Image Retrieval by Inter-media Fusion and Pseudo-relevance Feedback

Query Expansion using Pseudo Relevance Feedback is a useful and a popular technique for reformulating the query. In our proposed query expansion method, we assume that relevant information can be found within a document near the central idea. The document is normally divided into sections, paragraphs and lines. The proposed method tries to extract keywords that are closer to the central theme of the document. The expansion terms are obtained by equi-frequency partition of the documents obtained from pseudo relevance feedback and by using tf-idf scores. The idf factor is calculated for number of partitions in documents. The group of words for query expansion is selected using the following approaches: the highest score, average score and a group of words that has maximum number of keywords. As each query behaved differently for different methods, the effect of these methods in selecting the words for query expansion is investigated. From this initial study, we extend the experiment to develop a rule-based statistical model that automatically selects the best group of words incorporating the tf-idf scoring and the 3 approaches explained here, in the future. The experiments were performed on FIRE 2011 Adhoc Hindi and English test collections on 50 queries each, using Terrier as retrieval engine.

Vocabulary mismatch in Information retrieval can be solved by Query Expansion (QE) techniques. Relevance feedback is a prominent solution to improve recall of retrieval system. Sometimes user may be reluctant and novice in providing feedback to improve the retrieval performance. Pseudo Relevance Feedback (PRF) automates the process. PRF treats top ranked resultant items are relevant and uses them to expand the query, which is not always correct. PRF by local analysis does not give guarantee to feedback positive terms to the system. Use of global analysis to capture the positive feedback is a regular practice in information retrieval process. This paper addresses the limitations of local analysis and global analysis alone by a novel approach that integrates both PRF and WordNet to select good expansion terms. The proposed solution filters the expansion terms and optimizes the expanded query. The proposed work is carried out on a huge Telugu text corpus collected from Wikipedia and other Telugu daily news portals.

Microblog retrieval has received much attention in recent years due to the wide spread of social microblogging platforms such as Twitter. Many research studies investigated different approaches for microblog retrieval. Query expansion is one of the approaches that showed stable performance for improving microblog retrieval effectiveness. In this paper we comprehensively investigate query expansion for microblog retrieval using pseudo relevance feedback (PRF) with different configurations. In addition, we propose a hyperlink-extended PRF by expanding queries using embedded hyperlinks in retrieved microblogs. Our experimental results on TREC microblog data showed that PRF alone could outperform many retrieval approaches if configured properly. Results also showed that utilizing hyperlinked documents in tweets for query expansion improves results over traditional PRF. Our best results achieved using hyperlink-extended PRF outperformed most of the reported results using other approaches.

Query-document vocabulary mismatch, the lack of query expressiveness for user needs and the phenomenon of short queries are the main issues associated with information retrieval systems. Query Expansion (QE) is one of the well-known alternative for overcoming these problems. It mainly involves finding synonyms or related words for the query terms. There are several approaches in the query expansion field such as statistical and semantic approaches; they focus on expanding the individual query terms rather than the entire query during the expansion process. An other category of approaches deals with the whole query by using a neural approach based on Pseudo Relevance feedback (PRF) documents. In this work, we carried out an ablation study to measure the impact of the classical and semantic (word embedding, order, context) based query expansion on the retrieval performance. The experiments conducted on the Arabic EveTAR dataset reveal that our hybrid proposed approach combining classical (PRF) and transformer (AraBERT) is competitive with the state-of-the-art methods. In fact, the obtained result in terms of the Mean Average Precision (MAP) is up to 0.72.

