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Academic literature on the topic 'TEXT RETRIEVAL METHODS'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "TEXT RETRIEVAL METHODS"

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Dik Lun Lee, Young Man Kim, and Gaurav Patel. "Efficient signature file methods for text retrieval." IEEE Transactions on Knowledge and Data Engineering 7, no. 3 (June 1995): 423–35. http://dx.doi.org/10.1109/69.390248.

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Kando, Noriko, Kyo Kageura, Masaharu Yoshioka, and Keizo Oyama. "Phrase processing methods for Japanese text retrieval." ACM SIGIR Forum 32, no. 2 (September 1998): 23–28. http://dx.doi.org/10.1145/305110.305120.

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Chute, C. G., and Y. Yang. "An Overview of Statistical Methods for the Classification and Retrieval of Patient Events." Methods of Information in Medicine 34, no. 01/02 (1995): 104–10. http://dx.doi.org/10.1055/s-0038-1634570.

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Abstract:Statistical methods that can support text retrieval are becoming an increasing focus of medical informatics activities. We overview our adaptation of existing knowlege sources to create pseudo-documents for concept based latent semantic indexing. Experience demonstrated this tack of limited practical value, since retrieval performance was invariably unsatisfactory. We discovered this was due in part to the introduction of a vocabulary gap between the queries and the cases we sought to retrieve. In part to address this problem, and to avail our large body of humanly coded text as a knowledge source, we developed a least squares fit alternative for the computer assisted indexing and retrieval of biomedical texts. This technique demonstrates equivalent or superior retrieval performance when compared to all other textual retrieval techniques. It does not depend upon elaborate knowledge bases, lexicons, or thesauri. It is a promising technique for classifying and retrieving the large volumes of clinical text.

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Rautray, Rasmita, Lopamudra Swain, Rasmita Dash, and Rajashree Dash. "A brief review on text summarization methods." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 728. http://dx.doi.org/10.14419/ijet.v7i4.5.25070.

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In present scenario, text summarization is a popular and active field of research in both the Information Retrieval (IR) and Natural Language Processing (NLP) communities. Summarization is important for IR since it is a means to identify useful information by condensing the document from large corpus of data in an efficient way. In this study, different aspects of text summarization methods with strength, limitation and gap within the methods are presented.

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Srinivasa Reddy, K., R. Anandan, K. Kalaivani, and P. Swaminathan. "A comprehensive survey on content based image retrieval system and its application in medical domain." International Journal of Engineering & Technology 7, no. 2.31 (May 29, 2018): 181. http://dx.doi.org/10.14419/ijet.v7i2.31.13436.

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Content Based Image Retrieval (CBIR) is an important and widely used technique for retrieval of different kinds of images from large database. Collection of information in database are available in different formats such as text, image, graph, chart etc. Here, our focus is on information which is available in the form of images. Searching and retrieval of the image from a large amount of database is difficult problem because it uses the image visual information such as shape, text and color for indexing and representation of an image. For efficient CBIR system, there is a need to develop different kinds of retrieval methods using feature extraction, similarity matching etc. Text Based Image Retrieval systems are used in many hospitals, but for large databases these are inefficient. To solve this problem, CBIR systems are proposed to retrieve matching images from database using automated feature extraction method. At present, medical imaging field finds extensive growth in the generation and evaluation of various types of medical images which are high inconsistency, usually fused and the combination of various minor composition structures. For easy retrieval, need to be development of feature extraction and image classification methods. Different methods are used for different kinds of medical images. The Radiology department and Cardiology department are the largest producers of medical images and the patient abnormal images can be stored with the normal images. CBIR uses query image as input and it retrieves the images, which are similar to the query more efficiently and effectively. This paper provides a comprehensive Survey about CBIR system and its one of the major application in medical domain.

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Suhartono, Didit, and Khodirun Khodirun. "System of Information Feedback on Archive Using Term Frequency-Inverse Document Frequency and Vector Space Model Methods." IJIIS: International Journal of Informatics and Information Systems 3, no. 1 (March 1, 2020): 36–42. http://dx.doi.org/10.47738/ijiis.v3i1.6.

