No version of Word after 2000 caught any of the mistakes (oddly, Word 97 scored better) and WordPerfect only identified 40 percent of the errors. When checked against 20 sentences containing the most common writing errors, all the grammar checkers performed fairly miserably. Hendrich says that algorithms developed by Microsoft through machine learning are what drive Word's decisions about whether or not a sentence needs a question mark, or what types of clauses require a comma (pretty tricky stuff, even for us human writers).īut did it work? Daniel Kies, an English professor at the College of Du Page, in Glen Ellyn, Illinois, once conducted a head-to-head test of various grammar checkers ranging from WordPerfect 8, released in the late 1990s, up to Word 2007. Instead, they train the machine on a huge dataset of correct English usage and let the machine learn from the patterns it discovers. With machine learning, Microsoft engineers could go beyond programming each and every grammar rule into the software. Susan Hendrich is a group program manager at Microsoft in charge of the natural language processing teams working on Office. The next step was to introduce machine learning. This is called natural language processing or NLP.
#Passive voice in word for mac how to
Then engineers figured out how to parse a sentence into smaller "chunks" of two or three words to target things like "a/an" agreement. She says that in the early days, the best Word could do was parse a sentence into its component parts of speech and identify simple grammar errors like noun-verb agreement. Mar Ginés Marín is a principal program manager at Microsoft who's been tinkering with the Office grammar editor for the past 17 years. The first automated spell checker shipped with an early version of WordPerfect in 1983, and the first computerized grammar checkers soon followed in both WordPerfect and Microsoft Word. The grammar checker found 62 errors - including 14 instances of a sentence starting with a coordinating conjunction ("and," "but," "or") and nine missing commas - all but one of which Perelman classified as "perfectly grammatical prose." In one exercise, Perelman plugged 5,000 words of a famous Noam Chomsky essay into the e-rater scoring engine by ETS, the company that produces (and grades) the GRE and TOEFL exams. And even worse, they often flagged perfectly good prose as a mistake, known as a false positive.
Citing previous research, he found that grammar checkers only correctly identified errors in student papers 50 percent of the time. Perelman has a beef with grammar checkers, which he claims simply do not work.
You can train the machine for a specific situation, but when you talk about transactions in human language, there's actually a huge number of inferences like that going on all the time." "When I make a statement, I believe that you know what I know about this. "So much of English grammar involves inference and something called mutual contextual beliefs," says Perelman. Simple mistake, you might say, but look what happens when I change the sentence to "The car was parked by the curb." Word underlines it and suggests: "The curb parked the car." That's downright goofy, even for a computer. When I type this sentence into Word, the program dutifully underlines it in green and suggests: "John parked the car." That would be fine if John had parked the car, but what if I meant that the car was physically parked near John?
#Passive voice in word for mac for mac
My admittedly dated version of Microsoft Word (Word for Mac 2011) is programmed to recognize and correct passive voice, a no-no in most grammar circles. Les Perelman, a retired MIT professor and former associate dean of undergraduate education who ran the university's writing program, gave me this one: "The car was parked by John."
That's why certain English sentences are such a pain in the neck for automated grammar checkers. English grammar, on the other hand, contains a near infinite number of possibilities, and whether something is grammatically correct or incorrect can largely depend on subtle clues like context and inference.
Spelling is a finite task with discrete right or wrong answers.
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