Difference between revisions of "User:Wei2912"

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My name is Ng Wei En and I am helping out Apertium by participating as a Google Code-In mentor. I was a GCI student in 2013 and 2014, and have helped out at previous GCIs in 2015, 2016 and 2017. I have a general interest in mathematics and computer science, particularly algorithms and cryptography.
My name is Wei En and I'm currently a GCI student. My blog is at http://wei2912.github.io.
 
   
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'''Blog''': https://weien.io
I decided to help out at Apertium because I find the work here quite interesting and I believe Apertium will benefit many.
 
   
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'''GitHub''': https://github.com/wei2912
The following are projects related to Apertium.
 
   
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'''Twitter''': https://twitter.com/wei2912
== Wiktionary Crawler ==
 
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== Projects ==
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=== Wiktionary Crawler ===
   
 
https://github.com/wei2912/WiktionaryCrawler is a crawler for Wiktionary which aims to extract data from pages. It was created for a GCI task which you can read about at [[Task ideas for Google Code-in/Scrape inflection information from Wiktionary]].
 
https://github.com/wei2912/WiktionaryCrawler is a crawler for Wiktionary which aims to extract data from pages. It was created for a GCI task which you can read about at [[Task ideas for Google Code-in/Scrape inflection information from Wiktionary]].
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The crawler crawls a starting category (usually Category:XXX language)for subcategories, then crawls these subcategories for pages. It then passes the page to language-specific parsers which turn it into the [[Speling format]].
 
The crawler crawls a starting category (usually Category:XXX language)for subcategories, then crawls these subcategories for pages. It then passes the page to language-specific parsers which turn it into the [[Speling format]].
   
The current languages supported are Chinese (zh), Thai (th) and Lao (lo). You are welcome to contribute to this project.
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The current languages supported are Chinese (zh), Thai (th) and Lao (lo).
   
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'''Note: The project has been deprecated as a more modular web crawler has been built in GCI 2015.'''
== Spaceless Segmentation ==
 
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=== Spaceless Segmentation ===
   
 
Spaceless Segmentation has been merged into Apertium under https://svn.code.sf.net/p/apertium/svn/branches/tokenisation. It serves to tokenize languages without any whitespace. More information can be found under [[Task ideas for Google Code-in/Tokenisation for spaceless orthographies]].
 
Spaceless Segmentation has been merged into Apertium under https://svn.code.sf.net/p/apertium/svn/branches/tokenisation. It serves to tokenize languages without any whitespace. More information can be found under [[Task ideas for Google Code-in/Tokenisation for spaceless orthographies]].
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The tokeniser looks for possible tokenisations in the corpus text and selects the tokenisation which tokens appears the most in corpus.
 
The tokeniser looks for possible tokenisations in the corpus text and selects the tokenisation which tokens appears the most in corpus.
   
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== Miscelleanous ==
A report comparing the above method, LRLM and RLLM (longest left to right matching and longest right to left matching respectively) is available at https://www.dropbox.com/sh/57wtof3gbcbsl7c/AABI-Mcw2E-c942BXxsMbEAja
 
   
== Conversion of PDF dictionary to lttoolbox format ==
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=== Conversion of Sakha-English dictionary to lttoolbox format ===
 
'''NOTE: This document is a draft.'''
 
   
 
In this example we're converting the following PDF file: http://home.uchicago.edu/straughn/sakhadic.pdf
 
In this example we're converting the following PDF file: http://home.uchicago.edu/straughn/sakhadic.pdf
   
We copy the text directly from the PDF file, as PDF to text converters are currently unable to convert the text properly (thanks to the PDF format).
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We copy the text directly from the PDF file, as PDF to text converters are currently unable to convert the text properly (thanks to the arcane PDF format).
   
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Then, we obtain the script for converting our dictionary:
Here's a small sample:
 
   
 
<pre>
 
<pre>
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$ svn co https://svn.code.sf.net/p/apertium/svn/trunk/apertium-tools/dixscrapers/
аа exc. Oh! See!
 
