System and methods for carrying out semantic classification on unknown words
A technology of unknown words and semantics, applied in the field of natural language processing, can solve problems such as unobtainable performance and reduced classification accuracy, and achieve the effects of saving time, large coverage, and improving work efficiency
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example 1
[0078] Example 1: Suppose an unknown word "muscle" is input. The similar word retrieval means 1202 can retrieve the similar word "skin and flesh" from the dictionary. Comparing its different parts "muscle" and "skin", C(muscle)=C(skin)=c. Since C (skin and flesh)=c is known, it can be determined that the semantic class to which the unknown word "muscle" belongs is also c, that is, C (muscle)=c.
example 2
[0079] Example 2: Assume that an unknown word "vegetable" is input. The similar word retrieval means 1202 can retrieve three similar words "green garlic", "green beans" and "green hemp" from the dictionary. And, these three similar words all satisfy following condition: C (vegetable)=C (garlic)=C (green garlic)=c1, C (vegetable)=C (bean)=C (green bean)=c1, C (vegetable) =C (hemp)=C (green hemp)=c2. That is, in this case, for the unknown word "green vegetables", there are two different classification results. As mentioned above, in this case, the result selection means 1204 can select one of the results by voting. Since the words green garlic and green beans both support the c1 category, and only the green ma word supports the c2 category, the unknown word "green vegetable" is classified into c1 by voting.
[0080] The three semantic classification methods for unknown words according to the first, second and third embodiments of the present invention have been described in d...
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