Proximity Full-Text Search with a Response Time Guarantee by Means of Additional Indexes

Full-text search engines are important tools for information retrieval. Term proximity is an important factor in relevance score measurement. In a proximity full-text search, we assume that a relevant document contains query terms near each other, especially if the query terms are frequently occurring words. A methodology for high-performance full-text query execution is discussed. We build additional indexes to achieve better efficiency. For a word that occurs in the text, we include in the indexes some information about nearby words. What types of additional indexes do we use? How do we use them? These questions are discussed in this work. We present the results of experiments showing that the average time of search query execution is 44-45 times less than that required when using ordinary inverted indexes. This is a pre-print of a contribution "Veretennikov A.B. Proximity Full-Text Search with a Response Time Guarantee by Means of Additional Indexes" published in "Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868" published by Springer, Cham. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-01054-6_66. The work was supported by Act 211 Government of the Russian Federation, contract no 02.A03.21.0006.Comment: Alexander B. Veretennikov. Chair of Calculation Mathematics and Computer Science, INSM. Ural Federal Universit

A study of search intermediary working notes: implications for IR system design

This paper reports findings from an exploratory study investigating working notes created during encoding and external storage (EES) processes, by human search intermediates using a Boolean information retrieval (JR) system. EES processes have been an important area of research in educational contexts where students create and use notes to facilitate learning. In the context of interactive IR, encoding can be conceptualized as the process of creating working notes to help in the understanding and translating a user's information problem into a search strategy suitable for use with an IR system. External storage is the process of using working notes to facilitate interaction with IR systems. Analysis of 221 sets of working notes created by human search intermediaries revealed extensive use of EES processes and the creation of working notes of textual, numerical and graphical entities. Nearly 70% of recorded working notes were textual/numerical entities, nearly 30% were graphical entities and 0.73% were indiscernible. Segmentation devices were also used in 48% of the working notes. The creation of working notes during EES processes was a fundamental element within the mediated, interactive IR process. Implications for the design of IR interfaces to support users' EES processes and further research is discussed

Relevance-based Word Embedding

Learning a high-dimensional dense representation for vocabulary terms, also known as a word embedding, has recently attracted much attention in natural language processing and information retrieval tasks. The embedding vectors are typically learned based on term proximity in a large corpus. This means that the objective in well-known word embedding algorithms, e.g., word2vec, is to accurately predict adjacent word(s) for a given word or context. However, this objective is not necessarily equivalent to the goal of many information retrieval (IR) tasks. The primary objective in various IR tasks is to capture relevance instead of term proximity, syntactic, or even semantic similarity. This is the motivation for developing unsupervised relevance-based word embedding models that learn word representations based on query-document relevance information. In this paper, we propose two learning models with different objective functions; one learns a relevance distribution over the vocabulary set for each query, and the other classifies each term as belonging to the relevant or non-relevant class for each query. To train our models, we used over six million unique queries and the top ranked documents retrieved in response to each query, which are assumed to be relevant to the query. We extrinsically evaluate our learned word representation models using two IR tasks: query expansion and query classification. Both query expansion experiments on four TREC collections and query classification experiments on the KDD Cup 2005 dataset suggest that the relevance-based word embedding models significantly outperform state-of-the-art proximity-based embedding models, such as word2vec and GloVe.Comment: to appear in the proceedings of The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '17

Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search

Retrieval pipelines commonly rely on a term-based search to obtain candidate records, which are subsequently re-ranked. Some candidates are missed by this approach, e.g., due to a vocabulary mismatch. We address this issue by replacing the term-based search with a generic k-NN retrieval algorithm, where a similarity function can take into account subtle term associations. While an exact brute-force k-NN search using this similarity function is slow, we demonstrate that an approximate algorithm can be nearly two orders of magnitude faster at the expense of only a small loss in accuracy. A retrieval pipeline using an approximate k-NN search can be more effective and efficient than the term-based pipeline. This opens up new possibilities for designing effective retrieval pipelines. Our software (including data-generating code) and derivative data based on the Stack Overflow collection is available online

Reverse spatial visual top-k query

With the wide application of mobile Internet techniques an location-based services (LBS), massive multimedia data with geo-tags has been generated and collected. In this paper, we investigate a novel type of spatial query problem, named reverse spatial visual top- $k$ query (RSVQ k ) that aims to retrieve a set of geo-images that have the query as one of the most relevant geo-images in both geographical proximity and visual similarity. Existing approaches for reverse top- $k$ queries are not suitable to address this problem because they cannot effectively process unstructured data, such as image. To this end, firstly we propose the definition of RSVQ k problem and introduce the similarity measurement. A novel hybrid index, named VR 2 -Tree is designed, which is a combination of visual representation of geo-image and R-Tree. Besides, an extension of VR 2 -Tree, called CVR 2 -Tree is introduced and then we discuss the calculation of lower/upper bound, and then propose the optimization technique via CVR 2 -Tree for further pruning. In addition, a search algorithm named RSVQ k algorithm is developed to support the efficient RSVQ k query. Comprehensive experiments are conducted on four geo-image datasets, and the results illustrate that our approach can address the RSVQ k problem effectively and efficiently