9. A document cleaning system for cleaning an electronic document, comprising: a memory; one or more processors, configured to: identify at least one sentence in the electronic document; numerically represent features of the sentence to obtain a numeric feature representation associated with the sentence; input the numeric feature representation into a machine learning classifier, the machine learning classifier being configured to determine, based on each numeric feature representation, whether the sentence associated with that numeric feature representation is a bad sentence; and remove sentences determined to be bad sentences from the electronic document to create a cleaned document, wherein numerically representing features of the sentence to obtain a numeric feature representation associated with the sentence comprises: creating a part of speech feature vector representation by performing part of speech tagging on each word in the sentence and determining a unique number associated with each part-of-speech corresponding to each word in the sentence, each position in the part of speech feature vector representation indicating a frequency of occurrence of a part of speech tag; creating a rule vector feature representation by determining whether the sentence satisfies a plurality of predetermined rules, each position in the rule vector feature representation indicating whether the sentence satisfies a particular one of the plurality of predetermined rules; and obtaining the numeric feature representation by concatenating the part of speech feature vector representation and the rule vector feature representation.
9. A document cleaning system for cleaning an electronic document, comprising: a memory; one or more processors, configured to: identify at least one sentence in the electronic document; numerically represent features of the sentence to obtain a numeric feature representation associated with the sentence; input the numeric feature representation into a machine learning classifier, the machine learning classifier being configured to determine, based on each numeric feature representation, whether the sentence associated with that numeric feature representation is a bad sentence; and remove sentences determined to be bad sentences from the electronic document to create a cleaned document, wherein numerically representing features of the sentence to obtain a numeric feature representation associated with the sentence comprises: creating a part of speech feature vector representation by performing part of speech tagging on each word in the sentence and determining a unique number associated with each part-of-speech corresponding to each word in the sentence, each position in the part of speech feature vector representation indicating a frequency of occurrence of a part of speech tag; creating a rule vector feature representation by determining whether the sentence satisfies a plurality of predetermined rules, each position in the rule vector feature representation indicating whether the sentence satisfies a particular one of the plurality of predetermined rules; and obtaining the numeric feature representation by concatenating the part of speech feature vector representation and the rule vector feature representation. 13. The document cleaning system of claim 9 , wherein numerically representing features of the sentences comprises: identifying, from a dictionary map which maps words to unique numbers, the unique number associated with each word in the sentence; and obtaining a count of the number of occurrences of each word in the sentence; and creating a numeric feature representation in accordance with the unique numbers identified from the dictionary map and the count of the number of occurrences of each word.