9. A method comprising: receiving, from a third-party electronic device at one or more computer processing modules, a title for a product; dividing, at the one or more computer processing modules, the title into a sequence of tokens; storing, by the one or more computer processing modules onto one or more non-transitory storage modules, the sequence of tokens; determining, at the one or more computer processing modules, a type of each token of the sequence of tokens using machine learning algorithms comprising a sequence labeling model, wherein the sequence labeling model comprises a set of feature functions, each of the set of feature functions comprising: f ⁡ ( x , y , i ) = { 1 ⁢ ⁢ if ⁢ ⁢ x i = the ⁢ ⁢ and ⁢ ⁢ y i = DT 0 ⁢ ⁢ otherwise wherein each x consists of a different token of the sequence of tokens, each y consists of a first type of label for each token of the sequence of tokens, and DT is a first label assigned to a determiner; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the type of each token of the sequence of tokens; encoding, at the one or more computer processing modules, each token of the sequence of tokens to indicate a second type of label for each token of the sequence of tokens, wherein the second type of label for each token of the sequence of tokens is based on the type of each token of the sequence of tokens and is chosen from a BIO encoding scheme, wherein a label B of the BIO encoding scheme indicates a first token of a brand name, a label I of the BIO encoding scheme indicates a subsequent token of the brand name, and a label O of the BIO encoding scheme indicates a token that is not part of the brand name; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the second type of label for each token of the sequence of tokens; determining, at the one or more computer processing modules, an attribute for each token of the sequence of tokens, the attribute comprising the brand name from each token of the sequence of tokens using a label for each token of the sequence of tokens; normalizing, at the one or more computer processing modules, the attribute for each token of the sequence of tokens to create standardized representations of the attribute for each token of the sequence of tokens; writing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the attribute for each token of the sequence of tokens to empty database entries associated with the product; and facilitating a representation of the attribute for each token of the sequence of tokens on a user display in response to a search request from a user.
9. A method comprising: receiving, from a third-party electronic device at one or more computer processing modules, a title for a product; dividing, at the one or more computer processing modules, the title into a sequence of tokens; storing, by the one or more computer processing modules onto one or more non-transitory storage modules, the sequence of tokens; determining, at the one or more computer processing modules, a type of each token of the sequence of tokens using machine learning algorithms comprising a sequence labeling model, wherein the sequence labeling model comprises a set of feature functions, each of the set of feature functions comprising: f ⁡ ( x , y , i ) = { 1 ⁢ ⁢ if ⁢ ⁢ x i = the ⁢ ⁢ and ⁢ ⁢ y i = DT 0 ⁢ ⁢ otherwise wherein each x consists of a different token of the sequence of tokens, each y consists of a first type of label for each token of the sequence of tokens, and DT is a first label assigned to a determiner; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the type of each token of the sequence of tokens; encoding, at the one or more computer processing modules, each token of the sequence of tokens to indicate a second type of label for each token of the sequence of tokens, wherein the second type of label for each token of the sequence of tokens is based on the type of each token of the sequence of tokens and is chosen from a BIO encoding scheme, wherein a label B of the BIO encoding scheme indicates a first token of a brand name, a label I of the BIO encoding scheme indicates a subsequent token of the brand name, and a label O of the BIO encoding scheme indicates a token that is not part of the brand name; storing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the second type of label for each token of the sequence of tokens; determining, at the one or more computer processing modules, an attribute for each token of the sequence of tokens, the attribute comprising the brand name from each token of the sequence of tokens using a label for each token of the sequence of tokens; normalizing, at the one or more computer processing modules, the attribute for each token of the sequence of tokens to create standardized representations of the attribute for each token of the sequence of tokens; writing, by the one or more computer processing modules onto the one or more non-transitory storage modules, the attribute for each token of the sequence of tokens to empty database entries associated with the product; and facilitating a representation of the attribute for each token of the sequence of tokens on a user display in response to a search request from a user. 16. The method of claim 9 , wherein: determining the type of each token of the sequence of tokens comprises: storing the first token of the sequence of tokens associated with the label B of the BIO encoding scheme as a beginning of a brand name token; concatenating to the brand name token of each subsequent token of the sequence of tokens that is associated with the label I of the BIO encoding scheme; comparing the brand name token to a blacklist database; changing a label of the first token when the brand name token is part of the blacklist database; wherein: the blacklist database comprises tokens that have been determined to not be the brand name; gathering each unique standardized representation of the brand name; determining a correctness of each unique standardized representation of the brand name; and when the standardized representation of the brand name is not correct, creating an entry in the normalization dictionary to reflect a correct standardized representation of the brand name, wherein the standardized representations comprise the standardized representation; and further wherein normalizing the attribute for each token of the sequence of tokens to create the standardized representations of the attribute for each token of the sequence of tokens further comprises: comparing the brand name token with a normalization dictionary to determine the standardized representation of the brand name; writing the standardized representation of the brand name to a database entry associated with the product; gathering each unique standardized representation of the brand name; determining the correctness of each unique standardized representation of the brand name; and when the standardized representation of the brand name is not correct, creating an entry in the normalization dictionary to reflect the correct standardized representation of the brand name.