Abstract: Edits on a content item, such as a document, are divided into microtasks. The microtasks associated with a document can be automatically identified based on a workflow or can be identified by a user associated with the content item or an administrator. At a later time, the user can complete the microtasks for a content item using an application associated with their smart phone or tablet. The application may present the microtasks in a game-like environment where the user can compete with other users based on metrics such as number of microtasks completed in a day or fastest completion time. In addition, the user can earn rewards such as badges, coupons, or credits by completing microtasks. In this way, users can use time that would have been wasted playing games to complete their content items, while still experiencing some of the fun and competition associated with the games.

Abstract: Edits on a content item, such as a document, are divided into microtasks. The microtasks associated with a document can be automatically identified based on a workflow or can be identified by a user associated with the content item or an administrator. At a later time, the user can complete the microtasks for a content item using an application associated with their smart phone or tablet. The application may present the microtasks in a game-like environment where the user can compete with other users based on metrics such as number of microtasks completed in a day or fastest completion time. In addition, the user can earn rewards such as badges, coupons, or credits by completing microtasks. In this way, users can use time that would have been wasted playing games to complete their content items, while still experiencing some of the fun and competition associated with the games.

Abstract: Edits on a content item, such as a document, are divided into microtasks. The microtasks associated with a document can be automatically identified based on a workflow or can be identified by a user associated with the content item or an administrator. At a later time, the user can complete the microtasks for a content item using an application associated with their smart phone or tablet. The application may present the microtasks in a game-like environment where the user can compete with other users based on metrics such as number of microtasks completed in a day or fastest completion time. In addition, the user can earn rewards such as badges, coupons, or credits by completing microtasks. In this way, users can use time that would have been wasted playing games to complete their content items, while still experiencing some of the fun and competition associated with the games.

Abstract: Described is a technology for performing unsupervised learning using global features extracted from unlabeled examples. The unsupervised learning process may be used to train a log-linear model, such as for use in morphological segmentation of words. For example, segmentations of the examples are sampled based upon the global features to produce a segmented corpus and log-linear model, which are then iteratively reprocessed to produce a final segmented corpus and a log-linear model.

Abstract: Systems and methods for automatically extracting parallel word sequences from comparable corpora are described. Electronic documents, such as web pages belonging to a collaborative online encyclopedia, are analyzed to locate parallel word sequences between electronic documents written in different languages. These parallel word sequences are then used to train a machine translation system that can translate text from one language to another.

Abstract: A model for mapping the raw text representation of a text object to a vector space is disclosed. A function is defined for computing a similarity score given two output vectors. A loss function is defined for computing an error based on the similarity scores and the labels of pairs of vectors. The parameters of the model are tuned to minimize the loss function. The label of two vectors indicates a degree of similarity of the objects. The label may be a binary number or a real-valued number. The function for computing similarity scores may be a cosine, Jaccard, or differentiable function. The loss function may compare pairs of vectors to their labels. Each element of the output vector is a linear or non-linear function of the terms of an input vector. The text objects may be different types of documents and two different models may be trained concurrently.

Abstract: Systems and methods for automatically extracting parallel word sequences from comparable corpora are described. Electronic documents, such as web pages belonging to a collaborative online encyclopedia, are analyzed to locate parallel word sequences between electronic documents written in different languages. These parallel word sequences are then used to train a machine translation system that can translate text from one language to another.

Abstract: Described is a technology for performing unsupervised learning using global features extracted from unlabeled examples. The unsupervised learning process may be used to train a log-linear model, such as for use in morphological segmentation of words. For example, segmentations of the examples are sampled based upon the global features to produce a segmented corpus and log-linear model, which are then iteratively reprocessed to produce a final segmented corpus and a log-linear model.