A method for enabling machine learning from unstructured documents, the method comprising - analyzing, at an electronic device, one or more structured databases to provide a mapping between a plurality of referenced character strings and a corresponding plurality of type labels; wherein a type label indicates the function or meaning of a corresponding referenced character string; wherein the one or more structured databases are any of: an address book database, with the plurality of type labels representing one or more of: surname, first name, street name, house number, city name, city code, state name, country name, telephone number, email address; a calendar database, with the plurality of type labels representing one or more of: date, time, year, month, day, hour, minute, appointment, meeting, birthday, anniversary; a task database, with the plurality of type labels representing one or more of: task, date, time, priority; or a file structure, with the plurality of type labels representing one or more of: folder name, file name, storage drive, URL; - providing, at the electronic device, a first unstructured document comprising a plurality of unstructured character strings; - analyzing, at the electronic device, the first unstructured document to identify a first character string of the plurality of unstructured character strings which is associated with a first referenced character string of the plurality of referenced character strings; - annotating, at the electronic device, within the first unstructured document, the first character string with a first type label which is mapped to the first referenced character string, thereby Indicating the meaning or function of the first character string; - replacing, at the electronic device, the first character string within the first unstructured document by the first type label; - determining, at the electronic device, a training set for machine learning as the first unstructured document comprising the first type label instead of the first character string; - determining, at a central machine learning server, a pattern from the training set; wherein the pattern is a syntactic rule defining a syntactic relationship between the first type label and the first character string; - providing, at the electronic device, a second unstructured document; - applying, at the electronic device, the pattern and the plurality of referenced character strings to the second unstructured document to determine a new referenced character string and a corresponding type label; and - storing, at the electronic device, the new referenced character string within the one or more structured databases in accordance to its corresponding type label, thereby yielding an extended plurality of referenced character strings. - analyzing, at an electronic device, one or more structured databases to provide a mapping between a plurality of referenced character strings and a corresponding plurality of type labels; wherein a type label indicates the function or meaning of a corresponding referenced character string; wherein the one or more structured databases are any of: an address book database, with the plurality of type labels representing one or more of: surname, first name, street name, house number, city name, city code, state name, country name, telephone number, email address; a calendar database, with the plurality of type labels representing one or more of: date, time, year, month, day, hour, minute, appointment, meeting, birthday, anniversary; a task database, with the plurality of type labels representing one or more of: task, date, time, priority; or a file structure, with the plurality of type labels representing one or more of: folder name, file name, storage drive, URL; - providing, at the electronic device, a first unstructured document comprising a plurality of unstructured character strings; - analyzing, at the electronic device, the first unstructured document to identify a first character string of the plurality of unstructured character strings which is associated with a first referenced character string of the plurality of referenced character strings; - annotating, at the electronic device, within the first unstructured document, the first character string with a first type label which is mapped to the first referenced character string, thereby Indicating the meaning or function of the first character string; - replacing, at the electronic device, the first character string within the first unstructured document by the first type label; - determining, at the electronic device, a training set for machine learning as the first unstructured document comprising the first type label instead of the first character string; - determining, at a central machine learning server, a pattern from the training set; wherein the pattern is a syntactic rule defining a syntactic relationship between the first type label and the first character string; - providing, at the electronic device, a second unstructured document; - applying, at the electronic device, the pattern and the plurality of referenced character strings to the second unstructured document to determine a new referenced character string and a corresponding type label; and - storing, at the electronic device, the new referenced character string within the one or more structured databases in accordance to its corresponding type label, thereby yielding an extended plurality of referenced character strings.