Analysis of syntactic elements and structure of web pages using eye-tracking technology

My PhD research aims to develop novel solutions to the challenge of identifying web page structure through the visual analysis of web pages as images. The intention is to then combine this back end design with various front end applications in order to provide improved web experiences for users with assistive needs (e.g. assisting visually impaired users by supporting more selective screenreader output, or improving experiences of users with cognitive deficits by allowing reduction of clutter or zooming in on selected web page content). I propose to build a comprehensive computer vision-based system to analyse the semantic structure of web pages based purely on an image of the rendered page, which will produce a rich representation of the page as a tree of regions labelled according to their semantic role. Most research into web page segmentation has focused on the use of the structure of the DOM tree and visual features derived from properties specified in the DOM tree. I argue, however, that the image of the rendered page may be a better representation to use, since it is created by the page designer to convey the structure of the page to the user, while the source code and DOM tree are simply intended to cause the browser's rendering engine to produce the correct appearance, and treat many types of content as black boxes. Additionally, my proposed system uses exactly the information seen by a user regardless of implementation method; this gives advantages in implementation-independence and versatility.

This paper describes a dynamic rearrangement mechanism of Web page layouts. The aim of this research is to provide webmasters or administrators with a means of determining easily the best suited content layout of their Web pages in a short period of time by automatically testing and relocating the contents with a trend of user actions. We will become able to modify dynamically the structure and layout of Web pages on the client browsers using the information of user behaviors. Our method provides the means of changing the contents inside or between Web pages and updating the contents on Web browsers, and it shorten the period of time for optimization.KeywordsUser BehaviorCellular PhoneContent DeliveryContent Management SystemPage ViewThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In order to overcome the shortcomings of insufficient information return by traditional search engine, improve the speed and accuracy of relevance judgment, a new sentence scoring method is proposed, which incorporate weighted query phrases and the structure information into sentence scoring based on the phrase recognition of the query and the analysis of structure of Web pages. Mutual information (MI) between keywords is used to recognize query phrases; Useful information for the summary is mined from the structure of Web pages. Experimental results show that the improved algorithm performs better in the speed and accuracy of relevance judgment than the existing methods.

This research work explores how noise in web data is currently addressed. We establish that current research works eliminate noise in web data mainly based on the structure and layout of web pages i.e. they consider noise as any data that does not form part of the main web page. However, not all data that form part of the main web page is of a user interest and not every data considered noise is actually noise to a given user. The ability to determine what is useful from noise data taking into account dynamic change of user interests has not been fully addressed by current research works. We aim to justify a claim that it is important to learn noise prior to elimination, not only to decrease levels of noise but also reduce the loss of useful information otherwise eliminated as noise. This is because if the process of eliminating noise in web data is not user-driven, the interestingness of web data available to a user will not reflect their interests given the time of the request.

Most Web browsers are designed for visual interaction, which receive input and render webpage content via a graphical and visual mode. For people with visual disabilities, access to the Web is very difficult. In this paper, we propose a new scheme of non-visual browser, which is referred to as Multi-Channel Speech Pushing System (MCSPS). The system can support browsing from both PC client and telephone users, avoiding construction of two individual systems. Furthermore, by web page content and structure analysis and reorganization, the system can help user locate desired information efficiently. Compared with current non-visual browsers, the proposed system performs deeper and more thorough analysis and re-organization of web pages, thus resulting in higher efficiency in browsing.

There exist noisy, unparallel sentences in parallel web pages. Web page structure is subjected to some limitation for sentences alignment task for web page text. The most straightforward way of aligning sentences is using a translation lexicon. However, a major obstacle to this approach is the lack of dictionary for training. This paper presents a method for automatically align Mongolian-Chinese parallel text on the Web via vector space model. Vector space model is an algebraic model for representing any object as vectors of identifiers, such as index terms. In the statistically based vector-space model, a sentence is conceptually represented by a vector of keywords extracted from the text. Extracted keywords are composed by content words, known as terms and the weight of a term in a sentence vector can be determined tf-idf method. CHI is used to compute the association between bilingual words. Once the term weights are determined, the similarity between sentence vectors is computed via cosine measure. The experimental results indicate that the method is accurate and efficient enough to apply without human intervention.

Visual extraction of information from web pages

We present a semi-supervised model which learns the semantics of negation purely through analysis of syntactic structure. Linguistic theory posits that the semantics of negation can be understood purely syntactically, though recent research relies on combining a variety of features including part-of-speech tags, word embeddings, and semantic representations to achieve high task performance. Our simplified model returns to syntactic theory and achieves state-of-the-art performance on the task of Negation Scope Detection while demonstrating the tight relationship between the syntax and semantics of negation.

