211 results about "Language understanding" patented technology

A method for assisting a user with one or more desired tasks is disclosed. For example, an executable, generic language understanding module and an executable, generic task reasoning module are provided for execution in the computer processing system. A set of run-time specifications is provided to the generic language understanding module and the generic task reasoning module, comprising one or more models specific to a domain. A language input is then received from a user, an intention of the user is determined with respect to one or more desired tasks, and the user is assisted with the one or more desired tasks, in accordance with the intention of the user.

Methods and systems for dynamic natural language understanding. A hierarchical structure of semantic categories is exploited to assist in the natural language understanding. Optionally, the natural language to be understood includes a request.

A method for assisting a user with one or more desired tasks is disclosed. For example, an executable, generic language understanding module and an executable, generic task reasoning module are provided for execution in the computer processing system. A set of run-time specifications is provided to the generic language understanding module and the generic task reasoning module, comprising one or more models specific to a domain. A language input is then received from a user, an intention of the user is determined with respect to one or more desired tasks, and the user is assisted with the one or more desired tasks, in accordance with the intention of the user.

The invention relates to a method and an apparatus for separating processing for language understanding from an application and its functionality. The application for example contains functionality within a provided domain, whereby the domain and its functionality belongs to a speech controlled device such as a car-radio, a car-AC, a CD-player or almost any program controlled device.

A method for adaptively training a subject, using auditory processing of phonemes within command sentences, to improve the subject's listening comprehension, grammatical parsing, and serial memory is provided. The method utilizes a number of training installments, each designed for testing a particular aspect of the subject's language skills, all tied together by a common story. More specifically, installments are provided that narrate a story, test the subject's listening comprehension to the narrated story, test the subject's ability to grammatically parse increasingly difficult sentence structures, and test the subject's ability to select and manipulate graphical objects in response to auditory commands. Speech processing is used for the narration, as well as for commands within each test to allow the subject to more easily distinguish between similar sounding phonemes. As the subject improves his / her ability to correctly respond to the tests, the amount of processing applied to the commands is reduced, ultimately to the level of normal speech.

Enabling natural language understanding using an X+V page of a multimodal application implemented with a statistical language model (‘SLM’) grammar of the multimodal application in an automatic speech recognition (‘ASR’) engine, with the multimodal application operating in a multimodal browser on a multimodal device supporting multiple modes of interaction including a voice mode and one or more non-voice modes, the multimodal application operatively coupled to the ASR engine through a VoiceXML interpreter, including: receiving, in the ASR engine from the multimodal application, a voice utterance; generating, by the ASR engine according to the SLM grammar, at least one recognition result for the voice utterance; determining, by an action classifier for the VoiceXML interpreter, an action identifier in dependence upon the recognition result, the action identifier specifying an action to be performed by the multimodal application; and interpreting, by the VoiceXML interpreter, the multimodal application in dependence upon the action identifier.

The invention provides distributed cognitive technology for an intelligent emotional robot, which can be applied in the field of multi-channel human-computer interaction in service robots, household robots, and the like. In a human-computer interaction process, the multi-channel cognition for the environment and people is distributed so that the interaction is more harmonious and natural. The distributed cognitive technology comprises four parts, namely 1) a language comprehension module which endows a robot with an ability of understanding human language after the steps of word division, word gender labeling, key word acquisition, and the like; 2) a vision comprehension module which comprises related vision functions such as face detection, feature extraction, feature identification, human behavior comprehension, and the like; 3) an emotion cognition module which extracts related information in language, expression and touch, analyzes user emotion contained in the information, synthesizes a comparatively accurate emotion state, and makes the intelligent emotional robot cognize the current emotion of a user; and 4) a physical quantity cognition module which makes the robot understand the environment and self state as the basis of self adjustment.

The invention discloses natural language understanding method, comprising the steps that: a natural language is matched with a conceptual language symbol after receiving the natural language input by the customer, and then a conception is associated with the conceptual language symbol; a conception which is most suitable to the current language content is selected by being compared with the preset conception dictionary, and then whether the conception is ambiguous is judged; and if the answer is YES, the conception is obtained by a language data base, entering the next step; and if the answer is NO, the conception is obtained based on the principle of language content matched, entering to the next step; a core conception and a sub conception are obtained by a conception reorganization, wherein, the core language meaning of the core conception is defined by an operation of the computer while the sub language meaning of the sub conception is defined by the operation content of the computer; and the complete language meaning is obtained by combining the core language meaning with the sub language meaning. The invention also provides a human-computer interaction intelligent system based on the method provided by the invention. The invention recognizes the natural sound input by the customer more accurately, thereby providing the customer with more intelligent and perfect services.

