Our group aims to develop a Language Correction software that efficiently analyzes text for correct language usage by crawling through pages of the target language(s) on the Internet. The evaluator will compile common usage patterns of words and phrases from crawled texts and use them as a reference to identify words or phrases that are consistent or inconsistent with the gathered data. This project will assist users in improving their language skills and ensuring the accuracy of their written content.
### Credits
Webcrawler by Alex Melnick
Alex Melnick
Data Parser by Michael
Michael Harkess
Language checker and corrector by Reza Sajjadi
Reza Sajjadi
### Implementation Description
## Sources
### Implemented Features
- Provide real-time status and statistics ( e.g., what URL is being processed, rate of processing, size of storage, etc.) feedback for the crawler. [10%]
- IMPLEMENTATION DESCRIPTION
- Provide a list of reasonable corrections to a suspicious text, ranked in order of how different they are from the original text. [15%]
- IMPLEMENTATION DESCRIPTION
- Graphical User Interface that highlights suspicious and non-suspicious textual elements in a given text. [15%]
- IMPLEMENTATION DESCRIPTION
- Provide a graphical human feedback system for deciding among possible phrase corrections, with feedback into the suspicion levels reported by the system. [15%]
- IMPLEMENTATION DESCRIPTION
- Develop and Android client for your checker. [15%]
- For the most part, the Android client of the checker is interfacing with the Checker CLI tool to produce its output in a pop-up window on the app. To do this, the Checker tool was modified so that it did not write the checked result to a JSON file (and instead kept it as a JSON string) as well as not require CLI arguments for the script to function. In addition, helper class functions were used to display the output to the user in the app.
The only library used for the checker and corrector is https://github.com/xerial/sqlite-jdbc and its dependency https://www.slf4j.org. These are both provided in the repo, and there is no need to download them. They are used for communicating with SQLite databases.
## Code
- Most Updated (Stable) Branch: Master
- Folder with All data needed to run repo:
- Folder with All testing Code:
## Usage
### Web Crawler
To run the webcrawler, run `./crawler --file [PATH]`. Alternatively, `java ScratchCrawler --file [PATH]` also works.
## Work Breakdown
Webcrawler by Alex Melnick
### Checker/Corrector
To build the project for CheckerCorrector we are using a make file. Running `make dev_corrector` and `make dev_checker` will compile and build the `checker.jar` and `corrector.jar` with the user interface requested, and and bash script ready to be run them as `./checker --file [PATH]` and `./corrector --file [PATH]`.
Data Parser, Android App by Michael Harkess
## How it Works
### Checker
Language checker and corrector by Reza Sajjadi
## Advanced Technical Information
### How it Works
#### Checker
Our checker is using two different methods to assign confidence points. The first method uses a State Machine and the second is using an n-Grams inspired implementation. The final score will be a weighted sum of these scores provided by two different methods.
#### State Machine Checker
##### State Machine Checker
In order to have a good working State machine we need to first update the grammar and roles of each word manually. This step will be automatized to some extent in the next milestones. Tokens are provided in `CheckerCorrector/SQLite/mydatabase.db`, and also the basic graph provided by `CheckerCorrector/DirectedGraph/BasicGraph.java`. A sentence will first go through a typo checker and get updated if needed (it will also affect the confidence score). Then the sentence will be tokenized, and using the provided graph it will check whether the sentence is following the correct format or not, for each miss on any edge of the graph a penalty will be added to the confidence score.
The typo corrector is calculating the distance of a word to dictionary(supposly provided by crawler). Here we used the small dictionary of homewrok2, and replace the misspelled word with closest distant word with some condition.
#### n-Grams checker
##### n-Grams checker
This checker used the crawled data and gave a score by summing up all the n_grams probabilities of phrases in a sentence. In order to store the data of the crawled data we used SHA-256 hash and store the result in CheckerCorrector/SQLite/hash_database.db.
### Proof of Effictiveness
#### Proof of Effictiveness
In order to show the effictiveness of our tool we used ChatGBT to write a script that is using a third-party Python library to make the exact same score for each sentence. These can be found in CheckerCorrector/samples/ directory. Both Json generated by our tool and the third party are available.
### Corrector
#### Corrector
The current corrector uses the typo corrector which was used in the checker and also the state machine to suggest possible corrections. Corrections of the typo corrector are based on the most similar path through the state machine. In order to find a similar path we are first doing a DFS on the graph starting from the first token and storing all the possible paths. Then, based on the most similar path we decide whether we should change/delete/add a token and suggest a token with a similar role.
The correction at this point is limited to the state machine complexity, but it will be improved for the next milestone.
### Known Issues/Bugs
#### Android App
- If a dialog box pops up and the device/screen is rotated, then the app will unexpectedly crash
- The confidence score of every word in a given sentence is 0
