According to one embodiment, a learning apparatus includes a first rule generator, a feature value calculator, a related word extractor, a second rule generator, and a learning unit. The first rule generator generates a first rule to label the event candidate, the first rule including a keyword of the event candidate. The feature value calculator calculates feature values of other words included in the text other than the event candidate. The related word extractor extracts a related word relating to the keyword from the other words using the feature values. The second rule generator generates a second rule to label the event candidate, the second rule being different from the first rule and including the related word. The learning unit generates learning data associating the keyword, the related word, and labeled event candidate with each other.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A learning apparatus comprising: an event candidate extractor configured to extract a word as an event candidate from a text in accordance with an extraction rule; a first rule generator configured to generate a first rule to label the event candidate, the first rule including a keyword of the event candidate; a feature value calculator configured to calculate feature values of other words included in the text other than the event candidate; a related word extractor configured to extract a related word relating to the keyword from the other words using the feature values; a second rule generator configured to generate a second rule to label the event candidate, the second rule being different from the first rule and including the related word; a labeling unit configured to label the event candidate using the first rule and the second rule; and a learning unit configured to generate learning data associating the keyword, the related word, and the labeled event candidate with each other. 2. The apparatus according to claim 1, wherein the feature value calculator indicates each of the feature values as a vector of a continuous value using context before and after the word included in the text. 3. The apparatus according to claim 1, wherein the related word extractor extracts a word with a high similarity between the keyword and the other words as the related word. 4. The apparatus according to claim 1, wherein the second rule generator generates the second rule by replacing the keyword in the first rule by the related word. 5. The apparatus according to claim 1, wherein the related word extractor further extracts the other words, each having high similarity between relation of the keyword and the event candidate and relation of the other words and a desired word used for the extraction as related words. 6. The apparatus according to claim 5, wherein the second rule generator further generates a second rule to label a word matching the desired word serving as the event candidate, using the first rule, the related word, and the desired word. 7. The apparatus according to claim 5, further comprising an event case data storage configured to store the desired word. 8. The apparatus according to claim 6, further comprising an event case data storage configured to store the desired word. 9. The apparatus according to claim 1, further comprising a corpus storage configured to store the text. 10. A learning method comprising: extracting a word as an event candidate from a text in accordance with an extraction rule; generating a first rule to label the event candidate, the first rule including a keyword of the event candidate; calculating feature values of other words included in the text other than the event candidate; extracting a related word relating to the keyword from the other words using the feature values; generating a second rule to label the event candidate, the second rule being different from the first rule and including the related word; labeling the event candidate using the first rule and the second rule; and generating learning data associating the keyword, the related word, and the labeled event candidate with each other. 11. A non-transitory computer readable medium storing a computer program which is executed by a computer to provide the steps of: extracting a word as an event candidate from a text in accordance with an extraction rule; generating a first rule to label the event candidate, the first rule including a keyword of the event candidate; calculating feature values of other words included in the text other than the event candidate; extracting a related word relating to the keyword from the other words using the feature values; generating a second rule to label the event candidate, the second rule being different from the first rule and including the related word; labeling the event candidate using the first rule and the second rule; and generating learning data associating the keyword, the related word, and the labeled event candidate with each other.