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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pre-requisites\n",
"\n",
"- Python 3.5+\n",
"- Python packages: \n",
" - `pip install bs4 pandas mmh3`\n",
"- [Indic NLP Library](https://github.com/anoopkunchukuttan/indic_nlp_library)\n",
"- [Indic NLP Resources](https://github.com/anoopkunchukuttan/indic_nlp_resources)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Initialize the Indic NLP Library\n",
"\n",
"Run the cell below to initialize the Indic NLP Library"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# The path to the local git repo for Indic NLP Library\n",
"INDIC_NLP_LIB_HOME=\"/disk1/src/indic_nlp_library\"\n",
"\n",
"# The path to the local git repo for Indic NLP Resources\n",
"INDIC_NLP_RESOURCES=\"/disk1/src/indic_nlp_resources\"\n",
"\n",
"import sys\n",
"sys.path.append('{}/src'.format(INDIC_NLP_LIB_HOME))\n",
"\n",
"from indicnlp import common\n",
"common.set_resources_path(INDIC_NLP_RESOURCES)\n",
"\n",
"from indicnlp import loader\n",
"loader.load()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from bs4 import BeautifulSoup\n",
"import os\n",
"import string\n",
"import indicnlp\n",
"from indicnlp.tokenize import indic_tokenize\n",
"from indicnlp.normalize import indic_normalize\n",
"from indicnlp.transliterate import unicode_transliterate\n",
"from indicnlp.tokenize import sentence_tokenize\n",
"import re\n",
"import collections\n",
"import random\n",
"import mmh3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Common Functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def preprocess_sent(text,lang,normalizer):\n",
" \"\"\"\n",
" Pre-process text (normalization and tokenization)\n",
" \n",
" text: text string to preprocess\n",
" lang: language code (2-letter ISO code)\n",
" normalizer: normalizer object for language\n",
" \n",
" returns the processed text string\n",
" \"\"\"\n",
" return ' '.join(indic_tokenize.trivial_tokenize(normalizer.normalize(text.replace('\\n',' ')),lang)) \n",
"\n",
"def sent_split(text,lang):\n",
" \"\"\"\n",
" Sentence splitter\n",
" \n",
" text: text to sentence split \n",
" lang: language\n",
" \n",
" returns list of sentences \n",
" \"\"\"\n",
" return sentence_tokenize.sentence_split(text,lang)\n",
"\n",
"def extract_all_content(indir,lang,\n",
" article_extract_fn,\n",
" preprocess_fn=preprocess_sent,\n",
" narticles=-1,\n",
" start_artid=0):\n",
" \"\"\"\n",
" This method reads all files from the input directory, extracts text content from each file,\n",
" and pre-processes the text. This method is a generator. \n",
" For each sentence, the method yields a tuple of the format: \n",
" \n",
" (artid, fname, paraid, sentid, processed_text)\n",
" \n",
" indir: path to input directoryo containing files to be parsed \n",
" \n",
" lang: language to the files in the input directory\n",
" \n",
" article_extract_fn: the function to extract text content from each file. \n",
" Signature of the function: get_article_contents(fname,lang,encoding) \n",
" `fname` is name of the file, `lang` is langcode, \n",
" `encoding` is text-encoding (default=utf-8). \n",
" The function yields a tuple (paraid, sentid, extracted_text) \n",
" for each sentence.\n",
" \n",
" preprocess_fn: pre-processing function to apply to the extracted text. \n",
" The function takes a string as input and returns processed string as output.\n",
" \n",
" narticles: extract and process the first `narticles` from input directory. \n",
" if narticles=-1 (default), all files are extracted\n",
" \n",
" start_artid: the start of the article id to assign to extracted articles (default=0)\n",
" \n",
" \"\"\"\n",
"\n",
" fnames = os.listdir(indir)\n",
" if narticles>0:\n",
" fnames=fnames[:narticles]\n",
" nsent=0\n",
"\n",
" normalizer_factory=indic_normalize.IndicNormalizerFactory()\n",
" normalizer=normalizer_factory.get_normalizer(lang)\n",
" \n",
" print('Number of articles: {}'.format(len(fnames)))\n",
" for artid, fname in enumerate(fnames,start_artid):\n",
"# print(fname)\n",
" if artid%100 == 0:\n",
" print('({}|{})'.format(artid,nsent),end=' ... ')\n",
