Industrial Technology Research Institute

Chung-Jen Chiu

Min-Hsin Shen

Ching-Hsien Li

Chih-Chung Kuo

Shih-Chieh Chien

Yi-Chung Lin

Hsin-Chang Chang

Chieh-Jen Wang

A graph-based natural language optimization method and an electronic apparatus are provided. The method is adapted for an electronic apparatus with a processor. In the method, an input sentence submitted by a user is received, and multiple domain-related entities are extracted from the input sentence. The domain-related entities are input to a graph database to analyze a connection relationship between the domain-related entities, and filling data is obtained from the graph database based on the connection relationship. The input sentence and the filling data are integrated via a natural language processing technology to generate an optimized natural language sentence.

Graph-based natural language optimization method and electronic apparatus

An internet question answering method is applied in an internet question answering system. A current-turn user question is received. Based on a discourse labeling model, a discourse of the current-turn user question is labeled as 'discourse continue' or 'discourse end' online and real-time. Based on the discourse of the current-turn user question, whether to clear at least one discourse of an old boundary is determined. The internet question answering system answers the current-turn user question. A match degree between a next-turn user question and the segment of the dialog log is compared to determine whether the discourse of the current-turn user question is correct, whether to correct the discourse of the current-turn user question, and whether to adjust the discourse labeling model by using the current-turn user question.

Internet question answering system and method, and computer readable recording media

In one exemplary embodiment, a system for discovering suspicious account groups establishes a language model according to the post contents from each account of a first group of accounts during a first time interval, to describe the speech of the account, and compares the similarity among a plurality of language models of the first group of accounts to cluster the first group of accounts; and for a plurality of newly added data during a second time interval, discovers near-synonyms of at least a monitored vocabulary set, and updates the near-synonyms to a plurality of language models of a second group of accounts. The system further integrates the first and the second groups of accounts, and re-clusters an integrated group of accounts.

Method and system for discovering suspicious account groups

A system for leaving and transmitting speech messages automatically analyzes input speech of at least a reminder, fetches a plurality of tag informations, and transmits speech message to at least a message receiver, according to the transmit criterions of the reminder. A command or message parser parses the tag informations at least including at least a reminder ID, at least a transmitted command and at least a speech message. The tag informations are sent to a message composer for being synthesized into a transmitted message. A transmitting controller controls a device switch according to the reminder ID and the transmitted command, to allow the transmitted message send to the message receiver via a transmitting device.

System and method for leaving and transmitting speech messages

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.

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Hsinchu County, Taiwan

Chung-Jen Chiu