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The archive is one of the examples of documents that important. Archives are stored systematically with a view to helping and simplifying the storage and retrieval of the archive. In the information retrieval (Information retrieval) the process of retrieving relevant documents and not retrieving documents that are not relevant. To retrieve the relevant documents, a method is needed. Using the Term Frequency-Inverse Document and Vector Space Model methods can find relevant documents according to the level of closeness or similarity, in addition to applying the Nazief-Adriani stemming algorithm can improve information retrieval performance by transforming words in a document or text to the basic word form. then the system indexes the document to simplify and speed up the search process. Relevance is determined by calculating the similarity values between existing documents by querying and represented in certain forms. The documents obtained, then the system sort by the level of relevance to the query.

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Hui, Fan, Guo Jie, and Jin Jiang Li. "New Research Progress in Image Retrieval." Applied Mechanics and Materials 333-335 (July 2013): 1076–79. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1076.

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Image retrieval is generally divided into two categories: one is text-based Image Retrieval; another is content-based Image Retrieval. Early image retrieval technology is mainly based on the text, after 90 years, the content-based image retrieval emerged. So far, we mainly use image retrieval technology that based on color, texture, layout analysis and retrieval. That is: content-based Image Retrieval (CBIR). This paper review the two kinds of image retrieval methods, and introduces a variety of techniques in content-based image retrieval, we also prospect of fusion research of text and content.

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Liu, Zhiqiang, Jingkun Feng, Zhihao Yang, and Lei Wang. "Document Retrieval for Precision Medicine Using a Deep Learning Ensemble Method." JMIR Medical Informatics 9, no. 6 (June 29, 2021): e28272. http://dx.doi.org/10.2196/28272.

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Background With the development of biomedicine, the number of biomedical documents has increased rapidly bringing a great challenge for researchers trying to retrieve the information they need. Information retrieval aims to meet this challenge by searching relevant documents from abundant documents based on the given query. However, sometimes the relevance of search results needs to be evaluated from multiple aspects in specific retrieval tasks, thereby increasing the difficulty of biomedical information retrieval. Objective This study aimed to find a more systematic method for retrieving relevant scientific literature for a given patient. Methods In the initial retrieval stage, we supplemented query terms through query expansion strategies and applied query boosting to obtain an initial ranking list of relevant documents. In the re-ranking phase, we employed a text classification model and relevance matching model to evaluate documents from different dimensions and then combined the outputs through logistic regression to re-rank all the documents from the initial ranking list. Results The proposed ensemble method contributed to the improvement of biomedical retrieval performance. Compared with the existing deep learning–based methods, experimental results showed that our method achieved state-of-the-art performance on the data collection provided by the Text Retrieval Conference 2019 Precision Medicine Track. Conclusions In this paper, we proposed a novel ensemble method based on deep learning. As shown in the experiments, the strategies we used in the initial retrieval phase such as query expansion and query boosting are effective. The application of the text classification model and relevance matching model better captured semantic context information and improved retrieval performance.

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KIKUCHI, Hirosato. "Progress in Literature Retrieval Methods and Appearance of Full Text Electronic Journal." Igaku Toshokan 50, no. 3 (2003): 226–29. http://dx.doi.org/10.7142/igakutoshokan.50.226.

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Ayyavaraiah, Monelli, and Dr Bondu Venkateswarlu. "Joint graph regularization based semantic analysis for cross-media retrieval: a systematic review." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 257. http://dx.doi.org/10.14419/ijet.v7i2.7.10592.

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The large number of heterogeneous data are rapidly increasing in the internet and most data consist of audio, video, text and images. The searching of the required data from the large database is difficult and time taking process. The single media retrieval is used to get the needed data from the large dataset and it has the drawback, it can only retrieve the single media only. If the query is given as the text and acquired result are present in text. The users demand the cross-media retrieval for their queries and it is very consistent in providing the result. This helps the users to get more information regarding to their queries. Finding the similarities between the heterogeneous data is very complex. Many research is done on the cross-media retrieval with different methods and provide the different result. The aim is to analysis the different cross-media retrieval with the joint graph regularization (JGR) to understand the various technique. The most of researches are using the parameter of MAP, precision and recall for their research.

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Dissertations / Theses on the topic "TEXT RETRIEVAL METHODS"

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Al, Tayyar Musaid Seleh. "Arabic information retrieval system based on morphological analysis (AIRSMA) : a comparative study of word, stem, root and morpho-semantic methods." Thesis, De Montfort University, 2000. http://hdl.handle.net/2086/4126.

