision preference of a document context as the log-odds and uses smoothing techniques as found in language models to solve the problem of zero probabilities. It combines these estimated preferences of document contexts using different types of aggregation operators that comply with different relevance decision principles (e.g., aggregate relevance principle). Our model is evaluated using retrospective experiments (i.e., with full relevance information), because such experiments can (a) reveal the potential of our model, (b) isolate the problems of the model from those of the parameter estimation, (c) provide information about the major factors affecting the retrieval effectiveness of the model, and (d) show that whether the model obeys the probability ranking principle. Our model is promising as its mean average precision is 60–80% in our experiments using different TREC ad hoc English collections and the NTCIR-5 ad hoc Chinese collection. Our experiments showed that (a) the operators that are consistent with aggregate relevance principle were effective in combining the estimated preferences, and (b) that estimating probabilities using the contexts in the relevant documents can produce better retrieval effectiveness than using the entire relevant documents.     

Estimating Gaussian mixture models in the local neighbourhood of embedded word vectors for query performance prediction

The study of query performance prediction (QPP) in information retrieval (IR) aims to predict retrieval effectiveness. The specificity of the underlying information need of a query often determines how effectively can a search engine retrieve relevant documents at top ranks. The presence of ambiguous terms makes a query less specific to the sought information need, which in turn may degrade IR effectiveness. In this paper, we propose a novel word embedding based pre-retrieval feature which measures the ambiguity of each query term by estimating how many ‘senses’ each word is associated with. Assuming each sense roughly corresponds to a Gaussian mixture component, our proposed generative model first estimates a Gaussian mixture model (GMM) from the word vectors that are most similar to the given query terms. We then use the posterior probabilities of generating the query terms themselves from this estimated GMM in order to quantify the ambiguity of the query. Previous studies have shown that post-retrieval QPP approaches often outperform pre-retrieval ones because they use additional information from the top ranked documents. To achieve the best of both worlds, we formalize a linear combination of our proposed GMM based pre-retrieval predictor with NQC, a state-of-the-art post-retrieval QPP. Our experiments on the TREC benchmark news and web collections demonstrate that our proposed hybrid QPP approach (in linear combination with NQC) significantly outperforms a range of other existing pre-retrieval approaches in combination with NQC used as baselines.     

Improving Information Retrieval Performance on OCRed Text in the Absence of Clean Text Ground Truth

OCR errors in text harm information retrieval performance. Much research has been reported on modelling and correction of Optical Character Recognition (OCR) errors. Most of the prior work employ language dependent resources or training texts in studying the nature of errors. However, not much research has been reported that focuses on improving retrieval performance from erroneous text in the absence of training data. We propose a novel approach for detecting OCR errors and improving retrieval performance from the erroneous corpus in a situation where training samples are not available to model errors. In this paper we propose a method that automatically identifies erroneous term variants in the noisy corpus, which are used for query expansion, in the absence of clean text. We employ an effective combination of contextual information and string matching techniques. Our proposed approach automatically identifies the erroneous variants of query terms and consequently leads to improvement in retrieval performance through query expansion. Our proposed approach does not use any training data or any language specific resources like thesaurus for identification of error variants. It also does not expend any knowledge about the language except that the word delimiter is blank space. We have tested our approach on erroneous Bangla (Bengali in English) and Hindi FIRE collections, and also on TREC Legal IIT CDIP and TREC 5 Confusion track English corpora. Our proposed approach has achieved statistically significant improvements over the state-of-the-art baselines on most of the datasets.     

Lexical cohesion and term proximity in document ranking

We demonstrate effective new methods of document ranking based on lexical cohesive relationships between query terms. The proposed methods rely solely on the lexical relationships between original query terms, and do not involve query expansion or relevance feedback. Two types of lexical cohesive relationship information between query terms are used in document ranking: short-distance collocation relationship between query terms, and long-distance relationship, determined by the collocation of query terms with other words. The methods are evaluated on TREC corpora, and show improvements over baseline systems.     

