Difference between revisions of "LP Extractor Protocol"
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==== Supervised Learning Approach ==== | ==== Supervised Learning Approach ==== | ||
| − | Additionally, the HTML tree structure analysis method will require a subprocess by which to parse a complex HTML page into our DSL. An example of a similar process is Pix2Code, in which a DSL context and a GUI are feed into an architecture containing LSTM layers and a CNN-based vision model(see image) which outputs a DSL token. After training is complete, this architecture can then take an empty context and a GUI input and output DSL code. | + | Additionally, the HTML tree structure analysis method will require a subprocess by which to parse a complex HTML page into our DSL. An example of a similar process is Pix2Code, in which a DSL context and a GUI are feed into an architecture containing LSTM layers and a CNN-based vision model(see image) which outputs a DSL token. After training with paired inputs is complete, this architecture can then take an empty context and a GUI input and output DSL code. |
[[File:Pix2Code.png|frame|Image from "Project Goal V2"]] | [[File:Pix2Code.png|frame|Image from "Project Goal V2"]] | ||
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| + | ==Literature== | ||
Revision as of 12:39, 22 March 2019
| LP Extractor Protocol | |
|---|---|
| Project Information | |
| Has title | LP Extractor Protocol |
| Has start date | |
| Has deadline date | |
| Has project status | Active |
| Subsumed by: | Listing Page Extractor |
| Copyright © 2019 edegan.com. All Rights Reserved. | |
Contents
Overview of Possible Methods
According to “Project Goal V2,” (E:\mcnair\Projects\Incubators) there are three proposed methods to organize and extract useful information from an HTML web page. The first method is textual processing, analyzing the text of the HTML page either through a Word2Vec or “Bag of Words” approach. The second method is to use image based pattern recognition, likely through an off-the-shelf model that can extrapolate key HTML elements from web page screenshots. The third, and most novel method is to structurally analyze the HTML tree structure, and express that simplified HTML structure in a Domain Specific Language (DSL).
HTML Tree Structure Analysis
Structurally analyzing the HTML tree structure of a web page and expressing it in a DSL is the most innovative method of the three. It would require more than simply adapting off-the-shelf models. First, the DSL itself would need to be designed to optimize abstraction into the target domain, a web page. (See Domain Specific Language Research.) Then, the DSL would need to be integrated into the machine learning pipeline by encoding the DSL into an appropriately formatted input, such as a vector or matrix, for a neural network. Three proposed methods for this encoding are using an adjacency matrix, an edges to vertices approach, or utilizing DFS (depth-first search) algorithms.
DFS Encoding
Currently, we are leaning towards utilizing DFS algorithms. A depth-first search algorithm could traverse any given tree and record 1 when a new node is found, and 0 when that node is fully explored. This creates a numerical representation of that tree that can then be entered into a vector or matrix.
Supervised Learning Approach
Additionally, the HTML tree structure analysis method will require a subprocess by which to parse a complex HTML page into our DSL. An example of a similar process is Pix2Code, in which a DSL context and a GUI are feed into an architecture containing LSTM layers and a CNN-based vision model(see image) which outputs a DSL token. After training with paired inputs is complete, this architecture can then take an empty context and a GUI input and output DSL code.