" \n",
" try:\n",
" fpath=os.sep.join([indir,fname])\n",
" for paraid, sentid, sent in article_extract_fn(fpath,lang):\n",
" nsent+=1\n",
" yield( ( artid, fname, paraid, sentid, preprocess_fn(sent,lang,normalizer) ) )\n",
" except:\n",
" print('Cannot parse {}'.format(fname))\n",
" \n",
"def write_corpus(corpus_iterator,content_fname,article_mapping_fname,delimiter=' ||| ', encoding='utf-8'):\n",
" \"\"\"\n",
" Writes the extracted corpus to a file. The extracted data is organized in terms of articles, paragraphs \n",
" and sentences. The following is the format of the output file: \n",
" - one line per sentence\n",
" - format of line: article_id, para_id, sent_id, sentence\n",
" In addition to the content file mention, a metadata file which maps the article id to the filename is also written. \n",
" \n",
" corpus_iterator: iterator over the corpus, yielding tuple (artid, fname, paraid, sentid, processed_text). \n",
" The function `extract_all_content` yields a generator in this format. \n",
" content_fname: output content file to write the extracted data to in the format mentioned above\n",
" article_mapping_fname: output metadata file to write article id to filename mapping.\n",
" delimiter=' ||| ': delimiter for the content file. The default delimiter is the same \n",
" as used in the Moses phrase table\n",
" encoding: text encoding default - 'utf-8'\n",
" \n",
" \"\"\"\n",
" \n",
" artid_name_mapping={}\n",
" with open(content_fname,'w',encoding=encoding) as contentfile:\n",
" for artid, fname, paraid, sentid, text in corpus_iterator:\n",
" contentfile.write(delimiter.join([str(artid), str(paraid), str(sentid), text]) + '\\n')\n",
" artid_name_mapping[artid]=fname\n",
"\n",
" with open(article_mapping_fname,'w',encoding=encoding) as artmappingfile:\n",
" for artid, name in sorted(artid_name_mapping.items(),key=lambda x: x[0]):\n",
" artmappingfile.write('{} {} {}\\n'.format(artid,delimiter,name))\n",
"\n",
"def convert_txt_to_csv_format(infname, outfname, encoding='utf-8'):\n",
" \"\"\"\n",
" convert txt file to csv format. This method is used when the text file is directly available.\n",
" The input file has one sentence per line. Assumed to be preprocessed (tokenized, normalized)\n",
" \n",
" \"\"\"\n",
" with open(infname,'r',encoding=encoding) as infile, \\\n",
" open(outfname,'w',encoding=encoding) as outfile: \n",
" for i, line in enumerate(infile):\n",
" outfile.write('0 ||| 0 ||| {} ||| {}\\n'.format(i,line.strip()))\n",
" \n",
"def preprocess_convert_txt_to_csv_format(infname, outfname, lang, encoding='utf-8'):\n",
" \"\"\"\n",
" Convert raw text file to csv format\n",
" \"\"\"\n",
" \n",
" normalizer_factory=indic_normalize.IndicNormalizerFactory()\n",
" normalizer=normalizer_factory.get_normalizer(lang)\n",
" \n",
" with open(infname,'r',encoding=encoding) as infile, \\\n",
" open(outfname,'w',encoding=encoding) as outfile: \n",
" i=0\n",
" for line in infile:\n",
" sents = sent_split(line.strip(),lang)\n",
" for sent in sents:\n",
" outfile.write('0 ||| 0 ||| {} ||| {}\\n'.format(i,\n",
" preprocess_sent(sent.strip(), lang, normalizer)) )\n",
" i=i+1\n",
"\n",
"def print_txt(infnames, outfname, encoding='utf-8'):\n",
" \"\"\"\n",
" Extract only the text from the content csv file. The output file has one sentence per file.\n",
" \"\"\"\n",
" with open(outfname,'w',encoding=encoding) as outfile: \n",
" for infname in filter(lambda x: os.path.isfile(x),infnames):\n",
" with open(infname,'r',encoding=encoding) as infile:\n",
" for i, line in enumerate(infile):\n",
" fields=line.strip().split('|||')\n",
" if len(fields) >=4:\n",
" outfile.write('{}\\n'.format(fields[3].strip()))\n",
" \n",
"# def dedup_and_print_txt(infnames, outfname, encoding='utf-8'):\n",
" \n",
"# total=0\n",
"# unique=0\n",
"# hash_codes=set()\n",
" \n",
"# with open(outfname,'w',encoding=encoding) as outfile: \n",
"# for infname in filter(lambda x: os.path.isfile(x),infnames):\n",
"# with open(infname,'r',encoding=encoding) as infile:\n",
"# for i, line in enumerate(infile):\n",
"# fields=line.strip().split('|||')\n",
