Abstract:
This invention provides a device and method for language model switching and adaptation, wherein the device comprises a notification manager which notifies a language model switching section of the current status information or the request for the language model of an destination application when the status of the destination application is changed; a language model switching section which selects one or more language models to be switched from a language model set according to the received current status information or the request; a LMB engine decodes a user input using the one or more selected language models; and a language model adaptation section which receives the decoded result and modifies the one or more selected language models based on the decoded result. Therefore, the user input is more accurate even if the language model switching section performs different switches among different language models and the performance of the language models are improved by the language model adaptation section.
Abstract:
A method and system for cleaning an electronic document are provided. The method comprises: identifying at least one sentence in the electronic document; numerically representing features of the sentence to obtain a numeric feature representation associated with the sentence; inputting the numeric feature representation into a machine learning classifier, the machine learning classifier being configured to determine, based on each numeric feature representation, whether the sentence associated with that numeric feature representation is a bad sentence; and removing sentences determined to be bad sentences from the electronic document to create a cleaned document.
Abstract:
The present invention provides an apparatus and a method for categorizing entities based on time-series relation graphs. In each of the time-series relation graphs within a prescribed time period, nodes represent entities, and links between the nodes represent entity relations in a corresponding time unit. The inventive apparatus for categorizing entities based on time-series relation graphs comprises: a time-series relation graph categorizing means for categorizing the nodes in each of the time-series relation graphs to generate a node category result for the corresponding time unit in time sequence; and a category result post-processing means for post-processing all the node category results for the corresponding time units in time sequence generated by the time-series relation graph categorizing means to generate finally categorized nodes.
Abstract:
This invention provides a dictionary learning method, said method comprising the steps of: learning a lexicon and a Statistical Language Model from an untagged corpus; integrating the lexicon, the Statistical Language Mode and subsidiary word encoding information into a small size dictionary. And this invention also provides an input method on a user terminal device using the dictionary with Part-of-Speech information and a Part-of-Speech Bi-gram Model added, and a user terminal device using the same. Therefore, sentence level prediction and word level prediction can be given by the user terminal device and the input is speeded up by using the dictionary which is searched by a Patricia Tree index of a dictionary index.
Abstract:
This utility model relates to a kind of self-balancing scooter accessory connecting structure and accessory, comprising at least a clamping mechanism used to clamp the driving parts. The said clamping mechanism comprises the 1st gripper and the 2nd gripper. The said 1st gripper and the 2nd gripper may be mounted in a way permitting relative motion. The said 1st gripper comprises the 1st connection and 1st clamp plate. The said 2nd gripper comprises the 2nd connection and 2nd clamp plate; a self-locking structure, comprising the limit stops and length adjustment mechanism. The said 1st connection and 2nd connection are equipped with the said limit stop. The said length adjustment mechanism is mounted between two limit stops. The above self-balancing scooter accessory connecting structure is mounted between the self-balancing scooter saddle and driving parts. The 1st clamp plate and the 2nd clamp plate structures are suitable for fastening the driving parts without use of straps. This approach can eliminate safety hazards on velcro strap's susceptibility to breakdown, and make assembly and disassembly more efficiently.
Abstract:
A method for localizing for GPS denied environments is provided with a plurality of broadcasting transmitters and a software-defined radio (SDR). A pre-processing stage and a query phase is executed for localizing an unknown location. The results from the preprocessing stage are made available to the query stage. A simulated power spectrum is computed for a region of interest during the pre-processing phase via the plurality of broadcasting transmitters. A transmitter location, a radius of influence, and a power contour plot of each of the plurality of broadcasting transmitters are used for computing the simulated power spectrum. Next, a peak-finding process, a filtering process, and a localization process is executed during the query phase in order to identify the unknown location.
Abstract:
Profiling systems and methods of creating and using user interest profiles are described. In some example embodiments, the method includes: creating a topic set which includes topics which are organized in a hierarchical structure which includes a plurality of topic levels including an upper topic level and a lower topic level, each topic in the lower topic level being a subtopic of at least one of the topics in the upper topic level; monitoring interest in a plurality of documents for a user to identify one or more documents-of-interest to the user; and based on the monitored interest for the user, creating an interest profile for the user by determining a measure of topical interest for the user for at least one of the topics at the upper topic level and for a subtopic of that topic, the subtopic being at the lower topic level.
Abstract:
A method and system for cleaning an electronic document are provided. The method comprises: identifying at least one sentence in the electronic document; numerically representing features of the sentence to obtain a numeric feature representation associated with the sentence; inputting the numeric feature representation into a machine learning classifier, the machine learning classifier being configured to determine, based on each numeric feature representation, whether the sentence associated with that numeric feature representation is a bad sentence; and removing sentences determined to be bad sentences from the electronic document to create a cleaned document.
Abstract:
A phrase identification system and method are provided. The method comprises: identifying one or more phrase candidates in the electronic document; selecting one of the phrase candidates; numerically representing features of the selected phrase candidates to obtain a numeric feature representation associated with that phrase candidate; and inputting the numeric feature representation into a machine learning classifier, the machine learning classifier being configured to determine, based on each numeric feature representation, whether the phrase candidate associated with that numeric feature representation is a phrase.
Abstract:
This invention provides a device and method for language model switching and adaptation, wherein the device comprises a notification manager which notifies a language model switching section of the current status information or the request for the language model of an destination application when the status of the destination application is changed; a language model switching section which selects one or more language models to be switched from a language model set according to the received current status information or the request; a LMB engine decodes a user input using the one or more selected language models; and a language model adaptation section which receives the decoded result and modifies the one or more selected language models based on the decoded result. Therefore, the user input is more accurate even if the language model switching section performs different switches among different language models and the performance of the language models are improved by the language model adaptation section.