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$ cd dixscrapers/
ааҕыс= v. to reckon with
 
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$ cat orig.txt | sakhadic2dix.py > sakhadic.xml
аайы a. each, every; күн аайы every day
 
аак cf аах n. document, paper; аах= v. to read
 
аал n. ship, barge, float, buoy
 
 
</pre>
 
</pre>
   
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This will give us a XML dump of the dictionary, converted to the lttoolbox format. We sort and format the XML file as shown here to get the final dictionary:
As we can see, words on the same line are seperated by "; ". Hence, we can replace "; " with "\n" so as to get a list of words seperated by newlines. We also remove everything within brackets and equal signs using the following script:
 
   
 
<pre>
 
<pre>
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$ apertium-dixtools sort sakhadic.xml sakhadic.dix
#!/bin/bash
 
cat $1 | perl -wpne 's/\(.+\)//g; s/\[.+\]//g; s/=//g; s/\; /\n/g' > $1.new
 
 
</pre>
 
</pre>
   
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Our final dictionary is in <code>sakhadic.dix</code>.
which we supply a filename to.
 
 
A diff reveals the following image: [[File:Sakhadic_pg3.png]]
 
 
Unfortunately for us, definitions may be seperated by "; " too. Hence, we'll need to merge these lines together and replace the original semicolons with commas. Also, some definitions spread over to the next line; we'll also need to fix that. At the same time, we can remove the equal signs too (they appear to indicate the end of verbs, but this notation is not required in lttoolbox format).
 
 
Also, some words have different word forms. To handle this, we copy over the original word to create a new entry. This:
 
 
<pre>
 
албас a. cunning; n. trick, ruse
 
</pre>
 
 
becomes
 
 
<pre>
 
албас a. cunning
 
албас n. trick, ruse
 
</pre>
 
 
The good part about this is that they're also seperated by "; " and will be placed on a newline, so it's easy to spot the lines where we need to handle this.
 
 
Another thing is that some words may have the same translation. In that case, we also create two entries.
 
 
In the process, we also remove any "cf" tags, as they are not required.
 
 
Each entry should look like this after the processing:
 
<pre>
 
word abbrv1. abbrv2. definition1, definition2, definition3, definition4
 
</pre>
 
   
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For more details on sorting dictionaries, take a look at [[Sort a dictionary]].
Note that abbreviations end with a fullstop and the definitions are seperated by commas.
 

Latest revision as of 15:30, 16 September 2018

My name is Ng Wei En and I am helping out Apertium by participating as a Google Code-In mentor. I was a GCI student in 2013 and 2014, and have helped out at previous GCIs in 2015, 2016 and 2017. I have a general interest in mathematics and computer science, particularly algorithms and cryptography.

Blog: https://weien.io

GitHub: https://github.com/wei2912

Twitter: https://twitter.com/wei2912

Projects[edit]

Wiktionary Crawler[edit]

https://github.com/wei2912/WiktionaryCrawler is a crawler for Wiktionary which aims to extract data from pages. It was created for a GCI task which you can read about at Task ideas for Google Code-in/Scrape inflection information from Wiktionary.

The crawler crawls a starting category (usually Category:XXX language)for subcategories, then crawls these subcategories for pages. It then passes the page to language-specific parsers which turn it into the Speling format.

The current languages supported are Chinese (zh), Thai (th) and Lao (lo).

Note: The project has been deprecated as a more modular web crawler has been built in GCI 2015.

Spaceless Segmentation[edit]

Spaceless Segmentation has been merged into Apertium under https://svn.code.sf.net/p/apertium/svn/branches/tokenisation. It serves to tokenize languages without any whitespace. More information can be found under Task ideas for Google Code-in/Tokenisation for spaceless orthographies.

The tokeniser looks for possible tokenisations in the corpus text and selects the tokenisation which tokens appears the most in corpus.

Miscelleanous[edit]

Conversion of Sakha-English dictionary to lttoolbox format[edit]

In this example we're converting the following PDF file: http://home.uchicago.edu/straughn/sakhadic.pdf

We copy the text directly from the PDF file, as PDF to text converters are currently unable to convert the text properly (thanks to the arcane PDF format).

Then, we obtain the script for converting our dictionary:

$ svn co https://svn.code.sf.net/p/apertium/svn/trunk/apertium-tools/dixscrapers/
$ cd dixscrapers/
$ cat orig.txt | sakhadic2dix.py > sakhadic.xml

This will give us a XML dump of the dictionary, converted to the lttoolbox format. We sort and format the XML file as shown here to get the final dictionary:

$ apertium-dixtools sort sakhadic.xml sakhadic.dix

Our final dictionary is in sakhadic.dix.

For more details on sorting dictionaries, take a look at Sort a dictionary.