Adapting information retrieval to query contexts

In current IR approaches documents are retrieved only according to the terms specified in the query. The same answers are returned for the same query whatever the user and the search goal are. In reality, many other contextual factors strongly influence document’s relevance and they should be taken into account in IR operations. This paper proposes a method, based on language modeling, to integrate several contextual factors so that document ranking will be adapted to the specific query contexts. We will consider three contextual factors in this paper: the topic domain of the query, the characteristics of the document collection, as well as context words within the query. Each contextual factor is used to generate a new query language model to specify some aspect of the information need. All these query models are then combined together to produce a more complete model for the underlying information need. Our experiments on TREC collections show that each contextual factor can positively influence the IR effectiveness and the combined model results in the highest effectiveness. This study shows that it is both beneficial and feasible to integrate more contextual factors in the current IR practice.     

Relevance-based entity selection for ad hoc retrieval

Recent developments have shown that entity-based models that rely on information from the knowledge graph can improve document retrieval performance. However, given the non-transitive nature of relatedness between entities on the knowledge graph, the use of semantic relatedness measures can lead to topic drift. To address this issue, we propose a relevance-based model for entity selection based on pseudo-relevance feedback, which is then used to systematically expand the input query leading to improved retrieval performance. We perform our experiments on the widely used TREC Web corpora and empirically show that our proposed approach to entity selection significantly improves ad hoc document retrieval compared to strong baselines. More concretely, the contributions of this work are as follows: (1) We introduce a graphical probability model that captures dependencies between entities within the query and documents. (2) We propose an unsupervised entity selection method based on the graphical model for query entity expansion and then for ad hoc retrieval. (3) We thoroughly evaluate our method and compare it with the state-of-the-art keyword and entity based retrieval methods. We demonstrate that the proposed retrieval model shows improved performance over all the other baselines on ClueWeb09B and ClueWeb12B, two widely used Web corpora, on the [email protected], and [email protected] metrics. We also show that the proposed method is most effective on the difficult queries. In addition, We compare our proposed entity selection with a state-of-the-art entity selection technique within the context of ad hoc retrieval using a basic query expansion method and illustrate that it provides more effective retrieval for all expansion weights and different number of expansion entities.     

A split-list approach for relevance feedback in information retrieval

In this paper we present a new algorithm for relevance feedback (RF) in information retrieval. Unlike conventional RF algorithms which use the top ranked documents for feedback, our proposed algorithm is a kind of active feedback algorithm which actively chooses documents for the user to judge. The objectives are (a) to increase the number of judged relevant documents and (b) to increase the diversity of judged documents during the RF process. The algorithm uses document-contexts by splitting the retrieval list into sub-lists according to the query term patterns that exist in the top ranked documents. Query term patterns include a single query term, a pair of query terms that occur in a phrase and query terms that occur in proximity. The algorithm is an iterative algorithm which takes one document for feedback in each of the iterations. We experiment with the algorithm using the TREC-6, -7, -8, -2005 and GOV2 data collections and we simulate user feedback using the TREC relevance judgements. From the experimental results, we show that our proposed split-list algorithm is better than the conventional RF algorithm and that our algorithm is more reliable than a similar algorithm using maximal marginal relevance.     

Supervised approaches for explicit search result diversification

Diversification of web search results aims to promote documents with diverse content (i.e., covering different aspects of a query) to the top-ranked positions, to satisfy more users, enhance fairness and reduce bias. In this work, we focus on the explicit diversification methods, which assume that the query aspects are known at the diversification time, and leverage supervised learning methods to improve their performance in three different frameworks with different features and goals. First, in the LTRDiv framework, we focus on applying typical learning to rank (LTR) algorithms to obtain a ranking where each top-ranked document covers as many aspects as possible. We argue that such rankings optimize various diversification metrics (under certain assumptions), and hence, are likely to achieve diversity in practice. Second, in the AspectRanker framework, we apply LTR for ranking the aspects of a query with the goal of more accurately setting the aspect importance values for diversification. As features, we exploit several pre- and post-retrieval query performance predictors (QPPs) to estimate how well a given aspect is covered among the candidate documents. Finally, in the LmDiv framework, we cast the diversification problem into an alternative fusion task, namely, the supervised merging of rankings per query aspect. We again use QPPs computed over the candidate set for each aspect, and optimize an objective function that is tailored for the diversification goal. We conduct thorough comparative experiments using both the basic systems (based on the well-known BM25 matching function) and the best-performing systems (with more sophisticated retrieval methods) from previous TREC campaigns. Our findings reveal that the proposed frameworks, especially AspectRanker and LmDiv, outperform both non-diversified rankings and two strong diversification baselines (i.e., xQuAD and its variant) in terms of various effectiveness metrics.     

