Stop words are the most frequent words in a body of text that, in many cases, can be removed without detracting from the overall message. These words are often removed during natural language processing to improve search and other analytical efficiencies.<\/p>\n
There is no universal list of stop words<\/strong> and contextual relevancy should never be disregarded. That’s to say\u2014the most common words in a medical text are likely far different from the most common terms in sports literature.<\/p>\n Building a domain-specific stops words list can prove beneficial in nearly every NLP application. Functionally, it’s useful to have a core collection of stop words to start with.<\/p>\n Stop words are words that are so common to languages that removing them doesn’t affect the overall message enough to lose meaning. As such, Natural Language Processing (NLP)<\/strong> programs often disregard these terms as a means to improve contextual relevancy and performance. After all, fewer words to process mean less processing time.<\/p>\n This concept has become so popular, and useful, that every NLP pipeline I’ve ever worked with has some form of stop words filtering process. One of the commonest use cases is for search engines.<\/p>\n By removing frivolous terms, search queries can be done much faster and with greater relevancy. For example: searching for “what are stop words”<\/em> is pretty similar to “stop words.”<\/em><\/p>\n Google thinks they’re so similar that they return the same Wikipedia and Stanford.edu articles for both terms. The application is clear enough, but the question of which words to remove arises.<\/p>\n Not all stop word lists are created equally. Lists that are effective in one topical domain are ineffective (or maybe irrelevant) in others. However, even vastly different topical domains still share many similar words.<\/p>\n Words like With that in mind, one can appreciate how a\u00a0common<\/em> stop words list can offer utility. A list of common\u00a0common<\/em> words can serve well as a base to build more encompassing domain-specific filters. Let’s consider two-word frequency distributions from two very different sources:<\/p>\n The article\u00a0Efflux pump inhibitors for bacterial pathogens: From bench to bedside<\/em> was published in the February 2019 issue of the Indian Journal of Medical Research (R<\/a>).<\/p>\n It contains phrases like “the characterization of many efflux pumps in Gram-positive bacteria<\/em>,” “MC-207,110 [phenylalanyl arginyl \u03b2-naphthylamide (PA\u03b2N)], a peptidomimetic EPI, was the first to be discovered in 2001<\/em>,” and “yridopyrimidine analogues D2 and D13-9001 have been reported as MexAB-OprM-specific pump inhibitor in MexABoverexpressing P. aeruginosa under both in vitro and in vivo conditions.<\/em>”<\/p>\n Clearly\u2014a very unique domain of discussion. Check out the frequency distribution of words:<\/p>\n The first Request for Comments for Transmission Control Protocol (TCP)<\/a> was made public in September 1981 (R<\/a>). It would serve as the foundation for defining the most utilized transport-layer protocol<\/a> in the modern web\u2014while undergoing many revisions throughout the years.<\/p>\n This document contains phrases like “The sequence number of the first data octet in this segment (except when SYN is present). If SYN is present the sequence number is the initial sequence number (ISN) and the first data octet is ISN+1,<\/em>” and “Among the variables stored in the TCB are the local and remote socket numbers, the security and Again\u2014clearly a unique domain of discussion. Check out the frequency distribution of words from this text:<\/p>\n Note<\/strong>: Both of the above examples were minimally processed by normalizing whitespace, lowercasing text, and truncating to equal lengths.<\/p>\n The examples above illustrate how even\u00a0vastly<\/em> different text domains can have remarkably similar common terms. Of the 20 terms (10 from each source) there are only three that stand out as domain-specific: tcp<\/em>, sequence<\/em>, and efflux<\/em>.<\/p>\n For applications of Natural Language Processing (NLP) it is almost\u00a0always<\/em> useful to take some measure to filter out stop words. Not only does it decrease processing time (fewer words) but also helps to make domain-specific differences more apparent.<\/p>\n Let’s consider the above examples again, this time with stop words being filtered before frequency analysis.<\/p>\nWhat are Stop Words<\/h2>\n
Stop Word Lists<\/h2>\n
the<\/code>, of<\/code>, to<\/code>, in<\/code>, a<\/code>, and that<\/code> are all common connective tissue within the English language, irrespective of topical domain. That’s to say\u2014authors are likely to use these words regardless of what is being discussed.<\/p>\n1.Scientific Journal Article (biology)<\/h3>\n
# Top 10 most frequency words in Biology-domain text\r\n('the', 315)\r\n('of', 271)\r\n('and', 162)\r\n('to', 136)\r\n('in', 105)\r\n('a', 98)\r\n('efflux', 86)\r\n('s.', 78)\r\n('that', 68)\r\n('as', 65)<\/pre>\n2.RFC for TCP Protocol (networking)<\/h3>\n
\nprecedence of the connection, pointers to the user’s send and receive
\nbuffers, pointers to the retransmit queue and to the current segment.”<\/em><\/p>\n# Top 10 most frequency words in Networking-domain\r\n('the', 1323)\r\n('to', 460)\r\n('a', 451)\r\n('is', 415)\r\n('of', 409)\r\n('and', 332)\r\n('in', 295)\r\n('tcp', 219)\r\n('be', 194)\r\n('sequence', 162)<\/pre>\nThe Use of Stop Words<\/h2>\n
1.Sccientific Journal Article with Stop Words Removed<\/h3>\n
# Top 10 most frequency terms after removal of stop words\r\n('efflux', 86)\r\n('s.', 78)\r\n('aureus', 65)\r\n('nora', 57)\r\n('epis', 42)\r\n('antibiotics', 40)\r\n('pumps', 40)\r\n('ciprofloxacin', 37)\r\n('pump', 32)\r\n('norfloxacin,', 29)<\/pre>\n2.RFC for TCP Protocol with Stop Words Removed<\/h3>\n
# Top 10 most frequency terms after removal of stop words\r\n('tcp', 219)\r\n('sequence', 162)\r\n('data', 151)\r\n('connection', 143)\r\n('segment', 141)\r\n('control', 130)\r\n('number', 94)\r\n('1981transmission', 87)\r\n('user', 83)\r\n('send', 79)<\/pre>\n