Grammar Checker - Technical Issues

Technical Issues

The earliest writing style programs checked for wordy, trite, clichéd, or misused phrases in a text. This process was based on simple pattern matching. The heart of the program was a list of many hundreds or thousands of phrases that are considered poor writing by many experts. The list of suspect phrases included alternative wording for each phrase. The checking program would simply break text into sentences, check for any matches in the phrase dictionary, and flag suspect phrases and show an alternative. These programs could also perform some mechanical checks. For example, they would typically flag doubled words, doubled punctuation, some capitalization errors, and other simple mechanical mistakes.

True grammar checking is more complex. While a computer programming language has a very specific syntax and grammar, this is not so for natural languages. Though it is possible to write a somewhat complete formal grammar for a natural language, there are usually so many exceptions in real usage that a formal grammar is of minimal help in writing a grammar checker. One of the most important parts of a natural language grammar checker is a dictionary of all words in the language, along with the part of speech of each word. The fact that natural words can take many different parts of speech greatly increases the complexity of any grammar checker. A grammar checker will find each sentence in a text, look up each word in the dictionary, and then attempt to parse the sentence into a form that matches a grammar. Using various rules, the program can then detect various errors, such as agreement in tense, number, word order, and so on.

It is also possible to detect some stylistic problems with the text. For example, use of the passive voice is criticized by popular style guides such as The Elements of Style. Grammar checkers may attempt to identify passive sentences and suggest an active-voice alternative.

The software elements required for grammar checking are closely related to some of the development issues that need to be addressed for voice recognition software. In voice recognition, parsing can be used to help predict which word is most likely correct based on part of speech and position in the sentence. In grammar checking, the parsing is used to detect words that fail to follow proper grammar usage.

Recently, research has focused on developing algorithms which are capable of recognizing grammar errors based on the context of the surrounding words. Context-based grammar checkers appear in Microsoft Office 2010, Microsoft Office 2007, Google Wave, Ghotit Dyslexia Software, and WhiteSmoke .