You can create a web page layout in two ways. The old style is to use a table and nest other tables inside. In the 1990s, that was the most effective way to create a page layout that would work in Netscape 4.0 and Internet Explorer 4. As with <font> tags for presentation, tables for layout are out of fashion and create bloated web pages that do not work well in alternative web devices ranging from cell phones to PDAs to television sets used as monitors for game consoles to WebTV-type browsers. Browsers have matured, and all current or even semicur- rent web browsers support CSS 2.1 for page layout, which degrades gracefully to such devices; as a result, CSS is the preferred method to use for your web page layout.KeywordsList ItemConditional CommentPosition CategoryPage LayoutCopyright InformationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Each web page can be segmented into semantically coherent units that fulfill specific purposes. Though the task of automatic web page segmentation was introduced two decades ago, along with several applications in web content analysis, its foundations are still lacking. Specifically, the developed evaluation methods and datasets presume a certain downstream task, which led to a variety of incompatible datasets and evaluation methods. To address this shortcoming, we contribute two resources: (1) An evaluation framework which can be adjusted to downstream tasks by measuring the segmentation similarity regarding visual, structural, and textual elements, and which includes measures for annotator agreement, segmentation quality, and an algorithm for segmentation fusion. (2) The Webis-WebSeg-20 dataset, comprising 42,450~crowdsourced segmentations for 8,490~web pages, outranging existing sources by an order of magnitude. Our results help to better understand the mental segmentation model'' of human annotators: Among other things we find that annotators mostly agree on segmentations for all kinds of web page elements (visual, structural, and textual). Disagreement exists mostly regarding the right level of granularity, indicating a general agreement on the visual structure of web pages.

Web Pages contains enormous amount of data that is visible to all types of internet users. Web Pages is built using HTML and it is possible to capture the layout of a web page by converting to a canvas. You only look once algorithm is the best algorithm for object detection. In this study, a YOLOV3 model was created to detect pornographic content, educational content, and gaming content using the Canvas of the web pages saved into JPEG format. The images were annotated using Microsoft Vott. The YOLOV3 model was trained using the pre-trained weights of ImageNet. The accuracy of the YOLOV3 for detecting adult content in pornographic web page canvas has an accuracy of 84% while the educational and gaming web page canvas has an accuracy of 53%. For the safety of readers, the images presented in this paper were blurred or we covered portions of the canvas that showed sexual nudity.

A Novel Approach to HTML Page Creation Using Neural Network

In our research, we focus on ways to crawl and scrape structured data embedded in web pages and collect it as an available data set. To realize large-scale structured data collection, we develop SDC, specialized structured data extraction crawler.SDC integrates the three modules required for extracting structured data: crawling, scraping, and output generation. In particular, we are paying attention to the fact that only simple syntax and semantics are used for structured data. In this way, information extraction from web pages that exist in multiple domains is realized with a simple single setting. It is not necessary to consider the hierarchical structure of websites and the DOM structure of web pages. The users can also display a part of it as a preview to see if the extracted data is what they need.However, even if structured data is used, there are cases where how elements are written differently for each domain. For example, in an e-book sales website, one element is given to each author of the book, but in another site, the list of multiple authors is described as a comma-separated string. Also, there is a website that implements navigation such as pagination using Ajax, and it is necessary to execute the corresponding JavaScript to obtain hyperlink from this site. To cope with the difference between these domains, it is possible for users to quickly specify the data extraction mechanism corresponding to individual websites complementary.As the experiment, we extracted 500 top domains with a large number of links on the websites by our system, that 254 sites where structured data are present, and we have been successfully extracted from 243 sites.

Web pages contain a large amount of valuable information and resources, meanwhile may update at any time. However, the current Web-data extraction algorithms are generally targeted at specific web page structure. When web pages update, the problem which is caused by the changes of web pages may be encountered, leading to the inability to extract web page information or wrong information. In order to solve this problem, this paper proposes a new method to extract the feature values of each area in the web page through page rendering, and then combine the DOM tree structure of the page, semantic similarity and other information, so that it can still extract the target data correctly after the structure of the web page changes.

The addition of CSS cascading style sheets completes the layout of the interactive web page in the interactive web page layout technology HTML5+CSS3. The effect of web page optimization is very obvious, and it is also very important to float and clear floating in the web page layout. The four methods of clearing floating has been introduced mainly, in order to achieve the effect of further optimization to compare the advantages and disadvantages of each method from all aspects, and ultimately to make the overall layout of the page optimal. But floating can cause many problems, and it is especially significant to clear the float. There are many ways to clear float, but each method has more or less limitations. And other methods must be found to eliminate these limitations. In contrast, the method of removing floats by pseudo-elements is the most perfect.