Semi-structured Documents Mining: A Review and Comparison

Madani, Amina; Boussaïd, Omar; Zegour, Djamel Eddine

doi:10.1016/j.procs.2013.09.110

Cited by 11 publications

(3 citation statements)

References 13 publications

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“…In the recent years, researchers study the indexation of nodes. A survey on semi-structured documents mining was presented by Madani et al [10].They done a comparison between several existed approaches using different comparison criteria like used technique, complexity of algorithm, etc.…”

Section: State Of the Artmentioning

confidence: 99%

Search of Information Based Content in Semi-Structured Documents Using Interference Wave

Guezouli¹,

Essafi²

2016

IJCSITCE

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This paper proposes a semi-structured information retrieval model based on a new method for calculation of similarity. We have developed CASISS (Calculation of Similarity of Semi-Structured documents) method to quantify how two given texts are similar. This new method identifies elements of semi-structured documents using elements descriptors. Each semi-structured document is pre-processed before the extraction of a set of descriptors for each element, which characterize the contents of elements.It can be used to increase the accuracy of the information retrieval process by taking into account not only the presence of query terms in the given document but also the topology (position continuity) of these terms.

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Section: State Of the Artmentioning

confidence: 99%

Search of Information Based Content in Semi-Structured Documents Using Interference Wave

Guezouli¹,

Essafi²

2016

IJCSITCE

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“…Mathematical morphology is a tool used to extract image components helpful in representing and describing the shape of a region, such as borders, skeletons, and convex closure, through pre or post-processing, such as morphological filtering and thinning, and pruning. Morphology is related to the shape, and mathematical morphology describes or analyzes the shape of a digital object, most often rasterized [60][61].…”

Section: Mathematical Morphologymentioning

confidence: 99%

“…Although the dataset contains structured pages, most of the pages are semistructured. Structured data has a regular structure and semi-structured data arises when the source does not impose a rigid structure [61]. However, in practice, stamps are often applied with some randomness, even in cases where there is a specific region.…”

Section: Image Acquisitionmentioning

confidence: 99%

Method for Automatic Detection of Stamps in Scanned Documents Using Deep Learning and Synthetic Data Generation by Instance Augmentation

VALENTE¹

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Scanned documents in business environments have replaced large volumes of papers. Authorized professionals use stamps to certify critical information in these documents. Many companies need to verify the adequate stamping of incoming and outgoing documents. In most inspection situations, people perform a visual inspection to identify stamps. Therefore, manual stamp checking is tiring, susceptible to errors, and inefficient in terms of time spent and expected results.Errors in manual checking for stamps can lead to fines from regulatory bodies, interruption of operations, and even compromise workflows and financial transactions. This work proposes two methods that combined can address this problem, by fully automating stamp detection in real-world scanned documents.The developed methods can handle datasets containing many small sample-sized types of stamps, multiples overlaps, different combinations per page, and missing data. The first method proposes a deep network architecture designed from the relationship between the problems identified in real-world stamps and the challenges and solutions of the object detection task pointed out in the literature.The second method proposes a novel instance augmentation pipeline of stamp datasets from real data to investigate whether it is possible to detect stamp types with insufficient samples. We evaluate the hyperparameters of the instance augmentation approach and the obtained results through a Deep Explainability method. We achieve state-of-the-art results for the stamp detection task by successfully combining these two methods, achieving 97.3% of precision and 93.2% of recall.

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Stability Evaluation of Event Detection Techniques for Twitter

Weiler

Beel

Gipp

et al. 2016

Lecture Notes in Computer Science

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Abstract. Twitter continues to gain popularity as a source of up-todate news and information. As a result, numerous event detection techniques have been proposed to cope with the steadily increasing rate and volume of social media data streams. Although most of these works conduct some evaluation of the proposed technique, comparing their effectiveness is a challenging task. In this paper, we examine the challenges to reproducing evaluation results for event detection techniques. We apply several event detection techniques and vary four parameters, namely time window (15 vs. 30 vs. 60 mins), stopwords (include vs. exclude), retweets (include vs. exclude), and the number of terms that define an event (1...5 terms). Our experiments use real-world Twitter streaming data and show that varying these parameters alone significantly influences the outcomes of the event detection techniques, sometimes in unforeseen ways. We conclude that even minor variations in event detection techniques may lead to major difficulties in reproducing experiments.

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Semi-structured Documents Mining: A Review and Comparison

Cited by 11 publications

References 13 publications

Search of Information Based Content in Semi-Structured Documents Using Interference Wave

Search of Information Based Content in Semi-Structured Documents Using Interference Wave

Method for Automatic Detection of Stamps in Scanned Documents Using Deep Learning and Synthetic Data Generation by Instance Augmentation

Stability Evaluation of Event Detection Techniques for Twitter

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