"# if len(fields) >=4:\n",
"# sent=fields[3].strip()\n",
"# total+=1\n",
"# hs=hash(sent)\n",
"# if hs not in hash_codes:\n",
"# outfile.write('{}\\n'.format(sent))\n",
"# hash_codes.add(hs)\n",
"# unique+=1\n",
" \n",
"# print('Total: {}'.format(total))\n",
"# print('Unique: {}'.format(unique))\n",
"\n",
"def dedup_shuffle_and_print_txt(infnames, outfname, max_buf_size=100000,encoding='utf-8'):\n",
" \"\"\"\n",
" The method creates a sentence level corpora from multiple content csv files.\n",
" All sentences are extracted, they are de-duplicated using murmurhash and shuffled\n",
" before writing the entire corpus to the output file. The output file has one sentence per line.\n",
"\n",
" \"\"\"\n",
" \n",
" total=0\n",
" unique=0\n",
" hash_codes=set()\n",
" sent_buffer=[]\n",
" \n",
" with open(outfname,'w',encoding=encoding) as outfile: \n",
" for infname in filter(lambda x: os.path.isfile(x),infnames):\n",
" print('Processing: {}'.format(infname))\n",
" with open(infname,'r',encoding=encoding) as infile:\n",
" for i, line in enumerate(infile):\n",
" fields=line.strip().split('|||')\n",
" if len(fields) >=4:\n",
" sent=fields[3].strip()\n",
" total+=1\n",
"# hs=hash(sent)\n",
" hs=mmh3.hash128(sent)\n",
" if hs not in hash_codes:\n",
"# outfile.write('{}\\n'.format(sent))\n",
" sent_buffer.append(sent)\n",
" hash_codes.add(hs)\n",
" unique+=1\n",
" if len(sent_buffer)>=max_buf_size:\n",
" random.shuffle(sent_buffer)\n",
" for sent in sent_buffer: \n",
" outfile.write('{}\\n'.format(sent))\n",
" sent_buffer.clear()\n",
" \n",
" if len(sent_buffer)>0:\n",
" random.shuffle(sent_buffer)\n",
" for sent in sent_buffer: \n",
" outfile.write('{}\\n'.format(sent))\n",
" sent_buffer.clear() \n",
" \n",
" print('Total: {}'.format(total))\n",
" print('Unique: {}'.format(unique))\n",
"\n",
"def extract_wikiextractor_file(infname, outfname, lang, \n",
" encoding='utf-8', delimiter=' ||| ', preprocess_fn=preprocess_sent):\n",
" \"\"\"\n",
" Extract text content into a content csv file from wikipedia article page. \n",
" The wikipedia article page is the output from `wikiextractor` [https://github.com/attardi/wikiextractor] \n",
" \n",
" \"\"\"\n",
" normalizer_factory=indic_normalize.IndicNormalizerFactory()\n",
" normalizer=normalizer_factory.get_normalizer(lang)\n",
" \n",
" with open(infname,'r',encoding=encoding) as infile, \\\n",
" open(outfname,'w',encoding=encoding) as outfile: \n",
" artid=-1\n",
" paraid=0\n",
" for line in infile:\n",
" if line.find('<doc')==0:\n",
" artid+=1\n",
" paraid=0\n",
" continue\n",
" if line.find('</doc')==0:\n",
" continue\n",
" if len(line.strip())>0:\n",
" for sentid, sent in enumerate(sent_split(line.strip(),lang)):\n",
" sent=sent.strip()\n",
" if sent!='':\n",
" sent = preprocess_fn(sent,lang,normalizer)\n",
" outfile.write(delimiter.join([str(artid), str(paraid), str(sentid), sent]) + '\\n')\n",
" paraid+=1\n",
"\n",
" \n",
"def extract_leipzig_corpus(infname,outfname,lang,encoding='utf-8'):\n",
" \"\"\"\n",
" Extractor for files form the Leipzig corpus\n",
" [http://wortschatz.uni-leipzig.de/en/download/]\n",
" \n",
" \"\"\"\n",
" normalizer_factory=indic_normalize.IndicNormalizerFactory()\n",
" normalizer=normalizer_factory.get_normalizer(lang) \n",
"\n",
" with open(infname,'r',encoding=encoding) as infile, \\\n",
" open(outfname,'w',encoding=encoding) as outfile: \n",
" for i, line in enumerate(infile):\n",
" outfile.write('0 ||| 0 ||| {} ||| {}\\n'.format(i,preprocess_sent(line,lang,normalizer))) \n",
" \n",
"def dataset_stats(fname):\n",
" \"\"\"\n",
" Extracts dataset statistics from the final extracted file. This input file contains\n",
" one sentence per line. The sentences are tokenized.\n",
" \"\"\"\n",
"\n",
" all_puncs=set(string.punctuation+'\\u0964\\u0965')\n",
" \n",
" sent_count=0\n",
" token_cnt=0\n",
" true_token_cnt=0\n",
" tokens=set()\n",
" \n",
" with open(fname,'r',encoding='utf-8') as infile:\n",
" for line in infile:\n",
" sent_count+=1\n",
" a=line.strip().split(' ')\n",
" token_cnt+=len(a)\n",
" b=list(filter(lambda x: x not in all_puncs,a))\n",
" true_token_cnt+=len(b)\n",