A method of probabilistic error-tolerant natural language understanding. The process of language understanding is divided into a concept parse and a concept sequence comparison steps. The concept parse uses a parse driven by a concept grammar to construct a concept parse forest set by parsing results of speech recognition. The concept sequence comparison uses an error-tolerant interpreter to compare the hypothetical concept sequences included by the concept parse forest set and the exemplary concept sequences included in the database of the system. A most possible concept sequence is found and converted into a semantic framed that expresses the intention of the user. The whole process is led by a probability oriented scoring function. When error occurs in the speech recognition and a correct concept sequence cannot be formed, the position of the error is determined and the error is recovered according to the scoring function to reduce the negative effect.

This invention concerns a method and system for monitoring an automated dialog system for the automatic recognition of language understanding errors based on a user's input communications in a task classification system. The method may include determining whether the user's input communication can be understood in order to make a task classification decision. If the user's input communication cannot be understood and a task classification decision cannot be made, a probability of understanding the user's input communication may be determined. If the probability exceeds a first threshold, further dialog may be conducted with the user. Otherwise, the user may be directed to a human for assistance. In another possible embodiment, the method operates as above except that if the probability exceeds a second threshold, the second threshold being higher than the first, then further dialog may be conducted with the user using the current dialog strategy. However, if the probability falls between a first threshold and a second threshold, the dialog strategy may be adapted in order to improve the chances of conducting a successful dialog with the user. This process may be cumulative. In particular, the first dialog exchange may be stored in a database. Then, a second dialog exchange is conducted with the user. As a result, a second determination is made as to whether the user's input communication can be understood can be conducted based on the stored first exchange and the current second exchanges. This cumulative process may continue using a third and fourth exchange, if necessary.

A natural language understanding method begins with a radiological report text containing clinical findings. Errors in the text are corrected by analyzing character-level optical transformation costs weighted by a frequency analysis over a corpus corresponding to the report text. For each word within the report text, a word embedding is obtained, character-level embeddings are determined, and the word and character-level embeddings are concatenated to a neural network which generates a plurality of NER tagged spans for the report text. A set of linked relationships are calculated for the NER tagged spans by generating masked text sequences based on the report text and determined pairs of potentially linked NER spans. A dense adjacency matrix is calculated based on attention weights obtained from providing the one or more masked text sequences to a Transformer deep learning network, and graph convolutions are then performed over the calculated dense adjacency matrix.

Methods and systems for dynamic natural language understanding. A hierarchical structure of semantic categories is exploited to assist in the natural language understanding. Optionally, the natural language to be understood includes a request.

Techniques for detecting data anomalies in a natural language understanding (NLU) system are provided. A number of categorized sentences, categorized into a number of categories, are obtained. Sentences within a given one of the categories are clustered into a number of sub clusters, and the sub clusters are analyzed to identify data anomalies. The clustering can be based on surface forms of the sentences. The anomalies can be, for example, ambiguities or inconsistencies. The clustering can be performed, for example, with a K-means clustering algorithm.

A speech understanding apparatus includes a speech recognition unit which performs speech recognition of an utterance using multiple language models, and outputs multiple speech recognition results obtained by the speech recognition, a language understanding unit which uses multiple language understanding models to perform language understanding for each of the multiple speech recognition results output from the speech recognition unit, and outputs multiple speech understanding results obtained from the language understanding, and an integrating unit which calculates, based on values representing features of the speech understanding results, utterance batch confidences that numerically express accuracy of the speech understanding results for each of the multiple speech understanding results output from the language understanding unit, and selects one of the speech understanding results with a highest utterance batch confidence among the calculated utterance batch confidences.

A multi-lingual translation system that provides multiple output sentences for a given word or phrase. Each output sentence for a given word or phrase reflects, for example, a different emotional emphasis, dialect, accents, loudness or rates of speech. A given output sentence could be selected automatically, or manually as desired, to create a desired effect. For example, the same output sentence for a given word or phrase can be recorded three times, to selectively reflect excitement, sadness or fear. The multi-lingual translation system includes a phrase-spotting mechanism, a translation mechanism, a speech output mechanism and optionally, a language understanding mechanism or an event measuring mechanism or both. The phrase-spotting mechanism identifies a spoken phrase from a restricted domain of phrases. The language understanding mechanism, if present, maps the identified phrase onto a small set of formal phrases. The translation mechanism maps the formal phrase onto a well-formed phrase in one or more target languages. The speech output mechanism produces high-quality output speech. The speech output may be time synchronized to the spoken phrase using the output of the event measuring mechanism.

