#stopwords

"There has been a lot of conversation in both our professional networks and in the news about the increase in book challenges and book bans across the United States over the last five years. However, despite the fact that access decisions about print collections also impact digital collections, we found the discussion about censorship in libraries often excluded the complex factors at play in the digital realm, where restricting access is not as straightforward as removing a physical book from the shelf. In investigating two related accusations about “pornography” in library databases administered by EBSCO Industries, Inc.—a library vendor that provides proprietary access to K-12 and higher education research databases and educational materials—it became clear that digital censorship was an underreported but very concerning threat in the library landscape due to the ability of censors to quickly shut down access to large amounts of information for large populations. A common technique used in censoring digital collections is the use of stopwords and filters in library databases. However, a lot is still opaque in how these techniques are applied, making it difficult to understand how much content has been suppressed directly by vendors vs. what has been applied locally by information technology teams based on demands by pressure groups, school boards, and pro-censorship legislation."

What are Stopwords in python?

 Stopwords are the most common words in any natural language. To assay manual data and make NLP models, these Stopwords might not add major value to the meaning of the document.

Generally, the most common words used in a handbook are “ the ”, “ is ”, “ in ”, “ for ”, “ where ”, “ when ”, “ to ”, “ at ”etc.

Consider this handbook string – “ There's a pen on the table ”. The words “ is ”, “ a ”, “ on ”, and “ the ” add no meaning to the statement while parsing it. Whereas words like “ there ”, “ book ”, and “ table ” are the keywords and tell us what the statement is all about.

 Stop words are ever barred from the substance before preparing deep erudition and machine erudition models since stop words come in bounty, so giving constitutionally no phenomenal information that can be used for depiction or gathering. A couple of instruments expressly avoid barring these stop words to help the state hunt. We've to bar Stopwords while performing assignments, comparable to, Spam Filtering, Auto-Tag Generation, Language Family, and so forth.

Another advantage of removing stop words is that it reduces the size of the dataset and the time taken in training the model.

 How to Remove Stopwords ?

 NLTK, or the Natural Language Toolkit, is a treasure trove of a library for manual pre-processing. It’s one of the Python libraries. Natural Language Toolkit, most commonly used to remove Stopwords.

 The practice of removing stop words is also common among hunt machines. Search machines like Google remove stop words from hunt queries to yield a fast response.

前言

停用詞 (Stop Words) 的定義上是兩個集合：

這個語言中出現非常頻繁的詞。

文本資料中出現非常頻繁的詞。

以英文為例，非常頻繁出現的詞常是 "a", "the", "is", "are", "in", "on" 這些功能詞，這符合第 1 條定義。而如果我們拿美國總統川普的推特發文來計算詞彙的出現頻率的話，可能會發現 "very"、"best" 和 "perfect" 也是頻率非常高的詞彙，這符合第 2 條定義。