Method and system for intelligently understanding user query intention
A technology for querying intentions and users, applied in the field of intelligently understanding user querying intentions, can solve problems such as incorrect acquisition of user querying intentions, failure to make better use of useful information, and different levels of thinking, so as to reduce the cumbersomeness of system operations, Improve word segmentation efficiency and improve processing speed
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Embodiment 1
[0109] Taking the input query statement "China Merchants Bank's net profit and operating income" as an example, by understanding the query statement, we hope to obtain the user's real query intention:
[0110] The first step is to combine the dictionary and obtain word segmentation results through the forward maximum matching method:
[0111] China Merchants / Bank / Net Profit / Operation / Revenue / ;
[0112] The second step is to perform part-of-speech tagging on the part-of-speech results through the hidden Markov model obtained through training. The part-of-speech tagging results are:
[0113] China Merchants_v Bank_n Net Profit_n Business_n Revenue_v;
[0114] In the third step, the conditional random field model obtained through training is used for marking and named entity recognition, and the result is:
[0115] Marking: Merchants bank net profit business income
[0116] Entity identification: China Merchants Bank
[0117] In the fourth step, the grammatical rule...
Embodiment 2
[0122] Enter the query statement "As of March 31, 2016, the company's total liabilities were 1.036 billion, mainly composed of: short-term loans (including long-term loans due this year) 960 million, long-term loans 55 million, and accounts payable 7.07 million RMB 510,000, tax payable is RMB 510,000. The current loan scale is RMB 1.015 billion, and short-term loans account for 93% of the total liabilities, indicating that the company has a relatively large debt repayment pressure in the short term. Combined with the company’s existing monetary funds of RMB 762 million From the looks of it, the financial risk is not big." As an example, by understanding the query statement, we hope to obtain the user's real query intention:
[0123] The first step is to combine the dictionary and obtain word segmentation results through the forward maximum matching method:
[0124] Short-term / loan / (including / long-term / loan / of / this year / due / ) / 960 million / , / long-term / loan / 55 million / yuan / ...
Embodiment 3
[0139] The input query sentence is the same as that in Embodiment 2, and the method for understanding the user's query intention is the same as that in Embodiment 2, the only difference being that word segmentation results are obtained through the conditional random field model. The effect of conditional random field word segmentation model is shown in Table 9.
[0140] Table 9 Conditional Random Field Word Segmentation Model Effect
[0141] data set time Accuracy recall rate F value pku_test (510KB) 1.676s 0.931 0.919 0.925 msr_test (560KB) 1.928s 0.859 0.894 0.876
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