Patent Document ID: 9697199
Application ID: 14794805

Base Claim:
1. A method for identifying and extracting ideographic components, comprising: generating a bilingual sentence database after sentence alignment is performed on corpora of multilingual or bilingual text versions of same content; wherein different language texts are further associated by using ideographs; sentences of different language texts express same semantic content, the ideographs of different language texts are implemented by using four types of common ideographic components, and the four types of ideographic components are identified and extracted as follows; S1. reading a sentence and matching a frame ( 102 ); a sentence includes two parts: a sentence frame and a sentence cabin; in each round of identifying and extracting ideographic components, texts of languages A and B are selected; a machine reads a bilingual sentence pair of languages A and B from the bilingual sentence database, inquires a sentence frame database by using the current bilingual sentence pair to perform a sentence frame match operation, integrates the bilingual sentence pair into a matched sentence frame, and displays “Cabin Detection” and “Search, Match, and Label” buttons; after integration, if each sentence cabin is uneven or incomplete or stacking exists, and the “Search, Match, and Label” button is clicked, the machine performs a “search, match, and label” operation, and uses a word string of the sentence of language A to inquire a conventional electronic dictionary and generate a semantic match table for the current sentence pair according to whether the sentence of language B includes a word string corresponding to semantic content; then the cabin extracts a new sentence frame; S2. identifying and labeling sentence cabins ( 103 ); the machine simultaneously identifies bilingual quantity strings, proper noun strings, and article noun strings sequentially, and pre-labels them as sentence cabins; the machine checks the sentence frame format and collates the foregoing pre-labeled sentence cabins as formally labeled sentence cabins; this is a step of identification and extraction in the first round; parts of speech do not need to be identified in identification and extraction starting from a second round; instead, labeled language sentences are used as a template to identify and label sentence cabins of unlabeled language sentences; S3. detecting cabins and extracting a cabin model ( 106 ); the machine detects sentence cabins one by one; in the first round of identification and extraction, the machine counts word strings included in sentence cabins of a sentence of a Pinyin text of language A; if the quantity of word strings included in a sentence cabin is greater than or equal to five original word strings, the machine needs to perform an operation of composing complex words; if the quantity of word strings included in a sentence cabin is greater than or equal to eight original word strings, the machine performs a “search, match, and label” operation by using the current sentence cabin, and generates a semantic match table for the current sentence cabin, so that a cabin model is further extracted as a sentence cabin with a model; the machine extracts the cabin model, and simultaneously identifies bilingual quantity strings, proper noun strings, and article noun strings sequentially, and pre-labels them as cabin eyes; a model example percentage is decreased from 100% gradually; after pre-labeling is completed, if the quantity of word strings of the three types is still greater than 70%, the machine labels zero-article noun strings or other semantic match word strings as cabin eyes under a prerequisite that spacing exists, accepts the's approval or modification or addition of a semantic match word string by using a sense group alignment operation, and continues to pre-label cabin eyes; when conditions for pre-labeling do not exist and the model example percentage is between 50% and 70%, the operation of pre-labeling cabin eyes ends; the pre-labeled cabin eyes are modified as formally labeled according to a left-to-right order of English sentence cabins; differently, in extraction starting from the second round, labeled language sentence cabins are used as a template to identify and label unlabeled language cabin eyes; when the cabin eye quantity is greater than or equal to 1, residual parts after cabin eyes are removed from the current sentence cabin are cabin model components, and they are saved to corresponding language cabin model fields in a cabin model database ( 107 ); and then the machine continues to detect other sentence cabins, until detection of sentence cabins of the whole sentence pair is completed; S4. receiving and storing sense group strings ( 108 ); the machine integrates sentence cabins with cabin models back into the corresponding cabin models, and then searches for the sentence cabins or cabin eyes one by one; with reference to the semantic match table, if word fields of language A and word fields of language B are not empty in records of the match table of the corresponding content and semantic content of texts of languages A and B is aligned, the content is determined to be sense group string components, and fetched from the records in succession, and respectively stored in corresponding language fields in a same record in a sense group database ( 109 ); and then the operation of identifying and extracting the current sentence pair ends.

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Claim 5:
5. The method for identifying and extracting ideographic components according to claim 1 , which is characterized in that: the operation of matching the sentence frames is specifically as follows: a sentence frame string list is generated based on the word strings of the sentence frames and the blanks of the sentence cabins in advance, and indexed; when the sentence frames are matched, the English words are taken down from sentence cases from left to right verbatim and the first frame field is searched from the sentence frame string list and stored in a temporary list; the content of the temporary list is taken out according to the record; the sentence pair cases are inquired through the sentence frame word segments; each segment of the sentence frame strings can be found in the sentence pair cases and matched if orders are identical; and corresponding language sentence frames are taken out according to the sentence frame string list; the “search, match, and label” operation is specifically as follows: by using the matching list which at least comprises the word class field, the A language field and the B language field, A language word strings as units are segmented firstly by the machine, filled in the A language field of the matching list in sequence, and subsequently taken out according to the record; the traditional electronic dictionary is inquired, and the inquired corresponding B language explanation is used for searching whether B sentences contain the word strings or not; if so and longest, the word strings are filled in the B language field and the word classes of the word strings are simultaneously filled in the word class field; if not, the B language field is empty; the operation of sense group alignment is specifically as follows: {circle around (1)} based on the current sentence pair and the current sentence cabin, the B language explanation is extended or supplemented, and filled in the B language field of the matching list; {circle around (2)} bonding and attaching words, phrases or characters; circle around {(3)} if there is inflection, words are stored in the database as other entries, and the B language explanation is filled in the matching list according to the current sentence pair and the current sentence cabin by reference to the traditional dictionary; and {circle around (4)} the compound nouns are combined (below); and the operation of combining the compound nouns is specifically as follows: it is judged according to the semantic content of the current sentence pair and the current sentence cabin that if the semantic content of one unit is equal to that of another unit only when original two entries or more entries are merged, the former is connected through “-”, merged into one entry, known as a compound noun, respectively filled in the A language field and the B language field as one record in the matching list and added to a compound noun database of the sense group database; and nothing is changed if such operation is empty.