Industrial Technology Research Institute

Yi-Chung Lin

Huei-Ming Wang

Chung-Jen Chiu

Peng-Hsiang Hung

Tung-Hui Chiang

The present invention relates to an error-tolerant language understanding, system and method. The system and the method is using example sentences to provide the clues for detecting and recovering errors. The procedure of detection and recovery is guided by a probabilistic scoring function which integrated the scores from the speech recognizer, concept parser, the scores of concept-bigram and edit operations, such as deleting, inserting and substituting concepts. Meanwhile, the score of edit operations refers the confidence measure achieving more precise detection and recovery of the speech recognition errors. That said, a concept with lower confidence measure tends to be deleted or substituted, while a concept with higher one tends to be retained.

Error-tolerant language understanding system and method

A method for named-entity (NE) recognition and verification is provided. The method can extract at least one to-be-tested segments from an article according to a text window, and use a predefined grammar to parse the at least one to-be-tested segments to remove ill-formed ones. Then, a statistical verification model is used to calculate the confidence measurement of each to-be-tested segment to determine where the to-be-tested segment has a named-entity or not. If the confidence measurement is less than a predefined threshold, the to-be-tested segment will be rejected. Otherwise, it will be accepted.

Method for named-entity recognition and verification

The present invention relates to an example-based concept-orietned data extraction method. In an example labeling phase, the exemplary data string is converted into an exemplary token sequence, in which the target concepts and filler concepts are labeled to be tuples for use as an example, and thus an exemplary concept graph is constructed. In the data extraction phase, the untested data string is converted into an untested token sequence to be processed, and, based on the associated concept recognizers defined by the tuples in the example labeling phase, it is able to detect the concept candidates and establish the composite concepts and aggregate concepts, thereby constructing a hypothetical concept graph. After comparing the exemplary concept graph with the hypothetical concept graph, the optimal hypothetical concept sequence in the hypothetical graph is determined, so as to extract the targeted data from the matched target concepts.

Computer implemented example-based concept-oriented data extraction method

A method of phrase verification to verify a phrase not only according to its confidence measures but also according to neighboring concepts and their confidence tags. First, an utterance is received, and the received utterance is parsed to find a concept sequence. Subsequently, a plurality of tag sequences corresponding to the concept sequence is produced. Then, a first score of each of the tag sequences is calculated. Finally, the tag sequence of the highest first score is selected as the most probable tag sequence, and the tags contained therein are selected as the most probable confidence tags, respectively corresponding to the concepts in the concept sequence.

Method of phrase verification with probabilistic confidence tagging

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.

Method for probabilistic error-tolerant natural language understanding

A portable dialogue management system includes a dialogue manager and a hierarchical task description table. The hierarchical task description table has a plurality of base tables connected with a hierarchical structure. Each base table defines the strategy of a sub-dialogue and stores the dialogue states, a number of domain parameters, and a plurality of response actions corresponding to each dialogue state. The dialogue manager manages the dialogue state of a dialogue system, determines the dialogue state and executes the appropriate response action. Because the domain knowledge is defined in the hierarchical task description table and the dialogue manager is not dependent on the application domain, the dialogue management system is easily portable to different applications. A stack may also be used to push in or pop up a dialogue state so that dialogues of multiple purposes can be accomplished.

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Keelung, Taiwan

Yi-Chung Lin