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POIdentifying the components in a English sentence that do not make sense
 

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    <p>I'm wondering is there an algorithm or a library which helps me identify the components in an English which has no meaning? e.g., <strong>very serious</strong> grammar error? If so, could you explain how it works, because I would really like to implement that or use that for my own projects.</p> <p>Here's a random example:</p> <p>In the sentence: "I closed so etc page hello the door." </p> <p>As a human, we can quickly identify that [so etc page hello] does not make any sense. Is it possible for a machine to point out that the string does not make any sense and also contains grammar errors?</p> <p>If there's such a solution, how precise can that be? Is it possible, for example, given a clip of an English sentence, the algorithm returns a measure, indicating how meaningful, or correct that clip is? Thank you very much!</p> <p>PS: I've looked at CMU's link grammar as well as the NLTK library. But still I'm not sure how to use for example link grammar parser to do what I would like to do as the if the parser doesn't accept the sentence, I don't know how to tweak it to tell me which part it is not right.. and I'm not sure whether NLTK supported that.</p> <p>Another thought I had towards solving the problem is to look at the frequencies of the word combination. Since I'm currently interested in correcting very serious errors only. If I define the "serious error" to be the cases where words in a clip of a sentence are rarely used together, i.e., the frequency of the combo should be much lower than those of the other combos in the sentence. </p> <p>For instance, in the above example: [so etc page hello] these four words really seldom occur together. One intuition of my idea comes from when I type such combo in Google, no related results jump out. So is there any library that provides me such frequency information like Google does? Such frequencies may give a good hint on the correctness of the word combo.</p>
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