A domain-independent approach to finding related entities

We propose an approach to the retrieval of entities that have a specific relationship with the entity given in a query. Our research goal is to investigate whether related entity finding problem can be addressed by combining a measure of relatedness of candidate answer entities to the query, and likelihood that the candidate answer entity belongs to the target entity category specified in the query. An initial list of candidate entities, extracted from top ranked documents retrieved for the query, is refined using a number of statistical and linguistic methods. The proposed method extracts the category of the target entity from the query, identifies instances of this category as seed entities, and computes similarity between candidate and seed entities. The evaluation was conducted on the Related Entity Finding task of the Entity Track of TREC 2010, as well as the QA list questions from TREC 2005 and 2006. Evaluation results demonstrate that the proposed methods are effective in finding related entities.     

A relevance model for a data warehouse contextualized with documents

This paper presents a relevance model to rank the facts of a data warehouse that are described in a set of documents retrieved with an information retrieval (IR) query. The model is based in language modeling and relevance modeling techniques. We estimate the relevance of the facts by the probability of finding their dimensions values and the query keywords in the documents that are relevant to the query. The model is the core of the so-called contextualized warehouse, which is a new kind of decision support system that combines structured data sources and document collections. The paper evaluates the relevance model with the Wall Street Journal (WSJ) TREC test subcollection and a self-constructed fact database.     

Combining fields for query expansion and adaptive query expansion

In this paper, we aim to improve query expansion for ad-hoc retrieval, by proposing a more fine-grained term reweighting process. This fine-grained process uses statistics from the representation of documents in various fields, such as their titles, the anchor text of their incoming links, and their body content. The contribution of this paper is twofold: First, we propose a novel query expansion mechanism on fields by combining field evidence available in a corpora. Second, we propose an adaptive query expansion mechanism that selects an appropriate collection resource, either the local collection, or a high-quality external resource, for query expansion on a per-query basis. The two proposed query expansion approaches are thoroughly evaluated using two standard Text Retrieval Conference (TREC) Web collections, namely the WT10G collection and the large-scale .GOV2 collection. From the experimental results, we observe a statistically significant improvement compared with the baselines. Moreover, we conclude that the adaptive query expansion mechanism is very effective when the external collection used is much larger than the local collection.     

Elicitation and use of relevance feedback information

The paper presents two approaches to interactively refining user search formulations and their evaluation in the new High Accuracy Retrieval from Documents (HARD) track of TREC-12. The first method consists of asking the user to select a number of sentences that represent documents. The second method consists of showing to the user a list of noun phrases extracted from the initial document set. Both methods then expand the query based on the user feedback. The TREC results show that one of the methods is an effective means of interactive query expansion and yields significant performance improvements. The paper presents a comparison of the methods and detailed analysis of the evaluation results.     

Neural embedding-based specificity metrics for pre-retrieval query performance prediction