In microblog retrieval, query expansion can be essential to obtain good search results due to the short size of queries and posts. Since information in microblogs is highly dynamic, an up-to-date index coupled with pseudo-relevance feedback (PRF) with an external corpus has a higher chance of retrieving more relevant documents and improving ranking. In this paper, we focus on the research question:how can we reduce the query expansion computational cost while maintaining the same retrieval precision as standard PRF? Therefore, we propose to accelerate the query expansion step of pseudo-relevance feedback. The hypothesis is that using an expansion corpus organized into verticals for expanding the query, will lead to a more efficient query expansion process and improved retrieval effectiveness. Thus, the proposed query expansion method uses a distributed search architecture and resource selection algorithms to provide an efficient query expansion process. Experiments on the TREC Microblog datasets show that the proposed approach can match or outperform standard PRF in MAP and NDCG@30, with a computational cost that is three orders of magnitude lower.

A semantic framework for enhancing pseudo-relevance feedback with soft negative sampling and contrastive learning

This paper introduced a LDA-based pseudo relevance feedback (PRF) model for cross language information retrieval. To validate the performance of PRF techniques in CLIR task, we conducted cross language query expansion experiments based on a self-constructed CLIR system, the LDA-based PRF model was applied before or after the query translating process, namely the pre-translation-PRF, the post-translation-PRF, and the combined-PRF strategy. We also compared this model with the classical VSM-based PRF algorithm. Experiment results showed that the proposed LDA-based PRF method was effective for improving the performance of CLIR.

The effect of low-level image features on pseudo relevance feedback

Automatic query expansion techniques are widely applied for improving text retrieval performance, using a variety of approaches that exploit several data sources for finding expansion terms. Selecting expansion terms is challenging and requires a framework capable of extracting term relationships. Recently, several Natural Language Processing methods, based on Deep Learning, are proposed for learning high quality vector representations of terms from a large amount of unstructured text with billions of words. These high quality vector representations capture a large number of term relationships. In this paper, we experimentally compare several expansion methods with expansion using these term vector representations. We use language models for information retrieval to evaluate expansion methods. Experiments conducted on four CLEF collections show a statistically significant improvement over the language models and other expansion models.

Humans are accustomed to autonomously associating prior knowledge with the text in a query when answering questions. However, for machines lacking cognition and common sense, a query is merely a combination of some words. Although we can enrich the semantic information of the given query through language representation or query expansion (QE) , the information contained in the query is still insufficient. In this paper, we propose an effective passage retrieval method named query context expansion-based retrieval (QCER) for open-domain question answering (OpenQA) . QCER associates a query with domain information by adding contextual association information based on the pseudo-relevance feedback (PRF) . QCER uses a dense reader to select top-n expansion terms for QE. We implement QCER by appending reader predictions, theoretically present in candidate passages, as contextual information to the initial query to form the new query. QCER with sparse representations (BM25) can improve retrieval efficiency and accelerate query convergence so that the reader can find the desired answer using fewer relevant passages, e.g., 10 passages, as soon as possible. Moreover, QCER can be easily combined with dense passage retrieval (DPR) to achieve even better performance, as sparse and dense representations are often complementary. Remarkably, we demonstrate that QCER achieves state-of-the-art performance in three tasks, passage retrieval, passage reading, and passage reranking, on the Natural Questions (NQ) and TriviaQA (Trivia) datasets under an extractive QA setup.

The paper presents several techniques for selecting noun phrases for interactive query expansion following pseudo-relevance feedback and a new phrase-based document ranking method. A combined syntactico-statistical method was used for the selection of phrases for query expansion. Several statistical measures of phrase selection were evaluated. Experiments were also conducted studying the effectiveness of noun phrases in document ranking. One of the major problems in phrase-based document retrieval is weighting of overlapping and non-contiguous word sequences in documents. The paper presents a new method of phrase weighting, which addressed this problem, and its evaluation on the TREC dataset.

