Abstract: Technology for improving the predictive accuracy of input word recognition on a device by dynamically updating the lexicon of recognized words based on the word choices made by similar users. The technology collects users' vocabulary choices (e.g., words that each user uses, or adds to or removes from a word recognition dictionary), associates users who make similar choices, aggregates related vocabulary choices, filters the words, and sends words identified as likely choices for that user to the user's device. Clusters may include, for example, users in a particular location (e.g., sets of people who use words such as “Puyallup,” “Gloucester,” or “Waiheke”), users with a particular professional or hobby vocabulary, or application-specific vocabulary (e.g., word choices in map searches or email messages).

Abstract: Technology for improving the predictive accuracy of input word recognition on a device by dynamically updating the lexicon of recognized words based on the word choices made by similar users. The technology collects users' vocabulary choices (e.g., words that each user uses, or adds to or removes from a word recognition dictionary), associates users who make similar choices, aggregates related vocabulary choices, filters the words, and sends words identified as likely choices for that user to the user's device. Clusters may include, for example, users in a particular location (e.g., sets of people who use words such as “Puyallup,” “Gloucester,” or “Waiheke”), users with a particular professional or hobby vocabulary, or application-specific vocabulary (e.g., word choices in map searches or email messages).

Abstract: The disclosed system provides an efficient method of using a later word or words entered after a previous word along with one or more language models that show which words are likely to occur together to identify a better disambiguated choice for the previous word. To identify the better disambiguated choice for the previous word, the system can evaluate the conditional probability for the later word of various candidate previous words, and select the candidate previous word that has the highest conditional probability. If the conditional probability of the selected candidate previous word exceeds that of the previous word that was entered by at least a factor, then the system can include the selected candidate previous word in a selection list for user selection. The disclosed system also provides an efficient method for using one or more language models and a later word to correct errors in segmenting the word.

Abstract: Technology for improving the predictive accuracy of input word recognition on a device by dynamically updating the lexicon of recognized words based on the word choices made by similar users. The technology collects users' vocabulary choices (e.g., words that each user uses, or adds to or removes from a word recognition dictionary), associates users who make similar choices, aggregates related vocabulary choices, filters the words, and sends words identified as likely choices for that user to the user's device. Clusters may include, for example, users in a particular location (e.g., sets of people who use words such as “Puyallup,” “Gloucester,” or “Waiheke”), users with a particular professional or hobby vocabulary, or application-specific vocabulary (e.g., word choices in map searches or email messages).

Abstract: System and methods are provided for detecting numerical text strings within a text string and converting those numerical text strings into digit strings. The digit strings may be reflected in real-time, such as when the user is typing a text message. If more than one possible format of the digit string is determined, the system may then provide a selection of the various formats for selection. Once the proper format for the digit string is determined, that digit string may replace the numerical string previously detected in the text string. The text to digit conversion and associated formatting expedites user text entry such that the user is not required to switch keyboard views, (e.g., virtual keyboards). Additionally, converting to digit strings compresses message length, as well as provide other benefits.

Abstract: System and methods are provided for detecting numerical text strings within a text string and converting those numerical text strings into digit strings. The digit strings may be reflected in real-time, such as when the user is typing a text message. If more than one possible format of the digit string is determined, the system may then provide a selection of the various formats for selection. Once the proper format for the digit string is determined, that digit string may replace the numerical string previously detected in the text string. The text to digit conversion and associated formatting expedites user text entry such that the user is not required to switch keyboard views, (e.g., virtual keyboards). Additionally, converting to digit strings compresses message length, as well as provide other benefits.

Abstract: The disclosed system provides an efficient method of using a later word or words entered after a previous word along with one or more language models that show which words are likely to occur together to identify a better disambiguated choice for the previous word. To identify the better disambiguated choice for the previous word, the system can evaluate the conditional probability for the later word of various candidate previous words, and select the candidate previous word that has the highest conditional probability. If the conditional probability of the selected candidate previous word exceeds that of the previous word that was entered by at least a factor, then the system can include the selected candidate previous word in a selection list for user selection. The disclosed system also provides an efficient method for using one or more language models and a later word to correct errors in segmenting the word.

Abstract: System and methods are provided for detecting numerical text strings within a text string and converting those numerical text strings into digit strings. The digit strings may be reflected in real-time, such as when the user is typing a text message. If more than one possible format of the digit string is determined, the system may then provide a selection of the various formats for selection. Once the proper format for the digit string is determined, that digit string may replace the numerical string previously detected in the text string. The text to digit conversion and associated formatting expedites user text entry such that the user is not required to switch keyboard views, (e.g., virtual keyboards). Additionally, converting to digit strings compresses message length, as well as provide other benefits.

