Abstract: Methods, systems, and computer-readable storage media for receiving a vocabulary, the vocabulary including text data that is provided as at least a portion of raw data, the raw data being provided in a computer-readable file, associating each word in the vocabulary with a feature vector, providing a sentence embedding for each sentence of the vocabulary based on a plurality of feature vectors to provide a plurality of sentence embeddings, providing a reconstructed sentence embedding for each sentence embedding based on a weighted parameter matrix to provide a plurality of reconstructed sentence embeddings, and training the unsupervised neural attention model based on the sentence embeddings and the reconstructed sentence embeddings to provide a trained neural attention model, the trained neural attention model being used to automatically determine aspects from the vocabulary.

Abstract: Methods, systems, and computer-readable storage media for receiving input data including a set of sentences, each sentence including computer-readable text as a sequence of tokens, providing a memory network with coupled attentions (MNCA), the coupled attentions including an aspect attention and an opinion attention that are coupled by tensor operators for each sentence in the set of sentences, processing the input data through the MNCA to identify a set of aspect terms, and a set of opinion terms, and simultaneously assign a category to each aspect term and each opinion term from a set of categories, and outputting the set of aspect terms with respective categories, and the set of opinion terms with respective categories.

Abstract: Methods, systems, and computer-readable storage media for receiving a vocabulary that includes text data that is provided as at least a portion of raw data, the raw data being provided in a computer-readable file, providing word embeddings based on the vocabulary, the word embeddings including word vectors for words included in the vocabulary, clustering word embeddings to provide a plurality of clusters, each cluster representing an aspect inferred from the vocabulary, determining a respective association score between each word in the vocabulary and a respective aspect, and providing a word ranking for each aspect based on the respective association scores.

Abstract: Methods, systems, and computer-readable storage media for receiving a vocabulary, the vocabulary including text data that is provided as at least a portion of raw data, the raw data being provided in a computer-readable file, associating each word in the vocabulary with a feature vector, providing a sentence embedding for each sentence of the vocabulary based on a plurality of feature vectors to provide a plurality of sentence embeddings, providing a reconstructed sentence embedding for each sentence embedding based on a weighted parameter matrix to provide a plurality of reconstructed sentence embeddings, and training the unsupervised neural attention model based on the sentence embeddings and the reconstructed sentence embeddings to provide a trained neural attention model, the trained neural attention model being used to automatically determine aspects from the vocabulary.

Abstract: Methods, systems, and computer-readable storage media for receiving a vocabulary that includes text data that is provided as at least a portion of raw data, the raw data being provided in a computer-readable file, providing word embeddings based on the vocabulary, the word embeddings including word vectors for words included in the vocabulary, clustering word embeddings to provide a plurality of clusters, each cluster representing an aspect inferred from the vocabulary, determining a respective association score between each word in the vocabulary and a respective aspect, and providing a word ranking for each aspect based on the respective association scores.

Abstract: Methods, systems, and computer-readable storage media for providing action items from audio within an enterprise context. In some implementations, actions include determining a context of audio that is to be processed, providing training data to a speech recognition component, the training data being provided based on the context, receiving text from the speech recognition component, processing the text to identify one or more action items by identifying one or more concepts within the text and matching the one or more concepts to respective transitions in an automaton, and providing the one or more action items for display to one or more users.

Abstract: Methods, systems, and computer-readable storage media for providing action items from audio within an enterprise context. In some implementations, actions include determining a context of audio that is to be processed, providing training data to a speech recognition component, the training data being provided based on the context, receiving text from the speech recognition component, processing the text to identify one or more action items by identifying one or more concepts within the text and matching the one or more concepts to respective transitions in an automaton, and providing the one or more action items for display to one or more users.