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Tarczyńska, Anna. "Methods of Text Information Extraction in Digital Videos." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2656.

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Context The huge amount of existing digital video files needs to provide indexing to make it available for customers (easier searching). The indexing can be provided by text information extraction. In this thesis we have analysed and compared methods of text information extraction in digital videos. Furthermore, we have evaluated them in the new context proposed by us, namely usefulness in sports news indexing and information retrieval. Objectives The objectives of this thesis are as follows: providing a better understanding of the nature of text extraction; performing a systematic literature review on various methods of text information extraction in digital videos of TV sports news; designing and executing an experiment in the testing environment; evaluating available and promising methods of text information extraction from digital video files in the proposed context associated with video sports news indexing and retrieval; providing an adequate solution in the proposed context described above. Methods This thesis consists of three research methods: Systematic Literature Review, Video Content Analysis with the checklist, and Experiment. The Systematic Literature Review has been used to study the nature of text information extraction, to establish the methods and challenges, and to specify the effective way of conducting the experiment. The video content analysis has been used to establish the context for the experiment. Finally, the experiment has been conducted to answer the main research question: How useful are the methods of text information extraction for indexation of video sports news and information retrieval? Results Through the Systematic Literature Review we identified 29 challenges of the text information extraction methods, and 10 chains between them. We extracted 21 tools and 105 different methods, and analyzed the relations between them. Through Video Content Analysis we specified three groups of probability of text extraction from video, and 14 categories for providing video sports news indexation with the taxonomy hierarchy. We have conducted the Experiment on three videos files, with 127 frames, 8970 characters, and 1814 words, using the only available MoCA tool. As a result, we reported 10 errors and proposed recommendations for each of them. We evaluated the tool according to the categories mentioned above and offered four advantages, and nine disadvantages of the Tool mentioned above. Conclusions It is hard to compare the methods described in the literature, because the tools are not available for testing, and they are not compared with each other. Furthermore, the values of recall and precision measures highly depend on the quality of the text contained in the video. Therefore, performing the experiments on the same indexed database is necessary. However, the text information extraction is time consuming (because of huge amount of frames in video), and even high character recognition rate gives low word recognition rate. Therefore, the usefulness of text information extraction for video indexation is still low. Because most of the text information contained in the videos news is inserted in post-processing, the text extraction could be provided in the root: during the processing of the original video, by the broadcasting company (e.g. by automatically saving inserted text in separate file). Then the text information extraction will not be necessary for managing the new video files
The huge amount of existing digital video files needs to provide indexing to make it available for customers (easier searching). The indexing can be provided by text information extraction. In this thesis we have analysed and compared methods of text information extraction in digital videos. Furthermore, we have evaluated them in the new context proposed by us, namely usefulness in sports news indexing and information retrieval.

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Bhattacharya, Sanmitra. "Computational methods for mining health communications in web 2.0." Diss., University of Iowa, 2014. https://ir.uiowa.edu/etd/4576.

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Data from social media platforms are being actively mined for trends and patterns of interests. Problems such as sentiment analysis and prediction of election outcomes have become tremendously popular due to the unprecedented availability of social interactivity data of different types. In this thesis we address two problems that have been relatively unexplored. The first problem relates to mining beliefs, in particular health beliefs, and their surveillance using social media. The second problem relates to investigation of factors associated with engagement of U.S. Federal Health Agencies via Twitter and Facebook. In addressing the first problem we propose a novel computational framework for belief surveillance. This framework can be used for 1) surveillance of any given belief in the form of a probe, and 2) automatically harvesting health-related probes. We present our estimates of support, opposition and doubt for these probes some of which represent true information, in the sense that they are supported by scientific evidence, others represent false information and the remaining represent debatable propositions. We show for example that the levels of support in false and debatable probes are surprisingly high. We also study the scientific novelty of these probes and find that some of the harvested probes with sparse scientific evidence may indicate novel hypothesis. We also show the suitability of off-the-shelf classifiers for belief surveillance. We find these classifiers are quite generalizable and can be used for classifying newly harvested probes. Finally, we show the ability of harvesting and tracking probes over time. Although our work is focused in health care, the approach is broadly applicable to other domains as well. For the second problem, our specific goals are to study factors associated with the amount and duration of engagement of organizations. We use negative binomial hurdle regression models and Cox proportional hazards survival models for these. For Twitter, the hurdle analysis shows that presence of user-mention is positively associated with the amount of engagement while negative sentiment has inverse association. Content of tweets is also equally important for engagement. The survival analyses indicate that engagement duration is positively associated with follower count. For Facebook, both hurdle and survival analyses show that number of page likes and positive sentiment are correlated with higher and prolonged engagement while few content types are negatively correlated with engagement. We also find patterns of engagement that are consistent across Twitter and Facebook.

