Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing entities using observations. In one aspect, a method includes receiving data identifying an entity; generating a user interface document that, when rendered by a user device, presents a plurality of attribute values to a user and allows the user to modify one or more of the plurality of attribute values; and storing an observation in a data store, the observation including a user-modified value of one of the plurality of attribute values and a context including one or more of the presented attribute values.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing entities using observations. In one aspect, a method includes receiving data identifying an entity; generating a user interface document that, when rendered by a user device, presents a plurality of attribute values to a user and allows the user to modify one or more of the plurality of attribute values; and storing an observation in a data store, the observation including a user-modified value of one of the plurality of attribute values and a context including one or more of the presented attribute values.

Abstract: Systems and methods for identifying music in a noisy environment are described. One of the methods includes receiving audio segment data. The audio segment data is generated from the portion that is captured in the noisy environment. The method further includes generating feature vectors from the audio segment data, identifying phonemes from the feature vectors, and comparing the identified phonemes with pre-assigned phoneme sequences. Each pre-assigned phoneme sequence identifies a known music piece. The method further includes determining an identity of the music based on the comparison.

Abstract: In general, the subject matter described in this specification can be embodied in methods, systems, and program products for performing compounded text segmentation. Compounded text that is extracted from one or more search queries submitted to a search engine is received. The compounded text includes a plurality of individual words that are joined together without intervening spaces. An electronic dictionary including words is accessed. A data structure representing possible segmentations of the compounded text is generated based on whether words in the possible segmentations occur in the electronic dictionary. A data store comprising data associated with a same field of usage as the compounded text is accessed to determine a frequency of occurrence for possible segmentations of the data structure. A segmentation of the compounded text that is most probable based on the data is determined. A language model is trained using the determined segmentation of the compounded text.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, relating to language models stored for digital language processing. In one aspect, a method includes the actions of generating a language model, including: receiving a collection of n-grams from a corpus, each n-gram of the collection having a corresponding first probability of occurring in the corpus, and generating a trie representing the collection of n-grams, the trie being represented using one or more arrays of integers, and compressing an array representation of the trie using block encoding; and using the language model to identify a second probability of a particular string of words occurring.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing entities using observations. In one aspect, a method includes receiving an observation, the observation including an updated piece of information about an entity and a context, wherein the context includes at least one value of an attribute describing the entity to which the updated piece of information relates; matching the received observation with a first cluster of observations representing the entity using the context; and associating the received observation with the first cluster of observations.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for managing entities using observations. In one aspect, a method includes receiving data identifying an entity; generating a user interface document that, when rendered by a user device, presents a plurality of attribute values to a user and allows the user to modify one or more of the plurality of attribute values; and storing an observation in a data store, the observation including a user-modified value of one of the plurality of attribute values and a context including one or more of the presented attribute values.

Abstract: An improved system and method is provided for feature selection for text classification using subspace sampling. A text classifier generator may be provided for selecting a small set of features using subspace sampling from the corpus of training data to train a text classifier for using the small set of features for classification of texts. To select the small set of features, a subspace of features from the corpus of training data may be randomly sampled according to a probability distribution over the set of features where a probability may be assigned to each of the features that is proportional to the square of the Euclidean norms of the rows of left singular vectors of a matrix of the features representing the corpus of training texts. The small set of features may classify texts using only the relevant features among a very large number of training features.

Abstract: An improved system and method is provided for feature selection for text classification using subspace sampling. A text classifier generator may be provided for selecting a small set of features using subspace sampling from the corpus of training data to train a text classifier for using the small set of features for classification of texts. To select the small set of features, a subspace of features from the corpus of training data may be randomly sampled according to a probability distribution over the set of features where a probability may be assigned to each of the features that is proportional to the square of the Euclidean norms of the rows of left singular vectors of a matrix of the features representing the corpus of training texts. The small set of features may classify texts using only the relevant features among a very large number of training features.