Clinical decision support (CDS) search is performed to retrieve key medical literature that can assist the practice of medical experts by offering appropriate medical information relevant to the medical case in hand. In this paper, we present a novel CDS search framework designed for passage retrieval from biomedical textbooks in order to support clinical decision-making using laboratory test results. The framework utilizes two unique characteristics of the textual reports derived from the test results, which are syntax variation and negation information. The proposed framework consists of three components: domain ontology, index repository, and query processing engine. We first created a domain ontology to resolve syntax variation by applying the ontology to detect medical concepts from the test results with language translation. We then preprocessed and performed indexing of biomedical textbooks recommended by clinicians for passage retrieval. We finally built the query-processing engine tailored for CDS, including translation, concept detection, query expansion, pseudo-relevance feedback at the local and global levels, and ranking with differential weighting of negation information. To evaluate the effectiveness of the proposed framework, we followed the standard information retrieval evaluation procedure. An evaluation dataset was created, including 28,581 textual reports for 30 laboratory test results and 56,228 passages from widely used biomedical textbooks, recommended by clinicians. Overall, our proposed passage retrieval framework, GPRF-NEG, outperforms the baseline by 36.2, 100.5, and 69.7 percent for MRR, R-precision, and Precision at 5, respectively. Our study results indicate that the proposed CDS search framework specifically designed for passage retrieval of biomedical literature represents a practically viable choice for clinicians as it supports their decision-making processes by providing relevant passages extracted from the sources that they prefer to refer to, with improved performances.

In pseudo-relevance feedback, the two key factors affecting the retrieval performance most are the source from which expansion terms are generated and the method of ranking those expansion terms. In this paper, we present a novel unsupervised query expansion technique that utilizes keyphrases and POS phrase categorization. The keyphrases are extracted from the retrieved documents and weighted with an algorithm based on information gain and co-occurrence of phrases. The selected keyphrases are translated into Disjunctive Normal Form (DNF) based on the POS phrase categorization technique for better query refomulation. Furthermore, we study whether ontologies such as WordNet and MeSH improve the retrieval performance in conjunction with the keyphrases. We test our techniques on TREC 5, 6, and 7 as well as a MEDLINE collection. The experimental results show that the use of keyphrases with POS phrase categorization produces the best average precision.

Most effective particular object and image retrieval approaches are based on the bag-of-words (BoW) model, and all state-of-the-art performance mainly involves a query expansion procedure, which is able to significantly improve retrieval results. Nowadays, Convolutional Neural Network(CNN) is widely applied in computer vision field, including image classification, caption, recognition and retrieval, etc. We introduce an extension to query expansion: an automatic method to select good candidate samples for interactive annotation which is used in query expansion using both BoW method and CNN feature. In this work, we address the query expansion framework using active learning, where the main focus is on the sample selection step in the process of query expansion. More specifically, we propose an active sample selection algorithm based on binary relevance classification, based on the assumption that most confusing samples of the classifiers have high probability to contain helpful true positives for query expansion, which significantly improves the retrieval performance. It takes full use of the multimodal information of the shortlist obtained from the basic retrieval to train a binary relevance classifier, which is used to pick up the most confusing samples for human annotation, with top list as unlabeled data and bottom list as fake negatives. And it can achieve a faster and better retrieval than naive top sample selection method. We also fuse BoW vector and CNN prediction in the retrieval system for a better performance. To evaluate the performance of our proposed method, experiments are conducted on Standard Oxford (5K and 105K) and Paris (6K) datasets, and experimental results and comparison with the state-of-the-art methods demonstrate the effectiveness of the proposed method.

Pseudo-relevance feedback (PRF) has been proven to be an effective query expansion strategy to improve retrieval performance. Several PRF methods have so far been proposed for many retrieval models. Recent theoretical studies of PRF methods show that most of the PRF methods do not satisfy all necessary constraints. Among all, the log-logistic model has been shown to be an effective method that satisfies most of the PRF constraints. In this paper, we first introduce two new PRF constraints. We further analyze the log-logistic feedback model and show that it does not satisfy these two constraints as well as the previously proposed "relevance effect" constraint. We then modify the log-logistic formulation to satisfy all these constraints. Experiments on three TREC newswire and web collections demonstrate that the proposed modification significantly outperforms the original log-logistic model, in all collections.