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Das, Manirupa. "Neural Methods Towards Concept Discovery from Text via Knowledge Transfer." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1572387318988274.

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Marakani, Sumeesha. "Employee Matching Using Machine Learning Methods." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18493.

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Background: Expertise retrieval is an information retrieval technique that focuses on techniques to identify the most suitable ’expert’ for a task from a list of individuals. Objectives: This master thesis is a collaboration with Volvo Cars to attempt applying this concept and match employees based on information that was extracted from an internal tool of the company. In this tool, the employees describe themselves in free-flowing text. This text is extracted from the tool and analyzed using Natural Language Processing (NLP) techniques. Methods: Through the course of this project, various techniques are employed and experimented with to study, analyze and understand the unlabelled textual data using NLP techniques. Through the course of the project, we try to match individuals based on information extracted from these techniques using Unsupervised MachineLearning methods (K-means clustering).Results. The results obtained from applying the various NLP techniques are explained along with the algorithms that are implemented. Inferences deduced about the properties of the data and methodologies are discussed. Conclusions: The results obtained from this project have shown that it is possible to extract patterns among people based on free-text data written about them. The future aim is to incorporate the semantic relationship between the words to be able to identify people who are similar and dissimilar based on the data they share about themselves.

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ABEYSINGHE, RUVINI PRADEEPA. "SIGNATURE FILES FOR DOCUMENT MANAGEMENT." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin990539054.

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Vasireddy, Jhansi Lakshmi. "Applications of Linear Algebra to Information Retrieval." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/71.

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Some of the theory of nonnegative matrices is first presented. The Perron-Frobenius theorem is highlighted. Some of the important linear algebraic methods of information retrieval are surveyed. Latent Semantic Indexing (LSI), which uses the singular value de-composition is discussed. The Hyper-Text Induced Topic Search (HITS) algorithm is next considered; here the power method for finding dominant eigenvectors is employed. Through the use of a theorem by Sinkohrn and Knopp, a modified HITS method is developed. Lastly, the PageRank algorithm is discussed. Numerical examples and MATLAB programs are also provided.

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Wiklund-Hörnqvist, Carola. "Brain-based teaching : behavioral and neuro-cognitive evidence for the power of test-enhanced learning." Doctoral thesis, Umeå universitet, Institutionen för psykologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-96395.

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A primary goal of education is the acquisition of durable knowledge which challenges the use of efficient pedagogical methods of how to best facilitate learning. Research in cognitive psychology has demonstrated that repeated testing during the learning phase improves performance on later retention tests compared to restudy of material. This empirical phenomenon is called the testing effect. The testing effect has shown to be robust across different kinds of material and when compared to different pedagogical methods. Despite the extensive number of published papers on the testing effect, the majority of the studies have been conducted in the laboratory. More specific, few studies have examined the testing effect in authentic settings when using course material during the progress of a course. Further, few studies have investigated the beneficial effects with test-enhanced learning by the use of neuroimaging methods (e.g. fMRI). The aim with the thesis was to investigate the effects of test-enhanced learning in an authentic educational context and how this is related to individual differences in working memory capacity (Study I and II) as well as changes in brain activity involved in successful repeated testing and long term retention (Study III). In study I, we examined whether repeated testing with feedback benefitted learning compared to rereading of introductory psychology key concepts in a sample of undergraduate students. The results revealed that repeated testing with feedback was superior compared to rereading both immediate after practice and at longer delays. The effect of repeated testing was beneficial for students irrespectively of WMC. In Study II, we investigated test-enhanced learning in relation to the encoding variability hypothesis for the learning of mathematics in a sample of fifth-grade children. Learning was examined in relation to both practiced and transfer tasks. No differences were found for the practiced tasks. Regarding the transfer tasks, the results gave support for the encoding variability hypothesis, but only at the immediate test. In contrast, when we followed up the durability of learning across time, the results showed that taking the same questions over and over again during the intervention resulted in better performance across time compared to variable encoding. Individual differences in WMC predicted performance on the transfer tasks, but only at the immediate test, regardless of group. Together, the results from Study I and Study II clearly indicate that testenhanced learning is effective in authentic settings, across age-groups and also produces transfer. Integrate current findings from cognitive science, in terms of test-enhanced learning, by the use of authentic materials and assessments relevant for educational goals can be rather easily done with vi computer based tasks. The observed influence of individual differences in WMC between the studies warrant further study of its specific contribution to be able to optimize the learning procedure. In Study III, we tested the complementary hypothesis regarding the mechanisms behind memory retrieval. Recurrent retrieval may be efficient because it induces representational consistency or, alternatively, because it induces representational variability - the altering or adding of underlying representations as a function of successful repeated retrieval. A cluster in right superior parietal cortex was identified as important for items successfully repeatedly retrieved Day 1, and also correctly remembered Day 7, compared to those successfully repeatedly retrieved Day 1 but forgotten Day 7. Representational similarity analysis in this region gave support for the theoretical explanations that emphasis semantic elaboration.

