Natural language level and syntax analytic method based on historical information
A technology of syntactic analysis and natural language, applied in special data processing applications, instruments, electronic digital data processing, etc., can solve problems that restrict the development of Chinese information processing, and achieve the effect of improving performance, improving accuracy, and simple and effective methods
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Embodiment 1
[0047] Embodiment one: see attached figure 1 As shown, a hierarchical syntactic analysis based on historical information, for any sentence that needs to be processed, the following steps are performed:
[0048] ① If the sentence is in Chinese and word segmentation is not performed, call the word segmentation module to segment the sentence; if the sentence is in English or Chinese that has already been divided into words, skip this step;
[0049] ② Call the part-of-speech tagging module, that is, find the part-of-speech for each word in the sentence; keep the top K best tagging results;
[0050] ③ Carry out basic phrase recognition on the top K best part-of-speech tagging results respectively; finally retain the top K best basic phrase recognition results;
[0051] ④ According to the number of chunks in each basic phrase recognition result, store them in the corresponding array unit, for example, if the basic phrase recognition result contains m chunks, store them in the array...
Embodiment 2
[0060] Embodiment two: see attached figure 2 As shown in , after obtaining an intermediate level analysis result, according to the pre-established feature template, the context features are extracted from left to right for each block unit to predict its label:
[0061] from figure 2 See, the current intermediate result is "(NP(Bush_NR))(PP(at_Pyesterday_NT afternoon_NT))(PP(from_PNanjing_NR))(VV(arrival_VV) )(NP(Shanghai_NR))(PU(._PU))”, wherein the italicized word in brackets is the central word of this group, such as the group “(PP(from_PNanjing_NR))” The central word is "自". And a decision has been made on the first three unit blocks “(NP(Bush_NR))(PP(at_Pyesterday_NT afternoon_NT))(PP(since_PNanjing_NR))”, respectively are "O", "O" and "O"; the next step is to predict the decision for the fourth chunk "(VV(Arrival_VV))". The basis for decision-making prediction is the context information of the current block. Therefore, according to the feature template, features are...
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