Traditional Information Retrieval Models Research Articles

Conditional variational autoencoder for query expansion in ad-hoc information retrieval

Query expansion (QE) is commonly used to improve the performance of traditional information retrieval (IR) models. With the adoption of deep learning in IR research, neural QE models have emerged in recent years. Many of these models focus on learning embeddings by leveraging query-document relevance. These embedding models allow computing semantic similarities between queries and documents to generate expansion terms. However, existing models often ignore query-document interactions. This research aims to address that gap by proposing a QE model using a conditional variational autoencoder. It first maps a query-document pair into a latent space based on their interaction, then estimates an expansion model from that latent space. The proposed model is trained on relevance feedback data and generates expansions using pseudo-relevance feedback at test time. The proposed model is evaluated on three standard TREC collections for document ranking: AP and Robust 04 and GOV02, and the MS MARCO dataset for passage ranking. Results show the model outperforms state-of-the-art traditional and neural QE models. It also demonstrates higher additivity with neural matching than baselines.

Ontology-based hierarchical retrieval model for digital English teaching information

In order to overcome the low recall and precision of traditional English teaching information retrieval model, this paper designs a hierarchical retrieval model of digital English teaching information based on ontology. TF-IDF and simhash algorithm are used to judge the similarity of digital English teaching database documents and calculate the weight of English teaching information retrieval keywords. Using the relationship between different retrieval concepts to build a semantic network diagram, a hierarchical retrieval model of digital English teaching information was built, the retrieval results according to the user's interest were adjusted, and more accurate retrieval results are obtained. The experimental results show that the recall rate of the model is more than 94%, the precision rate is more than 96%, and the average retrieval time is only 0.44s, which shows that the recall rate and precision rate of the design model are higher, and the retrieval time is shorter.

An Empirical Study on Utilizing Neural Network for Event Information Retrieval

Event information retrieval (EIR) is the task of retrieving news articles in response to the event-oriented query, which tailors for dealing with a large scale of news articles on breaking news (e.g., natural disaster). However, the challenges caused by the dynamic and complex natures of the event make EIR less studied than traditional information retrieval (IR). The existing studies usually employed either traditional IR models or machine learning approaches over hand-crafted features to retrieve events, which fails to capture more-complex structures of events with event evolution well. To address this issue, we exploit neural models incorporating the characteristics of events to EIR task. Experimental results demonstrate the effectiveness of our method over two datasets, which can regard as the simple but strong baseline for further research in EIR.

An end-to-end pseudo relevance feedback framework for neural document retrieval

Pseudo relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches. While neural retrieval models have recently demonstrated promising results for ad-hoc document retrieval, combining them with PRF is not straightforward due to incompatibilities between existing PRF approaches and neural architectures. To bridge this gap, we propose an end-to-end neural PRF framework, coined NPRF, that enriches the representation of user information need from a single query to multiple PRF documents. NPRF can be used with existing neural IR models by embedding different neural models as building blocks. Three state-of-the-art neural retrieval models, including the unigram DRMM and KNRM models, and the position-aware PACRR model, are utilized to instantiate the NPRF framework. Extensive experiments on two standard test collections, TREC1-3 and Robust04, confirm the effectiveness of the proposed NPRF framework in improving the performance of three state-of-the-art neural IR models. In addition, analysis shows that integrating the existing neural IR models within the NPRF framework results in reduced training and validation losses, and consequently, improved effectiveness of the learned ranking functions.

Improving Arabic information retrieval using word embedding similarities

Term mismatch is a common limitation of traditional information retrieval (IR) models where relevance scores are estimated based on exact matching of documents and queries. Typically, good IR model should consider distinct but semantically similar words in the matching process. In this paper, we propose a method to incorporate word embedding (WE) semantic similarities into existing probabilistic IR models for Arabic in order to deal with term mismatch. Experiments are performed on the standard Arabic TREC collection using three neural word embedding models. The results show that extending the existing IR models improves significantly baseline bag-of-words models. Although the proposed extensions significantly outperform their baseline bag-of-words, the difference between the evaluated neural word embedding models is not statistically significant. Moreover, the overall comparison results show that our extensions significantly improve the Arabic WordNet based semantic indexing approach and three recent WE-based IR language models.