" tokens.update(b)\n",
" \n",
" print('== Stats ==')\n",
" print('Sent count: {}'.format(sent_count))\n",
" print('Token count: {}'.format(token_cnt))\n",
" print('True Token count: {}'.format(true_token_cnt))\n",
" print('Unique Token count: {}'.format(len(tokens)))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Marathi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Wikipedia"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Wikipedia extraction commands using wikiextractor\n",
"\n",
"```\n",
"### This uses WikiExtractor (https://github.com/attardi/wikiextractor)\n",
"\n",
"x=/disk1/crawl_project/ta/wikipedia\n",
"mkdir $x\n",
"cd $x\n",
"wget https://dumps.wikimedia.org/tawiki/20190501/tawiki-20190501-pages-articles-multistream.xml.bz2\n",
"cd /disk1/src/wikiextractor\n",
"python3 WikiExtractor.py -cb 250k -o $x/extracted $x/tawiki-20190501-pages-articles-multistream.xml.bz2\n",
"cd -\n",
"find extracted -name '*bz2' -exec bunzip2 -c {} \\; > text.xml\n",
"rm text.xml\n",
"rm tawiki-20190501-pages-articles-multistream.xml.bz2\n",
"rm -rf extracted\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"mrwiki-20190401-pages-articles-multistream.xml.bz2\n",
"\n",
"INFO: Finished 1-process extraction of 53715 articles in 123.6s (434.7 art/s)\n",
"\n",
"INFO: total of page: 102025, total of articl page: 53715; total of used articl page: 53715"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Post-processing output generated by wikiextractor"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## tex.xml is extracted as shown in commanfs above\n",
"extract_wikiextractor_file('text.xml',\n",
" 'content_fname1.csv',\n",
" 'mr')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loksatta"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Extractor function for Marathi Loksatta page**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def get_article_contents_mr_loksatta(fname,lang,encoding='utf-8'):\n",
" with open(fname,'r',encoding=encoding) as infile: \n",
" soup = BeautifulSoup(infile)\n",
" for elem in soup.find_all('div'):\n",
" if 'itemprop' in elem.attrs and 'articleBody' in elem['itemprop']:\n",
" filtered_paras=list(filter(lambda x: x.name=='p' and len(x.attrs)==0,elem.children))\n",
" paraid=0\n",
" for blockid, block in enumerate(filtered_paras):\n",
"# print('Para: {}'.format(blockid))\n",
"# print(list(block.strings))\n",
" text=' '.join(block.strings)\n",
" if blockid==0 and text.find(':')>=0 and text.find(':')<20:\n",
" text=':'.join(text.split(':')[1:])\n",
" for para_text in text.split('\\n'): \n",
" for sentid, sent in enumerate(sent_split(para_text,lang)):\n",
" sent=sent.strip()\n",
" if sent!='':\n",
" # print('{}: {}'.format(sentid, sent))\n",
" yield((paraid,sentid,sent))\n",
" # yield((paraid,sentid,preprocess_sent(sent,'ml',normalizer)))\n",
" # print() \n",
" paraid+=1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Extracting data from crawled HTML files**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"lang='mr'\n",
"posts_dir='directory_containing_crawled_html_pages'\n",
"content_fname='content_fname2.csv'\n",
"article_mapping_fname='article_mapping_fname'\n",
"get_article_contents=get_article_contents_mr_loksatta\n",
"narticles=-1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"write_corpus(\n",
" extract_all_content(posts_dir, lang, article_extract_fn=get_article_contents,narticles=narticles),\n",
" content_fname,\n",
" article_mapping_fname\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Aggregating all crawled data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"### aggregating, de-duplicating and shuffling all the data \n",
"dedup_shuffle_and_print_txt([ 'content_fname1.csv', 'content_fname2.csv' ], 'output_fname.txt' )\n",
"### extract dataset statistics\n",
"dataset_stats('output_fname.txt')"
]
}
],
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"kernelspec": {
"display_name": "Python 3",
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"toc": {
"base_numbering": 1,
"nav_menu": {
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|