Web document text and images extraction using DOM analysis and natural language processing

Joshi, Parag; Liu, Sam

doi:10.1145/1600193.1600241

Cited by 27 publications

(18 citation statements)

References 8 publications

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“…(1) for each line ∈ lines do (2) if line match heuristic rules then (3) do operation (4) end if (5) end for (6) for each line ∈ lines do (7) find pattern of line (8) match the pattern to others (9) if match then (10) record the block (11) else (12) continue (13) …”

Section: Resume Facts Identificationmentioning

confidence: 99%

“…Since the details of resume are hard to extract, it is an alternative way to achieve the goal of job matching with keywords search approach [3,5]. Inspired by the way of extracting the news web page [6][7][8][9][10], several rule-based extraction approaches [11][12][13] treat the resume text as a web page and then extract detailed facts based on the DOM tree structure. For the last kind of methods, researchers treat the resume extracting task as a semantic-based entity extraction problem.…”

Section: Introductionmentioning

confidence: 99%

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A Two-Step Resume Information Extraction Algorithm

Chen

Zhang

Niu

2018

Mathematical Problems in Engineering

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With the rapid growth of Internet-based recruiting, there are a great number of personal resumes among recruiting systems. To gain more attention from the recruiters, most resumes are written in diverse formats, including varying font size, font colour, and table cells. However, the diversity of format is harmful to data mining, such as resume information extraction, automatic job matching, and candidates ranking. Supervised methods and rule-based methods have been proposed to extract facts from resumes, but they strongly rely on hierarchical structure information and large amounts of labelled data, which are hard to collect in reality. In this paper, we propose a two-step resume information extraction approach. In the first step, raw text of resume is identified as different resume blocks. To achieve the goal, we design a novel feature, Writing Style, to model sentence syntax information. Besides word index and punctuation index, word lexical attribute and prediction results of classifiers are included in Writing Style. In the second step, multiple classifiers are employed to identify different attributes of fact information in resumes. Experimental results on a real-world dataset show that the algorithm is feasible and effective.

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Section: Resume Facts Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Two-Step Resume Information Extraction Algorithm

Chen

Zhang

Niu

2018

Mathematical Problems in Engineering

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“…In these approaches, visual features, such as width, height, or position, are used to recognize and analyze information blocks. The combination of natural language processing and DOM analysis [24] or statistical analysis [23] was proposed to recognize the main content in a Web page. The TWWF approach [60] used the DOM structure to divide a Web page.…”

Section: Related Workmentioning

confidence: 99%

Specification and discovery of web patterns: a graph grammar approach

Roudaki

Kong

Zhang

2016

Information Sciences

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“…The topic of our paper centers on the textual information that surrounds or is attached to Web images. This contextual information is a unique feature of the Web images and has long been mined for various uses such as image annotation (Joshi and Liu, 2009;Leong at al., 2010;, clustering of image search results (Blaschko and Lampert, 2008;Cai et al, 2004;Gao et al, 2005;Rege et al, 2008;Wang et al, 2005), inference of image semantic content (Feng and Lapata, 2008;Ghoshal et al, 2005;Tang et al, 2009) etc.…”

Section: Issues In Contextual Information-based Image Understanding Omentioning

confidence: 99%

Image understanding and the web: a state-of-the-art review

Fauzi

Belkhatir

2014

J Intell Inf Syst

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Image understanding and the web: a state-of-the-art review. ABSTRACTThe contextual information of Web images is investigated to address the issue of characterizing their content with semantic descriptors and therefore bridge the semantic gap, i.e. the gap between their automated low-level representation in terms of colors, textures, shapes… and their semantic interpretation. Such characterization allows for understanding the image content and is crucial in important Web-based tasks such as image indexing and retrieval. Although we are highly motivated by the availability of rich knowledge on the Web and the relative success achieved by commercial search engines in automatically characterizing the image content using contextual information in Web pages, we are aware that the unpredictable quality of the contextual information is a major limiting factor. Among the reasons explaining the difficulty to leverage on the image contextual information, some problems are related to the characterization and extraction of this information. Indeed, the first issue is the lack of large-scale studies to highlight what is considered the relevant contextual information of an image, where it is located in a Web page and whether it is consistent across Web pages of different types, content layouts and domains. Also, the matter related to the extraction of this contextual information is topical as state-of-the-art automated extraction tools are unable to handle the heterogeneous Web. As far as the processing of the contextual information is concerned, problems linked to the syntactic and semantic characterizations of the textual components are important to address in order to tackle the semantic gap. Furthermore, questions pertaining to the organization of these textual components into coherent structures that are usable in image indexing and retrieval frameworks shall arise. To address these issues, we lay down the anatomy of a generic context-based Web image understanding framework and propose its stage-based decomposition, covering topical issues from information indexing and retrieval, image description models, natural language processing, Web page segmentation and automated information extraction. For each of the identified stages, we review state-of-the-art solutions in the literature categorized and analyzed under the light of the techniques used.Image retrieval systems are defined as computer systems for searching and retrieving images from large collections of digital images according to the users' information needs. Examples of image corpora include medical images, satellite images, photo galleries, the Web, etc.(a) The three rows shown are the first rows of the retrieved search results of the Google, Yahoo and Bing search engines respectively for the query "Baby boy in blue". 6 images (framed in red) out of 22 images are relevant.(b) The three rows shown are the first rows of the retrieved search results of the Google, Yahoo and Bing search engines respectively for the query "Car hits lorry". 4 images (framed in red) out ...

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Web document text and images extraction using DOM analysis and natural language processing

Cited by 27 publications

References 8 publications

A Two-Step Resume Information Extraction Algorithm

A Two-Step Resume Information Extraction Algorithm

Specification and discovery of web patterns: a graph grammar approach

Image understanding and the web: a state-of-the-art review

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