A Survey on Information Retrieval Models, Techniques and Applications

It has been long time many people have realized the importance of archiving and finding information. With the advent of computers, it became possible to store large amounts of information; and finding useful information from such collections became a necessity. Over the last forty years, Information Retrieval (IR) has matured considerably. Several IR systems are used on an everyday basis by a wide variety of users. Information retrieval (IR) is generally concerned with the searching and retrieving of knowledge-based information from database. In this paper, we will discuss about the various models and techniques and for information retrieval. We are also providing the overview of traditional IR models.

Diversifying Search Results through Pattern-Based Subtopic Modeling

Traditional information retrieval models do not necessarily provide users with optimal search experience because the top ranked documents may contain excessively redundant information. Therefore, satisfying search results should be not only relevant to the query but also diversified to cover different subtopics of the query. In this paper, the authors propose a novel pattern-based framework to diversify search results, where each pattern is a set of semantically related terms covering the same subtopic. They first apply a maximal frequent pattern mining algorithm to extract the patterns from retrieval results of the query. The authors then propose to model a subtopic with either a single pattern or a group of similar patterns. A profile-based clustering method is adapted to group similar patterns based on their context information. The search results are then diversified using the extracted subtopics. Experimental results show that the proposed pattern-based methods are effective to diversify the search results.

Modeling and mining term association for improving biomedical information retrieval performance

BackgroundThe growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance.ResultsWe propose a term association approach to discover term associations among the keywords from a query. The experiments are conducted on the TREC 2004-2007 Genomics data sets and the TREC 2004 HARD data set. The proposed approach is promising and achieves superiority over the baselines and the GSP results. The parameter settings and different indices are investigated that the sentence-based index produces the best results in terms of the document-level, the word-based index for the best results in terms of the passage-level and the paragraph-based index for the best results in terms of the passage2-level. Furthermore, the best term association results always come from the best baseline. The tuning number k in the proposed recursive re-ranking algorithm is discussed and locally optimized to be 10.ConclusionsFirst, modelling term association for improving biomedical information retrieval using factor analysis, is one of the major contributions in our work. Second, the experiments confirm that term association considering co-occurrence and dependency among the keywords can produce better results than the baselines treating the keywords independently. Third, the baselines are re-ranked according to the importance and reliance of latent factors behind term associations. These latent factors are decided by the proposed model and their term appearances in the first round retrieved passages.

Tensor Field Model for higher-order information retrieval

Abstract There is an implied assumption for keywords in the traditional Information Retrieval (IR) models: keywords are parallel to each other. In fact, there are some relations between terms in quite a few queries, and in these queries, perhaps one term is subordinate to another term according to the inner meanings of information needs. This is “Higher-order IR”(HIR) defined in this paper, and we call traditional IR “first-order IR” instead. Some research fields such as Public Opinion Analysis, Chain of Events Analysis and Trend Analysis which reflect the vague concept of HIR are all special form of HIR. Apparently, traditional IR models cannot deal with HIR directly. We need a new HIR model to represent and organize the documents, queries and relevance relationship between them. In this paper, we propose “Tensor Field Model” (TFM), and its perspectives are field theory in physics and multilinear algebra in maths. We construct the tensor representations of documents and queries in TFM, presenting some key concepts such as term field, tensor product of term array and term field constant. Empirical results show that TFM is appropriate for HIR theoretically and formally compared with traditional models which simplify the HIR problems as first-order IR problems to some extent.

