Abstract: A document sentence concept labeling system, a training method and a labeling method thereof are provided. The labeling method of the document sentence concept labeling system includes the following steps. An unlabeled document and one or more sentence concepts are inputted to a pre-trained language model to obtain a set of word embeddings of the unlabeled document. The set of word embeddings of the unlabeled document is inputted into a document analysis model to obtain a start position and an end position of a sentence set corresponding to each of the sentence concepts in the unlabeled document. According to each of the start positions and each of the end positions, each of the sentence sets is obtained.

Abstract: A system for adaptively adjusting related search words are provided. The system includes an input device, a search log collection module, a threshold setting module and a process evolution module. The input device receives a search word. The search log collection module determines whether the cumulative search count of the search word is greater than a first threshold or less than a second threshold. The threshold setting module sets the first threshold and the second threshold in terms of the number of search logs. When the cumulative search count of the search word is between the first threshold and the second threshold, the process evolution module optimizes the middle search process to find out at least one related word and/or at least one historical search word most related to the attributes or content of the search word from the indexed text and the historical search log.

Abstract: A question-answering learning method including the following steps is provided. Firstly, several classifiers are created according to N1 labeled sentences among N sentences. Then, at least one corresponding sentence type of each of the N2 unlabeled sentences among the N sentences is determined by each classifier. Then, N3 sentences are selected from the N2 unlabeled sentences according to a degree of consistency of determined results of the classifiers, wherein the determined results of the N3 sentences are determined by the classifiers and are inconsistent. Then, N4 mutually complementary sentences are selected as to-be-labeled sentences from the N3 sentences. Then, after the N4 selected to-be-labeled sentences are labeled, several classifiers are re-created according to the N1 labeled sentences and the N4 selected to-be-labeled sentences. Then, at least one of the previously created classifiers is added to the currently created classifiers to be members of the classifiers.

Abstract: 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.

Abstract: A system for adaptively adjusting related search words are provided. The system includes an input device, a search log collection module, a threshold setting module and a process evolution module. The input device receives a search word. The search log collection module determines whether the cumulative search count of the search word is greater than a first threshold or less than a second threshold. The threshold setting module sets the first threshold and the second threshold in terms of the number of search logs. When the cumulative search count of the search word is between the first threshold and the second threshold, the process evolution module optimizes the middle search process to find out at least one related word and/or at least one historical search word most related to the attributes or content of the search word from the indexed text and the historical search log.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A recommendation method and a recommender computer system using dynamic language model are provided. The recommender computer system using dynamic language model includes a language model constructing computer module, a language model adapting computer module, a sentence selecting computer module and a sentence recommendation computer module. The language model constructing computer module is used for constructing a language model. The language model adapting computer module is used for dynamically emerging different language models to construct a dynamic language model. The sentence selecting computer module generates a plurality of recommended sentences from a database according to a search keyword. The sentence recommendation computer module analyzes the difference level between the recommended sentences and the dynamic language model and sorts recommended sentences to provide a recommendation list.

Abstract: A speech synthesizer generating system and a method thereof are provided. A speech synthesizer generator in the speech synthesizer generating system automatically generates a speech synthesizer conforming to a speech output specification input by a user. In addition, a recording script is automatically generated by a recording script generator in the speech synthesizer generating system according to the speech output specification, and a customized or expanded speech material is recorded according to the recording script. After the speech material is uploaded to the speech synthesizer generating system, the speech synthesizer generator automatically generates a speech synthesizer conforming to the speech output specification. The speech synthesizer then synthesizes and outputs a speech output at a user end.

Abstract: A teaching material auto expanding method for expending an input teaching material data having at least one sentence unit into a database in a learning material expanding system is disclosed. The database has multiple subjects and structure information corresponding thereto, each subject having a corresponding subject category and each subject category having at least one corresponding sentence unit. First, subject similarity values corresponding to each subject in the database for each of the sentence units in the input teaching material data are separately calculated wherein each subject similarity value comprises a content similarity value and a structure similarity value. A confidence measurement operation is then performed to obtain confidence measurement values of each of the subjects by using the subject similarity value for each sentence unit. Thereafter, an expanding manner for each sentence unit is determined based on the obtained confidence measurement value corresponding thereto.

Abstract: A speech synthesizer generating system and a method thereof are provided. A speech synthesizer generator in the speech synthesizer generating system automatically generates a speech synthesizer conforming to a speech output specification input by a user. In addition, a recording script is automatically generated by a recording script generator in the speech synthesizer generating system according to the speech output specification, and a customized or expanded speech material is recorded according to the recording script. After the speech material is uploaded to the speech synthesizer generating system, the speech synthesizer generator automatically generates a speech synthesizer conforming to the speech output specification. The speech synthesizer then synthesizes and outputs a speech output at a user end.