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: 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 search system for collecting aliases associated with an entity name, includes a storage module configured to store at least one first lexical pattern, a search module coupled to the storage module and configured to obtain a plurality of first snippets from a database according to the entity name, and an alias extracting module coupled to the storage module and the search module separately and configured to, according to the entity name and the first lexical pattern, determine whether any first alias exists in the first snippets. If a first alias exists, the alias extracting module extracts it out and stores it in the storage module.

Abstract: A search system for collecting aliases associated with an entity name, includes a storage module configured to store at least one first lexical pattern, a search module coupled to the storage module and configured to obtain a plurality of first snippets from a database according to the entity name, and an alias extracting module coupled to the storage module and the search module separately and configured to, according to the entity name and the first lexical pattern, determine whether any first alias exists in the first snippets. If a first alias exists, the alias extracting module extracts it out and stores it in the storage module.

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 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.