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Liang, Tyne, and 梁婷. "The Study of Character-based Signature Methods in Chinese Text Retrieval." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/77873455363722672863.

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博士
國立交通大學
資訊工程研究所
83
Many Chinese text access methods use characters instead of words as the basic search units and treat polysyllabic queries as conjunctive combinations of their constituent characters. Therefore if no character sequence information is incorporated in the search algorithm, one may retrieve an adjacency false hit which is a document containing all the characters of a polysyllabic query but not in the exact character sequence as in the query itself. In search of a good character-based Chinese text retrieval methods, the relation of adjacency false hit to the construction of polysyllabic words in Chinese is examined. On the other hand, the extra storage overhead and processing time needed to eliminate adjacency false hits for commonly-used character-based text access methods (inversion and signature) are estimated. It turns out that signature method is more promising than the inversion method for its less space overhead and easy support for adjacency operation in Chinese text retrieval. However, signature-based access may retrieve those documents which do not contain all the keys of search term. In this thesis, the origin of random false hits is investigated and more realistic estimation of random false hit probability is derived for Chinese disyllabic and trisyllabic terms. To construct a Chinese signature file, a special scheme (combined scheme) is proposed in which every character (monogram ) and character pair (bigram) in the document is hashed to the document signature. For disyllabic queries, an analytical expression of the false hit rate is found. With this expression, the optimal monogram and bigram weight assignments are obtained in terms of the signature length, the storage overhead , as well as the occurrence frequency and the association value of the query.

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Kuan-MingChou and 周冠銘. "Using automatic keywords extraction and text clustering methods for medical information retrieval improvement." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/80362319360586009723.

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碩士
國立成功大學
醫學資訊研究所
101
Because there are huge data on the web, it will get many duplicate and near-duplicate search results when we search on the web. The motivation of this thesis is that reduce the time of filtering the huge duplicate and near-duplicate information when user search. In this thesis, we propose a novel clustering method to solve near-duplicate problem. Our method transforms each document to a feature vector, where the weights are terms frequency of each corresponding words. For reducing the dimension of these feature vectors, we used principle component analysis to transform these vectors to another space. After PCA, we used cosine similarity to compute the similarity of each document. And then, we used EM algorithm and Neyman-Pearson hypothesis test to cluster the duplicate documents. We compared out results with K-means method results. The experiments show that our method is outperformer than K-means method.

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Books on the topic "TEXT RETRIEVAL METHODS"

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Pirkola, Ari. Studies on linguistic problems and methods in text retrieval: The effects of anaphor and ellipsis resolution in proximity searching, and translation and query structuring methods in cross-language retrieval. Tampere: University of Tampere, 1999.

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Kenney, Anne R. Tutorial: Digital resolution requirements for replacing text-based material : methods for benchmarking image quality. Washington, DC: Commission on Preservation and Access, 1995.

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Thomas S. Morton Grant S. Ingersoll. Taming Text: How to Find, Organize, and Manipulate It. [S.l.]: Manning Publications, 2012.

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Argamon, Shlomo. Computational methods for counterterrorism. Dordrecht: Springer, 2009.

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Jiang, Dongwei. The methods of analyzing retrieved document sets in information retrieval. 1993.

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Text Mining: Predictive Methods for Analyzing Unstructured Information. Springer, 2004.

