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. 10. The method of claim 9 further comprising: 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; and concatenating to the brand name token each subsequent token of the sequence of tokens that is associated with the label I of the BIO encoding scheme.