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== Project Description == | == Project Description == | ||
Let me first describe the three kinds of Neural Networks that we can end up making (Depending on the training data available). | |||
:'''A network trained with only the correct spellings of the inbuilt functions''' | |||
This type of network would be very easy to make because only a list of all the existing functions of GNU Octave and no additional data will be required. With this approach, we would end up creating a Neural Network which would easily understand typographic errors caused due to letter substitutions and transportation of adjacent letters. In-fact, this network would understand multiple letter substitutions and transportations also and not only single letter substitutions or transportations. I say this with such confidence because I have already made a working neural network of this type [https://github.com/Sudeepam97/Did_You_Mean]. This network would however, perform poorly if an error is caused due to accidental inclusion or accidental deletion of letters. | |||
:'''A network trained with the correct spellings of the functions and self created errors''' | |||
This would be slightly harder to make but should give us an improved performance. I will create some misspellings of all the functions, by additional inclusion, deletion, substitution, and transportation of one or two letters and then add all these self created misspellings to the dataset which will be used to train the network. Such a network would understand what correct spellings and random typographic errors look like. It will easily understand substitutions and transportations like the previous network but would also be more accurate while predicting errors caused due to additions/deletions. However, it is worth mentioning here that we may create errors while creating errors. Because our training data will be modified randomly, although the chances are rare, the Neural Network may show uncertain behaviour. | |||
:'''A network trained with the correct spellings of the functions and the most common typographic errors''' | |||
To make this kind of Neural Network, we need to know what common typographic errors look like. With that goal in mind, I have already contacted the people behind octave-online.net [https://octave-online.net/] who say that they are happy to support the development of GNU Octave and have shared a list of top 1000 misspellings with me through email. However the users of octave-online.net are only one of the parts of the entire user group. For best results, we would require the involvement of the entire Octave community, which, also implies that it will be the hardest and the most fun Neural Network to make. | |||
By creating a script that would be able to catch typographic errors and by asking the users of GNU Octave to use this script and share the most common spelling errors with us, and training the network on the dataset thus created, we’ll create a Neural Network which would understand what correct spellings and the most common typographic errors look like. Such a network would give good results, almost every-time and with all kinds of errors. This is because when our network knows what common errors are like, most of the times it would know the answer beforehand. For the remaining times, the network would be able to predict the correct answer. | |||
I understand that using Neural Networks may seem like an overkill and that one could think about using traditional data structures like tries, or algorithms like 'edit distance' which are made for exactly these kinds of problems. However, I have chosen neural networks because, after due consideration, as described below, to me, neural networks look like the best solution to minimize the trade-off between speed and accuracy of the feature. | |||
Edit distance, while being accurate, would be the slowest approach of the three, and tries, though fast, would not be able to generalize to unknown typographic errors. Neural networks, however, when trained with proper data, would be highly accurate, would generalize to unknown typographic errors, and because of the fact that ultimately '''a 'trained' Neural Network''' will be merged with core Octave, this approach will be fast as well. Another disadvantage when using tries that I'd like to mention is that, if, say, we are unable to arrange a sufficiently large list of common spelling errors, a trie would fail miserably, however, a neural network even in that case, would easily identify letter substitutions and transportations of adjacent letters. | |||
At a later stage (possibly after GSoC), I could merge the data extraction script with Octave so that the performance of the Network could be improved with time. This could come with an easy disable feature, so that only the users who would like to share their spelling errors would do so. | |||