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Indurkhya, Nitin, Tong Zhang, F. J. Damerau, and Sholom M. M. Weiss. Text Mining: Predictive Methods for Analyzing Unstructured Information. Springer, 2010.

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Chapman, Stephen, and Anne R. Kenney. Tutorial: Digital Resolution Requirements for Replacing Text-Based Material: Methods for Benchmarking Image Quality. Council on Library & Information Resources, 1995.

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Howard, Newton, and Shlomo Argamon. Computational Methods for Counterterrorism. Springer, 2010.

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Howard, Newton, and Shlomo Argamon. Computational Methods for Counterterrorism. Springer, 2014.

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Book chapters on the topic "TEXT RETRIEVAL METHODS"

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Cardoso-Cachopo, Ana, and Arlindo L. Oliveira. "An Empirical Comparison of Text Categorization Methods." In String Processing and Information Retrieval, 183–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39984-1_14.

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Kumar Bhadani, Abhay, and Ankur Narang. "Information Retrieval Methods for Big Data Analytics on Text." In Data Analytics, 73–90. Boca Raton, FL : CRC Press/Taylor & Francis Group, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429446177-4.

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Bellogín, Alejandro, Jun Wang, and Pablo Castells. "Text Retrieval Methods for Item Ranking in Collaborative Filtering." In Lecture Notes in Computer Science, 301–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20161-5_30.

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Dvorský, Jiří, Jaroslav Pokorný, and Václav Snášel. "Word-Based Compression Methods and Indexing for Text Retrieval Systems." In Advances in Databases and Information Systems, 76–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48252-0_6.

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Skorkovská, Lucie. "Score Normalization Methods for Relevant Documents Selection for Blind Relevance Feedback in Speech Information Retrieval." In Text, Speech, and Dialogue, 316–24. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24033-6_36.

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Alpkocak, Adil, Deniz Kilinc, and Tolga Berber. "Expansion and Re–ranking Approaches for Multimodal Image Retrieval using Text–based Methods." In ImageCLEF, 261–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15181-1_14.

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Tollari, Sabrina, Philippe Mulhem, Marin Ferecatu, Hervé Glotin, Marcin Detyniecki, Patrick Gallinari, Hichem Sahbi, and Zhong-Qiu Zhao. "A Comparative Study of Diversity Methods for Hybrid Text and Image Retrieval Approaches." In Lecture Notes in Computer Science, 585–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04447-2_72.

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Losee, Robert M. "The Quality of a Ranking Method." In Text Retrieval and Filtering, 93–109. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5705-0_5.

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van Bakel, Ruud, Teodor Aleksiev, Daniel Daza, Dimitrios Alivanistos, and Michael Cochez. "Approximate Knowledge Graph Query Answering: From Ranking to Binary Classification." In Lecture Notes in Computer Science, 107–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72308-8_8.

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AbstractLarge, heterogeneous datasets are characterized by missing or even erroneous information. This is more evident when they are the product of community effort or automatic fact extraction methods from external sources, such as text. A special case of the aforementioned phenomenon can be seen in knowledge graphs, where this mostly appears in the form of missing or incorrect edges and nodes.Structured querying on such incomplete graphs will result in incomplete sets of answers, even if the correct entities exist in the graph, since one or more edges needed to match the pattern are missing. To overcome this problem, several algorithms for approximate structured query answering have been proposed. Inspired by modern Information Retrieval metrics, these algorithms produce a ranking of all entities in the graph, and their performance is further evaluated based on how high in this ranking the correct answers appear.In this work we take a critical look at this way of evaluation. We argue that performing a ranking-based evaluation is not sufficient to assess methods for complex query answering. To solve this, we introduce Message Passing Query Boxes (MPQB), which takes binary classification metrics back into use and shows the effect this has on the recently proposed query embedding method MPQE.

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López, Franco Rojas, Héctor Jiménez-Salazar, and David Pinto. "A Competitive Term Selection Method for Information Retrieval." In Computational Linguistics and Intelligent Text Processing, 468–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-70939-8_41.

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Conference papers on the topic "TEXT RETRIEVAL METHODS"

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Mokoena, Thato, and Deon Sabatta. "User Classification by Keystroke Dynamics using Text Retrieval Methods." In 2020 International SAUPEC/RobMech/PRASA Conference. IEEE, 2020. http://dx.doi.org/10.1109/saupec/robmech/prasa48453.2020.9040956.