Abstract: Technology is described for developing a language model for a language recognition system from scratch based on aggregating and analyzing text input from multiple users of the language. The technology allows a user to select a language, and if no existing language model is available for the selected language, provides a new language model for the selected language, monitors and collects information about the use of words in the selected language, combines information collected from multiple users of the selected language, and updates the user's language model based on the combined information from multiple users of the selected language.

Abstract: The present technology describes context based text input, which uses linguistic models based on conditional probabilities to provide meaningful word completion and modification suggestions, such as auto-capitalization, based on previously entered words. The technology may use previously entered left context words to modify a list of candidate words matching a current user input. The left context may include one or more previously input words followed by a space, hyphen, or another word. The technology may then modify the list of candidate words based on one or more conditional probabilities, where the conditional probabilities show a probability of a candidate list modification given a particular left context. The modifying may comprise reordering the list or modifying properties of words on the list such as capitalization. The technology may then display the modified list of candidate words to the user.

Abstract: Technology for improving the predictive accuracy of input word recognition on a device by dynamically updating the lexicon of recognized words based on the word choices made by similar users. The technology collects users' vocabulary choices (e.g., words that each user uses, or adds to or removes from a word recognition dictionary), associates users who make similar choices, aggregates related vocabulary choices, filters the words, and sends words identified as likely choices for that user to the user's device. Clusters may include, for example, users in a particular location (e.g., sets of people who use words such as “Puyallup,” “Gloucester,” or “Waiheke”), users with a particular professional or hobby vocabulary, or application-specific vocabulary (e.g., word choices in map searches or email messages).

Abstract: The present invention relates to systems and methods that determine intent for received data (e.g., email, voice, graphics . . . ) and respond to the data based on the intent. The systems and methods employ various combinations of features based on shallow and deep linguistic analysis (e.g., semantic and syntactic) to yield very high accuracy. The systems and methods analyze and categorize received data to locate data that can include intent. This data can be further refined by extracting features related to the intent. The features can be utilized by a classifier to determine the intent. If the intent warrants a response, the data are further scrutinized and reformulated to generate a description that is indicative of the intent. The reformulation can include representing the features in a logical form, transforming the form and generating a description of the intent that can be presented to a user visually and/or audibly.

Abstract: A system that facilitates performance of a focused search over a collection of sites comprises a subweb that corresponds to a topic and/or user characteristic(s) that are of interest to the user. The subweb includes a plurality of domains and/or paths (e.g. sites) that are related to the topic and/or the user characteristic(s). Each of the sites within the subweb is assigned a weight that indicates relevance of the site to the desirable topic and/or user characteristic(s). A search engine employs the subweb to facilitate focusing a search over a collection of sites. The search engine receives a query, and utilizes the subweb to focus a search over the selection of sites corresponding to the topic and/or user characteristic(s) represented by the subweb. The results from the search are returned to the user based at least in part upon the relevance weights assigned to the sites within the subweb.

Abstract: The present invention relates to a system and methodology that applies automated learning procedures for determining document relevance and assisting information retrieval activities. A system is provided that facilitates a machine-learned approach to determine document relevance. The system includes a storage component that receives a set of human selected items to be employed as positive test cases of highly relevant documents. A training component trains at least one classifier with the human selected items as positive test cases and one or more other items as negative test cases in order to provide a query-independent model, wherein the other items can be selected by a statistical search, for example. Also, the trained classifier can be employed to aid an individual in identifying and selecting new positive cases or utilized to filter or re-rank results from a statistical-based search.

Abstract: The present invention is directed to recognizing a discourse structure of a body of text. In a preferred embodiment, a discourse structure recognition facility utilizes syntactic information associated with the body of text to generate a discourse structure tree that characterizes the discourse structure of the body of text. The facility first identifies in the body of text a number of clauses. The facility then determines, for each distinct pair of clauses, which of a number of possible discourse relations should be hypothesized between the pair of clauses, based on the syntactic structure and semantic of the body of text relative to the pair of clauses. The facility then applies the hypothesized relations to the clauses in order to produce a discourse structure tree characterizing the discourse structure of the body of text. In certain embodiments, the facility further generates from the produced discourse structure tree a synopsis of the body of text reflecting the primary goals pursued by its author.