Disclosed is a system and a method for combining the computational resources of numerous embedded devices to enable any of them to perform complex tasks like speech recognition or natural language understanding. A distinguished master device communicates with a network of embedded devices, and organizes them as the nodes of a neural network. To each node (embedded device) in the neural network, the master device sends the activation function for that node and the connectivity pattern for that node. The master device sends the inputs for the network to the distinguished input nodes of the network. During computation, each node computes the activation function of all of its inputs and sends its activation to all the nodes to which it needs to send output to. The outputs of the neural network are sent to the master device. Thus, the network of embedded devices can perform any computation (like speech recognition, natural language understanding, etc.) which can be mapped onto a neural network model.

This language understanding apparatus includes a storage section, a phrase interpreter, and a bidding section. The bidding section reads information on a slot of a semantic frame corresponding to a concept indicated by an information read by the phrase interpreter and information on a concept corresponding to the slot by referring to the storage section, specifies, for each semantic frame read, from the concept representation detected by the phrase interpreter, a concept representation corresponding to a concept corresponding to each slot of the semantic frame or a subordinate concept of that concept, and a concept representation whose slot of another semantic frame is not bidden, and repeats bidding the slot for the specified concept representation, thereby generating slot allocation table data indicating a bidding result.

This invention concerns a method and system for monitoring an automated dialog system for the automatic recognition of language understanding errors based on a user's input communications. The method includes determining whether a probability of understanding the user's input communication exceeds a first thresholds, where if the first threshold is exceeded, further dialog is conducted with the user. Otherwise, the user may be directed to a human for assistance. In another possible embodiment, the method operates as above except that if the probability also exceeds a second threshold, the second threshold being higher than the first, then further dialog is conducted with the user using the current dialog strategy. However, if the probability falls between a first threshold and a second threshold, the dialog strategy may be adapted in order to improve the chances of conducting a successful dialog with the user.

This invention relates to a knowledge database establishing and automatically answer index system based on HNC natural language, which extracts ERE knowledge to establish ERE knowledge database and to define and establish knowledge frame to describe frame knowledge data. The two database receives user inquires by natural language through analysis on question sentences and aim ERE structure extraction.

A language understanding device includes: a language understanding model storing unit configured to store word transition data including pre-transition states, input words, predefined outputs corresponding to the input words, word weight information, and post-transition states, and concept weighting data including concepts obtained from language understanding results for at least one word, and concept weight information corresponding to the concepts; a finite state transducer processing unit configured to output understanding result candidates including the predefined outputs, to accumulate word weights so as to obtain a cumulative word weight, and to sequentially perform state transition operations; a concept weighting processing unit configured to accumulate concept weights so as to obtain a cumulative concept weight; and an understanding result determination unit configured to determine an understanding result from the understanding result candidates by referring to the cumulative word weight and the cumulative concept weight.

Disclosed herein is a system, method and computer readable medium storing instructions related to semantic and syntactic information in a language understanding system. The method embodiment of the invention is a method for classifying utterances during a natural language dialog between a human and a computing device. The method comprises receiving a user utterance; generating a semantic and syntactic graph associated with the received utterance, extracting all n-grams as features from the generated semantic and syntactic graph and classifying the utterance. Classifying the utterance may be performed any number of ways such as using the extracted n-grams, a syntactic and semantic graphs or writing rules.

The invention provides a method and device for constructing a legal text syntax tree based on a deep neutral network. The method comprises the following steps: acquiring a training text of the legal service; performing word vector sparse representation on the training text; performing semantic annotation on the training text through the DNN according to the word vector sparse representation; performing dependency description on the training text according to the semantic annotation to construct the legal text syntax tree. The invention provides a systematic method to construct the syntax tree about technical language understanding in the legal service field, and the global-local training is used for solving the semantic bias problem in the legal language understanding.

The invention discloses a training method, a training device, a dialogue method and a dialogue system of a dialogue model. The training method comprises the steps of total error constructing, whereina total error function comprising a first error of a natural language understanding model and a second error of a strategy generating model are constructed simultaneously; joint training, wherein withthe target of reducing the total error function, a corpus sample is utilized to jointly train the natural language understanding model and the strategy generating model, input of the natural languageunderstanding model is dialogue sentences, output of the natural language understanding model is internal representation obtained by analyzing the dialogue sentences, input of the strategy generatingmodel at least comprises the output of the natural language understanding model, and output of the strategy generating model is motions aiming at the dialogue sentence. Compared with the prior art, when an error occurs on the natural language understanding model or the strategy generating model, the system can normally conduct dialogues, and the error transferring problem in a traditional methodcaused when the natural language understanding model and a dialogue management model are modeled respectively is solved.