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"Clustering and Classifying Text Documents - A Revisit to Tagging Integration Methods." In International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004545201600168.

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Alksher, Mostafa A., Azreen Azman, Razali Yaakob, Rabiah Abdul Kadir, Abdulmajid Mohamed, and Eissa M. Alshari. "A review of methods for mining idea from text." In 2016 Third International Conference on Information Retrieval and Knowledge Management (CAMP). IEEE, 2016. http://dx.doi.org/10.1109/infrkm.2016.7806341.

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Li, Zhanjun, Victor Raskin, and Karthik Ramani. "Developing Ontologies for Engineering Information Retrieval." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34530.

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When engineering content is created and applied during the product lifecycle, it is often stored and forgotten. Since search remains text-based, engineers do not have the means to harness and reuse past designs and experiences. On the other hand, current information retrieval approaches based on statistical methods and keyword matching are not directly applicable to the engineering domain. We propose a new computational framework that includes an ontological basis and algorithms to retrieve unstructured engineering documents while handling complex queries. The results from the preliminary test demonstrate that our method outperforms the traditional keyword-based search with respect to the standard information retrieval measurement.

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Chen, Jianan, Lu Zhang, Cong Bai, and Kidiyo Kpalma. "Review of Recent Deep Learning Based Methods for Image-Text Retrieval." In 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2020. http://dx.doi.org/10.1109/mipr49039.2020.00042.

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Levin, Roy, and Haggai Roitman. "Enhanced Probabilistic Classify and Count Methods for Multi-Label Text Quantification." In ICTIR '17: ACM SIGIR International Conference on the Theory of Information Retrieval. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3121050.3121083.

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Feng, Zerun, Zhimin Zeng, Caili Guo, and Zheng Li. "Exploiting Visual Semantic Reasoning for Video-Text Retrieval." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/140.

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Video retrieval is a challenging research topic bridging the vision and language areas and has attracted broad attention in recent years. Previous works have been devoted to representing videos by directly encoding from frame-level features. In fact, videos consist of various and abundant semantic relations to which existing methods pay less attention. To address this issue, we propose a Visual Semantic Enhanced Reasoning Network (ViSERN) to exploit reasoning between frame regions. Speciﬁcally, we consider frame regions as vertices and construct a fully-connected semantic correlation graph. Then, we perform reasoning by novel random walk rule-based graph convolutional networks to generate region features involved with semantic relations. With the beneﬁt of reasoning, semantic interactions between regions are considered, while the impact of redundancy is suppressed. Finally, the region features are aggregated to form frame-level features for further encoding to measure video-text similarity. Extensive experiments on two public benchmark datasets validate the effectiveness of our method by achieving state-of-the-art performance due to the powerful semantic reasoning.

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Morris, Elissa, and Daniel A. McAdams. "Bioinspired Origami: Case Studies Using a Keyword Search Algorithm." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22228.

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Abstract Numerous folding patterns, structures, and behaviors exist in nature that may provide design solutions to engineering problems. While applying biological solutions to engineering design is evidently valuable, the retrieval of useful design inspiration remains a primary challenge preventing the transfer of knowledge from biology to the engineering domain. In prior research, information retrieval techniques are employed to retrieve useful biological design solutions and a text-based search algorithm is developed to return passages where folding in nature is observed. The search algorithm, called FoldSearch, integrates tailored biological keywords and filtering methods to retrieve passages from an extensive biological corpus. The objective of this paper is two-fold — 1) to demonstrate the functionality of FoldSearch, and 2) to create abstract models of the retrieved biological systems from FoldSearch which can be used for the development of novel origami crease patterns and foldable structures. In this paper, the utility of FoldSearch is demonstrated through two case studies where the retrieved biological examples undergo a design abstraction process that leads to the development of bioinspired origami crease patterns and novel foldable structures. The abstraction process is presented as a systematic design methodology for bioinspired origami for the growing research field of origami engineering.

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Moreau, Nicolas, Shan Jin, and Thomas Sikora. "Comparison of different phone-based spoken document retrieval methods with text and spoken queries." In Interspeech 2005. ISCA: ISCA, 2005. http://dx.doi.org/10.21437/interspeech.2005-71.

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Sun, Haitian, Tania Bedrax-Weiss, and William Cohen. "PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text." In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-1242.

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