The "correcter" is a worthy effort, and it needs to start somewhere. It shows the magnitude of the task considering that missing articles are not the most cruc

The "correcter" is a worthy effort, and it needs to start somewhere. It shows the magnitude of the task considering that missing articles are not the most crucial grammatical issues in on-line discourse. The meaning of a phrase is usually comprehensible with or without the article, and native speakers can easily overlook this kind of error made by non-native speakers. OTOH more troublesome to readers are common errors such as misuse of "its" vs. "it's", "to" vs. "too", and "their", "there" and "they're". These mistakes are quite prevalent among native-speaking writers so more ubiquitous than the missing article problem. The "correcter" didn't correct the latter class of errors. Understandably this would be a much harder goal to accomplish given the highly contextual nature of grammatically correct word choices. It prompts a question about how well the data-driven approach can handle the problem. Obviously that's what the research is trying to answer. It sure seems to point to something fairly easy for a human to do that's near or at the limit of what we can get a computer to do.