A speech understanding apparatus includes a speech recognition unit which performs speech recognition of an utterance using multiple language models, and outputs multiple speech recognition results obtained by the speech recognition, a language understanding unit which uses multiple language understanding models to perform language understanding for each of the multiple speech recognition results output from the speech recognition unit, and outputs multiple speech understanding results obtained from the language understanding, and an integrating unit which calculates, based on values representing features of the speech understanding results, utterance batch confidences that numerically express accuracy of the speech understanding results for each of the multiple speech understanding results output from the language understanding unit, and selects one of the speech understanding results with a highest utterance batch confidence among the calculated utterance batch confidences.

A method of speech recognition and language-understanding analysis is provided. According to a segmental word-concept-tag compound N-gram model, an input speech is divided into a plurality of segmental phrases. Each segmental phrase is attached a tag to indicate whether said segmental phrase is a meaningful segmental phrase or a meaningless segmental phrase. The meaningless segmental phrases are deleted, and only the meaningful segmental phrases are reserved. The language-understanding analysis is carried out to the meaningful segmental phrases according to segmental sub-grammars.

The invention relates to a system and method for processing complex an SMS message of a mobile customer hotline SMS messaging service hall. The system comprises a login module for entering an operator reply module and a knowledge base maintenance module after passing validation of login information, an SMS message access module for receiving SMS messages incapable of being understood by the SMS messaging service hall, a natural language understanding module for understanding the content of an SMS message, a knowledge base module for managing mobile knowledge, an automatic replay module for automatically replaying SMS messages with high recognition rate, an operator replay module for manually reviewing and replying natural language understanding results by an operator, with the replaying content coming from the knowledge base, an answer knowledge search module for acquiring answer knowledge according to the SMS information and the natural language understanding results, a knowledge base searching module for inputting a natural language problem and searching the desired answer knowledge, a knowledge template instantiation module for obtaining a concrete knowledge answer, and a trigger operation module for being triggered to complete execution when the SMS content contains the execution requirement of a business action.

The present invention is a method for assessing a patient's linguistic comprehension using a pupil response system comprising at least one pupillometer configured to measure the patient's pupil responses. The method includes (a) providing the patient with a list of verbal stimuli comprising at least two sets of verbal stimuli, each set of verbal stimuli comprising one or more verbal stimuli; wherein the two sets of the verbal stimuli differ substantially from each other in terms of the difficulty level; (b) presenting to the patient one verbal stimulus at a time from the list of verbal stimuli; (c) measuring and recording the patient's pupil response data for a period of time ranging from 200 milliseconds to 10 seconds during the presentation of each stimulus; and (d) analyzing the pupil response data to assess the patient's linguistic comprehension.

The invention provides an information processing device and an information processing method. The information processing device comprises a touchable display which is used for displaying information. The information processing method comprises the following steps: a detection step of detecting a specific touch operation carried out on information, with characters, displayed on the display; an attention text obtaining step of obtaining a text in the information touched by the touch operation according to the detected specific touch operation and taking the text as an attention text; a first natural language understanding step of carrying out word segmentation and named entity identification-based first natural language understanding processing on the attention text to obtain a word segmentation result and a named entity identification result of the attention text; a second natural language understanding step of carrying out semantic analysis-based second natural language understanding processing on the attention text to obtain a structured semantic meaning, taking a semantic fragment as a unit, of the attention text; and a display step of displaying the attention text on the display in a manner of marking the semantic fragment. According to the device and the method provided by the invention, the information operation convenience of the users is improved.

The invention belongs to the technical field of man-machine conversation, particularly relates to a multi-round spoken language understanding method, system and device based on conversation logic, andaims to solve the problem that an existing multi-round spoken language understanding method is low in historical conversation data utilization rate. The method of the system comprises the steps of obtaining current dialogue data and historical dialogue data; respectively encoding the current dialogue data and the historical dialogue data into an input vector and a memory vector through a bidirectional gated recurrent neural network; generating a context knowledge vector through a memory retrieval method based on an attention mechanism according to the input vector and the memory vector; and obtaining intention classification information and semantic slot filling information of the current dialogue data through a multi-round spoken language understanding model based on the context knowledge vector and the current dialogue data. Historical dialogue data can be efficiently utilized in multiple rounds of spoken language understanding, and the performance of language understanding in multiple rounds of scenes is improved.