A Service Search Engine for the Industrial Digital Ecosystems

Digital ecosystem (DE) is comprised of heterogeneous and distributed species which can play the dual role of service provider and service requester. Nowadays, DE lacks semantic search support, which means it cannot provide a reliable and trustworthy link between service providers and service requesters. To solve this issue, we design a conceptual framework of a service-ontology-based semantic service search engine. Apart from the function of service search with a novel search model, this framework also provides a quality-of-services-based service evaluation and ranking methodology. To evaluate the feasibility of our framework, we implement a prototype in the transport service domain, and compare the performance of the search model with three traditional information retrieval models. The conclusion to this evaluation and suggestions for future works are provided in the final section.

Lexical and Syntactic knowledge for Information Retrieval

Traditional Information Retrieval (IR) models assume that the index terms of queries and documents are statistically independent of each other, which is intuitively wrong. This paper proposes the incorporation of the lexical and syntactic knowledge generated by a POS-tagger and a syntactic Chunker into traditional IR similarity measures for including this dependency information between terms. Our proposal is based on theories of discourse structure by means of the segmentation of documents and queries into sentences and entities. Therefore, we measure dependencies between entities instead of between terms. Moreover, we handle discourse references for each entity. It has been evaluated on Spanish and English corpora as well as on Question Answering tasks obtaining significant increases.

Semi-supervised document retrieval

This paper proposes a new machine learning method for constructing ranking models in document retrieval. The method, which is referred to as SSR ank, aims to use the advantages of both the traditional Information Retrieval (IR) methods and the supervised learning methods for IR proposed recently. The advantages include the use of limited amount of labeled data and rich model representation. To do so, the method adopts a semi-supervised learning framework in ranking model construction. Specifically, given a small number of labeled documents with respect to some queries, the method effectively labels the unlabeled documents for the queries. It then uses all the labeled data to train a machine learning model (in our case, Neural Network). In the data labeling, the method also makes use of a traditional IR model (in our case, BM25). A stopping criterion based on machine learning theory is given for the data labeling process. Experimental results on three benchmark datasets and one web search dataset indicate that SSR ank consistently and almost always significantly outperforms the baseline methods (unsupervised and supervised learning methods), given the same amount of labeled data. This is because SSR ank can effectively leverage the use of unlabeled data in learning.

인터액티브 정보검색 모형

This paper is divided into two parts. The first part elaborates on four Information Retrieval (IR) models: a traditional IR model and three more recent, user-oriented models of IR interaction presented by Belkin, Ingwersen, and Saracevic. The strengths and limitations of each model are discussed. The second part, based on an analysis of the previous models, presents the author's interactive model, namely, the Iceberg Model. The rationales that are given to explain the design of this model are associated with the following: a greater specificity of system attributes; more concrete interplays among different components of IR interaction; and, the increased role of the Human Information Intermediary (HII). In sum, the new model presents a framework that can evolve in varying information-seeking contexts.

Two-handed volumetric document corpus management

To find a document in the sea of information, you must embark on a search process, usually computer-aided. In the traditional information retrieval model, the final goal is to identify and collect a small number of documents to read in detail. In this case, a single query yielding a scalar indication of relevance usually suffices. In contrast, document corpus management seeks to understand what is happening in the collection of documents as a whole (i.e. to find relationships among documents). You may indeed read or skim individual documents, but only to better understand the rest of the document set. Document corpus management seeks to identify trends, discover common links and find clusters of similar documents. The results of many single queries must be combined in various ways so that you can discover trends. We describe a new system called the Stereoscopic Field Analyzer (SFA) that aids in document corpus management by employing 3D volumetric visualization techniques in a minimally immersive real-time interaction style. This interactive information visualization system combines two-handed interaction and stereoscopic viewing with glyph-based rendering of the corpora contents. SFA has a dynamic hypertext environment for text corpora, called Telltale, that provides text indexing, management and retrieval based on n-grams (n character sequences of text). Telltale is a document management and information retrieval engine which provides document similarity measures (n-gram-based m-dimensional vector inner products) visualized by SFA for analyzing patterns and trends within the corpus.