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.


'''Blog''': https://wei2912.github.io
I decided to help out at Apertium because I find the work here quite interesting and I believe Apertium will benefit many.


'''GitHub''': https://github.com/wei2912
The following are projects related to Apertium.


'''Twitter''': https://twitter.com/wei2912
== Wiktionary Crawler ==

== Projects ==

=== 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.
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 ==

=== 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.


== 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 ==
=== 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
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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).
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 pipe the text to our script:
Then, we obtain the script for converting our dictionary:


<pre>
<pre>
$ svn co https://svn.code.sf.net/p/apertium/svn/trunk/apertium-tools/dixscrapers/
#!/usr/bin/python3
$ cd dixscrapers/
# -*- coding: utf-8 -*-
$ cat orig.txt | sakhadic2dix.py > sakhadic.xml

import fileinput
import itertools
import re
import xml.etree.cElementTree as ET

BRACKETS_RE = re.compile(r'(\(.+?\)|\[.+?\])')
PAGENUMBER_RE = re.compile(r'^\d+$')
SPLIT_RE = re.compile(r'[;,]\s+')

ABBRVS = {
'a.': ['adj'],
'adv.': ['adv'],
'arch.': [],
# cf. see also -- has been wiped out
'comp.': [],
# conv. converb, modifying verb -- covered later
'dial.': [],
'det.': ['det'],
'Evk.': [],
'exc.': ['ij'],
'int.': ['itg'],
'Mongo.': [],
'n.': ['n'],
'num.': ['det', 'qnt'],
'ono.': [],
'pl.': ['pl'],
'pp.': ['post'],
'pro.': ['prn'],
'Russ.': [],
'v.': ['v', 'TD']
}

class Entry(object):
def __split(self, line):
return SPLIT_RE.split(line)

def __init__(self, line):
tags = line.split()

self.words = []
self.abbrvs = []
self.meanings = []

found_abbrv = False
found_conv = False
for tag in tags:
if tag in ABBRVS.keys(): # abbreviations
found_abbrv = True
self.abbrvs.extend(ABBRVS[tag])
continue
elif tag == "conv.":
found_abbrv = True
found_conv = True
self.abbrvs.append("vaux")
continue

if not found_abbrv: # entrys
self.words.append(tag)
else: # translated
self.meanings.append(tag)

# if there's "cf" in a word, we trim off everything else
for i, word in enumerate(self.words):
if word == "cf":
self.words = self.words[:i]

# if there's a converb, just look at the last word
if found_conv:
self.words = self.words[-1]
else:
self.words = " ".join(self.words)
self.meanings = " ".join(self.meanings)

self.words = strip_brackets(self.words)
self.meanings = strip_brackets(self.meanings)

if not self.abbrvs:
self.words = None
self.abbrvs = None
self.meanings = None
return

# preprocessing meanings
self.meanings = self.meanings.replace("to", "")

# split up meanings and entrys
self.words = [x.strip() for x in self.__split(self.words)]
self.meanings = [x.strip() for x in self.__split(self.meanings)]

def insert_blanks(element, line):
words = line.split()
if not words:
return
element.text = words[0]
element.tail = None
blank = None
for i in words[1:]:
blank = ET.SubElement(element, 'b')
blank.tail = i

def is_page_num(line):
return PAGENUMBER_RE.match(line)

def strip_brackets(line):
brackets = BRACKETS_RE.search(line)
if brackets:
for bracket in brackets.groups():
line = line.replace(bracket, "")
return line

def is_cyrillic(word):
num_non_cyrillic = 0
num_cyrillic = 0
for c in word:
ordc = ord(c)
if 0x0400 <= ordc <= 0x04FF:
num_cyrillic += 1
else:
num_non_cyrillic += 1
return num_cyrillic > num_non_cyrillic

def preprocess(lines):
def preprocess_line(line):
if not line:
return None
line = line.strip()
line = line.replace("•", "")
line = line.replace("=", "")
line = line.replace("cf.", "cf")
line = strip_brackets(line)
if not line or is_page_num(line):
return None
return line

new_lines = []
for i, line in enumerate(lines):
line = preprocess_line(line)
if not line:
continue

# check if next line should be merged with this line
if i+1 < len(lines):
words = line.split()
next_line = preprocess_line(lines[i+1])
if next_line:
if (len(words) == 1 or
not is_cyrillic(next_line.split()[0])):
lines[i+1] = line + " " + next_line
continue

orig_word = ""
for j, word in enumerate(words):
if j+1 >= len(words):
continue
next_word = words[j+1]

if word.endswith("."):
orig_word = " ".join(words[:j])
if word.endswith(";"):
# if semicolon seperates dictionary entries
if is_cyrillic(next_word):
words[j] = word.replace(";", "")
line = " ".join(words[:j+1])
next_line = " ".join(words[j+1:])
lines.insert(i+1, next_line)
break
# if semicolon seperates abbreviations
elif next_word.endswith("."):
words[j] = word.replace(";", "")
line = " ".join(words[:j+1])
next_line = orig_word + " " + " ".join(words[j+1:])
lines.insert(i+1, next_line)
break

line = line.strip()
if line:
new_lines.append(line)
return new_lines

def main():
dictionary = ET.Element("dictionary")
section = ET.SubElement(dictionary, "section")
section.set("id", "main")
section.set("type", "standard")

lines = list(fileinput.input())
new_lines = preprocess(lines)

for line in new_lines:
comment = ET.Comment(text=line)
section.append(comment)

entry = Entry(line)
if not (entry.words and entry.abbrvs and entry.meanings):
continue

for word, meaning in itertools.product(entry.words, entry.meanings):
e = ET.SubElement(section, "e")
p = ET.SubElement(e, 'p')

# add word and meaning
left = ET.SubElement(p, 'l')
insert_blanks(left, word)

right = ET.SubElement(p, 'r')
insert_blanks(right, meaning)

# add abbreviations
for abbrv in entry.abbrvs:
s = ET.Element('s')
s.set('n', abbrv)
left.append(s)
right.append(s)
ET.dump(dictionary)

main()
</pre>
</pre>


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


<pre>
<pre>
$ xmllint --format --encode utf8 file.xml > file.dix
$ apertium-dixtools sort sakhadic.xml sakhadic.dix
</pre>
</pre>


Our final dictionary is in <code>sakhadic.dix</code>.
The `--encode utf8` option prevents `xmllint` from escaping our unicode.


For more details on sorting dictionaries, take a look at [[Sort a dictionary]].
The final file format looks like this:

<pre>
<?xml version="1.0" encoding="utf-8"?>
<dictionary>
<section id="main" type="standard">
<!--аа exc. Oh! See!-->
<e>
<p>
<l>аа<s n="ij"/></l>
<r>Oh!<b/>See!<s n="ij"/></r>
</p>
</e>
<!--ааҕыс v. to reckon with-->
<e>
<p>
<l>ааҕыс<s n="v"/><s n="TD"/></l>
<r>reckon<b/>with<s n="v"/><s n="TD"/></r>
</p>
</e>
...
</pre>

Latest revision as of 08:13, 29 May 2021

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://wei2912.github.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.

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