![]() ![]() If there is multiple unwanted sentences with slightly different words, you can copy the common substring into filter_strs, and the regular expression will filter out all sentences containing the substring. Copying the entire sentence can be accomplished by increasing the n-gram size until the entire sentence is captured in one n-gram and printed on the console, or simply printing the parsed_texts and searching for the sentence. To remove the sentence, copy the entire sentence and add it as a single string in the filter_strs array. If the frequency is higher than 1 or 2, the sentence might be something you would consider removing from the corpus. Once we have our frequency dictionary, we print the top 10 n-grams. ![]() We want to sort the n-grams by frequency using the FreqDist function provided by nltk. With these tokenized sentences, we are able to generate n-grams of a specific size (we want to start large, around 15). Next, we tokenize our sentences (break up the sentence into single word strings). We begin this process by breaking up our dataset up into sentences by splitting the text chunks up by the newline characters and periods. By searching for large n-grams that occur frequently, we are able to detect the repeated elements across websites in our corpus, and manually filter them out. Trigrams start to become more rare, and it is almost impossible for the articles to contain the same sequence of 20 words. However, as we increase our n-gram size, the probability of the n-gram repeating decreases. When we read articles, there are many single words (unigrams) that are repeated, such as “the” and “a”. Below is an example for trigrams (3-grams): input = 'It is quite sunny today.' output = This is frequently used to build language models which can assist in tasks ranging from text summarization to word prediction. N-grams is a concept from NLP where the “gram” is a contiguous sequence of words from a body of text, and “N” is the size of these sequences. ![]() To detect and remove these phrases, we analyze our corpus by looking at the frequency of large n-grams. Elements such as website titles, company slogans, and page footers can be present in your parsed text. It is common for web pages to contain repeated information, especially if you scrape multiple articles from the same domain. Once the text has been extracted, we want to continue with the cleaning process. This is done so that when we split the text up into sentences by \n and periods, we don’t get sentences with no words. Next, we condense all newline characters ( \n and \r) into one \n character. You can try out other extractors listed in the documentation for boilerp圓 and see what works best for your dataset. This extractor has been tuned for news articles that works well for most HTMLs. We use the ArticleExtractor to extract the text. Thankfully, there is a Python module called boilerp圓 that makes text extraction easy. Websites have lots of tags that don’t contain useful information when it comes to NLP, such as and. Once we have obtained our scraped web pages, we begin by extracting the text out of each web page. It is important to ensure that the pages you are scraping contain rich text data that is suitable for your use case. Otherwise, I recommend scrapyd because of the possible customizations and robustness. If you are looking for something quick and simple, the URL handling module in Python called urllib might do the trick for you. Here, we will focus on cleaning data that is composed of scraped web pages. Without these preprocessing steps, the results of a project can easily be biased or completely misunderstood. The most important step of any data-driven project is obtaining quality data.
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