Patent Document ID: 8645418
Application ID: 13465465

Base Claim:
1. A word mining and evaluating method, the method comprising: calculating a Document Frequency (DF) of a word in mass categorized data; evaluating the word in multiple single-aspects according to the DF of the word; and evaluating the word in a multiple-aspect according to the evaluations in the multiple single-aspects to obtain an importance weight of the word; wherein the evaluating the word in a multiple-aspect according to the evaluations in the multiple single-aspects to obtain the importance weight of the word comprises, classifying candidate words into levels according to DFs of the candidate words, wherein the levels comprises a SuperHigh level, a MidHigh level, a MidLow level and a SuperLow level; and for each candidate word in the SuperHigh level, the MidHigh level or the MidLow level, determining the importance weight of the candidate word according to, an absolute value of a difference between an average inverse document frequency (AVAIDF) and an inverse document frequency (IDF) of the candidate word, a linear combination of mutual information (MI), expect cross entropy (ECE) and entropy (ENT) of the candidate word, a combination of logarithmic normalized chi-square and information gain (IG) of the candidate word, and logarithmic normalized selective preference (SELPRE) of the candidate word; and for each candidate word in the SuperLow level, determining the importance weight of the candidate word according to, an absolute value of a difference between an average inverse document frequency (AVAIDF) and an inverse document frequency (IDF) of the candidate word, a linear combination of mutual information (MI), expect cross entropy (ECE) and entropy (ENT) of the candidate word, and a combination of logarithmic normalized chi-square and information gain (IG) of the candidate word.

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Claim 5:
5. The method of claim 1 , wherein the classifying the candidate words into levels according to the DFs comprises: determining the levels according to the DF of each word in all the categorized data; and classifying each word into a corresponding level according to the DF of the word in all the categorized data.