Patent application number | Description | Published |
20080281806 | SEARCHING A DATABASE OF LISTINGS - A database having listings rather than long documents is searched using a term frequency-inverse document frequency (Tf/Idf) algorithm. | 11-13-2008 |
20100076752 | Automated Data Cleanup - The described implementations relate to automated data cleanup. One system includes a language model generated from language model seed text and a dictionary of possible data substitutions. This system also includes a transducer configured to cleanse a corpus utilizing the language model and the dictionary. | 03-25-2010 |
20100076765 | STRUCTURED MODELS OF REPITITION FOR SPEECH RECOGNITION - Described is a technology by which a structured model of repetition is used to determine the words spoken by a user, and/or a corresponding database entry, based in part on a prior utterance. For a repeated utterance, a joint probability analysis is performed on (at least some of) the corresponding word sequences as recognized by one or more recognizers) and associated acoustic data. For example, a generative probabilistic model, or a maximum entropy model may be used in the analysis. The second utterance may be a repetition of the first utterance using the exact words, or another structural transformation thereof relative to the first utterance, such as an extension that adds one or more words, a truncation that removes one or more words, or a whole or partial spelling of one or more words. | 03-25-2010 |
20110224982 | AUTOMATIC SPEECH RECOGNITION BASED UPON INFORMATION RETRIEVAL METHODS - Described is a technology in which information retrieval (IR) techniques are used in a speech recognition (ASR) system. Acoustic units (e.g., phones, syllables, multi-phone units, words and/or phrases) are decoded, and features found from those acoustic units. The features are then used with IR techniques (e.g., TF-IDF based retrieval) to obtain a target output (a word or words). Also described is the use of IR techniques to provide a full large vocabulary continuous speech (LVCSR) recognizer | 09-15-2011 |
20140037218 | THREE-DIMENSIONAL OBJECT BROWSING IN DOCUMENTS - A document that includes a representation of a two-dimensional (2-D) image may be obtained. A selection indicator indicating a selection of at least a portion of the 2-D image may be obtained. A match correspondence may be determined between the selected portion of the 2-D image and a three-dimensional (3-D) image object stored in an object database, the match correspondence based on a web crawler analysis result. A 3-D rendering of the 3-D image object that corresponds to the selected portion of the 2-D image may be initiated. | 02-06-2014 |
20140067368 | DETERMINING SYNONYM-ANTONYM POLARITY IN TERM VECTORS - A document-term matrix may be generated based on a corpus. A term representation matrix may be generated based on modifying a plurality of elements of the document-term matrix based on antonym information included in the corpus. Similarities may be determined based on a plurality of elements of the term representation matrix. | 03-06-2014 |
20140156260 | GENERATING SENTENCE COMPLETION QUESTIONS - The subject disclosure is directed towards automated processes for generating sentence completion questions based at least in part on a language model. Using the language model, a sentence is located, and alternates for a focus word (or words) in the sentence are automatically provided. Also described is automated filtering candidate sentences to locate the sentence, filtering the alternates based upon elimination criteria, scoring sentences with the correct word and as modified the alternates, and ranking the alternates. Manual selection may be used along with the automated processes. | 06-05-2014 |
20150363393 | DIALOG STATE TRACKING USING WEB-STYLE RANKING AND MULTIPLE LANGUAGE UNDERSTANDING ENGINES - A dialog state tracking system. One aspect of the system is the use of multiple utterance decoders and/or multiple spoken language understanding (SLU) engines generating competing results that improve the likelihood that the correct dialog state is available to the system and provide additional features for scoring dialog state hypotheses. An additional aspect is training a SLU engine and a dialog state scorer/ranker DSR engine using different subsets from a single annotated training data set. A further aspect is training multiple SLU/DSR engine pairs from inverted subsets of the annotated training data set. Another aspect is web-style dialog state ranking based on dialog state features using discriminative models with automatically generated feature conjunctions. Yet another aspect is using multiple parameter sets with each ranking engine and averaging the rankings. Each aspect independently improves dialog state tracking accuracy and may be combined in various combinations for greater improvement. | 12-17-2015 |