In information retrieval, the task of query performance prediction (QPP) is concerned with determining in advance the performance of a given query within the context of a retrieval model. QPP has an important role in ensuring proper handling of queries with varying levels of difficulty. Based on the extant literature, query specificity is an important indicator of query performance and is typically estimated using corpus-specific frequency-based specificity metrics However, such metrics do not consider term semantics and inter-term associations. Our work presented in this paper distinguishes itself by proposing a host of corpus-independent specificity metrics that are based on pre-trained neural embeddings and leverage geometric relations between terms in the embedding space in order to capture the semantics of terms and their interdependencies. Specifically, we propose three classes of specificity metrics based on pre-trained neural embeddings: neighborhood-based, graph-based, and cluster-based metrics. Through two extensive and complementary sets of experiments, we show that the proposed specificity metrics (1) are suitable specificity indicators, based on the gold standards derived from knowledge hierarchies (Wikipedia category hierarchy and DMOZ taxonomy), and (2) have better or competitive performance compared to the state of the art QPP metrics, based on both TREC ad hoc collections namely Robust’04, Gov2 and ClueWeb’09 and ANTIQUE question answering collection. The proposed graph-based specificity metrics, especially those that capture a larger number of inter-term associations, proved to be the most effective in both query specificity estimation and QPP. We have also publicly released two test collections (i.e. specificity gold standards) that we built from the Wikipedia and DMOZ knowledge hierarchies.     

Query reformulation using automatically generated query concepts from a document space

We propose a new query reformulation approach, using a set of query concepts that are introduced to precisely denote the user’s information need. Since a document collection is considered to be a domain which includes latent primitive concepts, we identify those concepts through a local pattern discovery and a global modeling using data mining techniques. For a new query, we select its most associated primitive concepts and choose the most probable interpretations as query concepts. We discuss the issue of constructing the primitive concepts from either the whole corpus or from the retrieved set of documents. Our experiments are performed on the TREC8 collection. The experimental evaluation shows that our approach is as good as current query reformulation approaches, while being particularly effective for poorly performing queries. Moreover, we find that the approach using the primitive concepts generated from the set of retrieved documents leads to the most effective performance.     

Re-examining the effects of adding relevance information in a relevance feedback environment

This paper presents an investigation about how to automatically formulate effective queries using full or partial relevance information (i.e., the terms that are in relevant documents) in the context of relevance feedback (RF). The effects of adding relevance information in the RF environment are studied via controlled experiments. The conditions of these controlled experiments are formalized into a set of assumptions that form the framework of our study. This framework is called idealized relevance feedback (IRF) framework. In our IRF settings, we confirm the previous findings of relevance feedback studies. In addition, our experiments show that better retrieval effectiveness can be obtained when (i) we normalize the term weights by their ranks, (ii) we select weighted terms in the top K retrieved documents, (iii) we include terms in the initial title queries, and (iv) we use the best query sizes for each topic instead of the average best query size where they produce at most five percentage points improvement in the mean average precision (MAP) value. We have also achieved a new level of retrieval effectiveness which is about 55–60% MAP instead of 40+% in the previous findings. This new level of retrieval effectiveness was found to be similar to a level using a TREC ad hoc test collection that is about double the number of documents in the TREC-3 test collection used in previous works.     

Quantifying the impact of concept recognition on biomedical information retrieval

In ad hoc querying of document collections, current approaches to ranking primarily rely on identifying the documents that contain the query terms. Methods such as query expansion, based on thesaural information or automatic feedback, are used to add further terms, and can yield significant though usually small gains in effectiveness. Another approach to adding terms, which we investigate in this paper, is to use natural language technology to annotate - and thus disambiguate - key terms by the concept they represent. Using biomedical research documents, we quantify the potential benefits of tagging users’ targeted concepts in queries and documents in domain-specific information retrieval. Our experiments, based on the TREC Genomics track data, both on passage and full-text retrieval, found no evidence that automatic concept recognition in general is of significant value for this task. Moreover, the issues raised by these results suggest that it is difficult for such disambiguation to be effective.     

Iterative exploration,design and evaluation of support for query reformulation in interactive information retrieval

We report on the progressive investigation of techniques for supporting interactive query reformulation in the TREC Interactive Track. Two major issues were explored over four successive years: methods of term suggestion; and, interface design to support different system functionalities. Each year's results led to the following year's investigation, with respect to both of these issues. This paper presents first the general motivation for the entire series of studies; then an overview of each year's investigation, its results, and how they influenced the next year's investigation. We discuss what has been learned through this series of investigations about effective term suggestion, usable and useful interface design, and the relationships between these two in support of the TREC Interactive Track task. We conclude with comments about the general methodology employed over this series of studies, and its relevance to the development and evaluation of interactive information retrieval systems.