Systems and methods described herein utilize supervised machine learning to generate a figure-of-speech prediction model for classify content words in a running text as either being figurative (e.g., as a metaphor, simile, etc.) or non-figurative (i.e., literal). The prediction model may extract and analyze any number of features in making its prediction, including a topic model feature, unigram feature, part-of-speech feature, concreteness feature, concreteness difference feature, literal context feature, non-literal context feature, and off-topic feature, each of which are described in detail herein. Since uses of figure of speech in writings may signal content sophistication, the figure-of-speech prediction model allows scoring engines to further take into consideration a text's use of figure of speech when generating a score.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method of generating a model for predicting whether a content word is being used figuratively, comprising: accessing, with a processing system, a plurality of training texts, each training text including content words, and each content word having a corresponding annotation indicating whether the content word is being used figuratively in the associated training text; accessing, with the processing system, a plurality of topic models generated from a first corpus; for each content word in the plurality of training texts, assigning, with the processing system, a plurality of topic-model feature scores to the content word, each topic-model feature score being associated with one of the plurality of topic models and determined based on: determining, with the processing system, a first probability of the content word being used in a topic represented by the topic model; determining, with the processing system, a second probability of the content word being used in a second corpus; and computing, with the processing system, the topic-model feature score using the first probability and the second probability; and generating, with the processing system, a prediction model for predicting whether a content word is being used figuratively, the generating being based on at least one of the plurality of topic-model feature scores and the annotations. 2. The method of claim 1, comprising: accessing, with the processing system, a text to be evaluated; identifying, with the processing system, content words in the text; for each of the identified content words in the text, predicting, with the processing system, whether the content word is being used figuratively in the text by using the prediction model; and generating, with the processing system, an evaluation score for the text based on the predictions. 3. The method of claim 1, wherein the first corpus and the second corpus are identical. 4. The method of claim 1, comprising: for each content word in the plurality of training texts, determining, with the processing system, a unigram feature score based on a frequency of the content word being used figuratively in a training corpus; wherein the generating of the prediction model is further based on the unigram feature scores. 5. The method of claim 1, comprising: for each content word that is not an auxiliary word in the plurality of training texts, determining, with the processing system, a part-of-speech feature score based on a part-of-speech classification of the content word; wherein the generating of the prediction model is further based on the part-of-speech feature scores. 6. The method of claim 1, comprising: for each content word in the plurality of training texts, determining, with the processing system, a concreteness feature score based on a predetermined concreteness rating assigned to the content word; wherein the generating of the prediction model is further based on the concreteness feature scores. 7. The method of claim 6, wherein determining a concreteness feature score includes: defining a plurality of bins, each bin being associated with a concreteness rating range; selecting one of the plurality of bins, the selected bin having an associated concreteness rating range that encompasses the predetermined concreteness rating assigned to the content word; determining the concreteness feature score based on the bin selected. 8. The method of claim 6, wherein determining a concreteness feature score includes: defining a plurality of bins, each bin being associated with a concreteness rating condition; selecting one or more of the plurality of bins, each selected bin's concreteness rating condition being satisfied by the predetermined concreteness rating assigned to the content word; determining the concreteness feature score based on the one or more bins selected. 9. The method of claim 1, comprising: for each content word that is being used as an adjective or a verb in the plurality of training texts: if the content word is an adjective, identifying, with the processing system, an associated word, the associated word being a noun modified by the adjective; if the content word is a verb, identifying, with the processing system, an associated word, the associated word being a direct object of the verb; determining, with the processing system, a first concreteness rating for the content word and a second concreteness rating for the associated word; determining, with the processing system, a concreteness difference feature score based on the first concreteness rating and the second concreteness rating; wherein the generating of the prediction model is further based on the concreteness difference feature scores. 10. The method of claim 9, wherein a concreteness difference feature is used in the generating of the prediction model if the associated second concreteness rating satisfies a predetermined threshold. 11. The method of claim 1, comprising: for each sentence in a corpus: comparing, with the processing system, the sentence to one or more predetermined patterns for identifying hypernym-hyponym relationships; if the sentence matches any of the one or more predetermined patterns, identifying, with the processing system, at least one hypernym in the sentence, at least one hyponym in the sentence, and at least one contextual content word in the sentence other than the hypernym and hyponym, the hyponym being a potential hyponym of the hypernym; and creating, in a data source, a record representing an observance of the at least one hypernym and the at least one contextual content word occurring in the same sentence; and creating, in a data source, a record representing an observance of the at least one hyponym and the at least one contextual content word occurring in the same sentence; for each content word in the plurality of training texts: identifying, with the processing system, a sentence in which the content word occurs; identifying, with the processing system, at least one other content word in the sentence; determining, with the processing system, at least one frequency value based on using the data source, each frequency value representing a frequency of the content word and one of the at least one other content word occurring in a same sentence where the content word is being used as a hyponym or a hypernym; and determining, with the processing system, a literal-context feature score based on the at least one frequency value; wherein the generating of the prediction model is further based on the literal-context feature scores. 12. The method of claim 1, comprising: for each sentence in a corpus: comparing, with the processing system, the sentence to one or more predetermined patterns for identifying similes; if the sentence matches any of the one or more predetermined patterns, identifying, with the processing system, at least one simile source word in the sentence and at least one contextual content word in the sentence other than the simile source word; and creating, in a data source, a record representing an observance of the at least one simile source word and the at least one contextual content word occurring in the same sentence; for each content word in the plurality of training texts: identifying, with the processing system, a sentence in which the content word occurs; identifying, with the processing system, at least one other content word in the sentence; determining, with the processing system, at least one frequency value based on using the data source, each frequency value representing a frequency of the content word and one of the at least one other content word occurring in a same sentence where the content word is being used as a simile source word; and determining, with the processing system, a non-literal-context feature score based on the at least one frequency value; wherein the generating of the prediction model is further based on the non-literal-context feature scores. 13. The method of claim 1, comprising: accessing, with the processing system, a third corpus, the third corpus including texts associated with a plurality of predetermined topics; for each of the plurality of predetermined topics, generating, with the processing system, a plurality of subtopic models based the texts associated with that predetermined topic; for each content word in the plurality of training texts: identifying, with the processing system, one of the plurality of predetermined topics related to the training text in which the content word occurs; for each of the plurality of subtopic models associated with the identified predetermined topic: determining, with the processing system, a third probability of the content word being used in a subtopic represented by the subtopic model; determining, with the processing system, a fourth probability of the content word being used in the texts associated with the identified predetermined topic; and computing, with the processing system, a subtopic-model score using the third probability and the fourth probability; assigning, with the processing system, an off-topic feature score to the content word based on the computed subtopic-model scores; wherein the generating of the prediction model is further based on the off-topic feature scores. 14. The method of claim 1, wherein the generating of the prediction model is based on a model training algorithm that penalizes mistaken predictions of figurative words more than mistaken predictions of non-figurative words. 15. A computer-implemented method of scoring a text based on at least predicted figurative word usage in the constructed texts, the method comprising: accessing a text to be evaluated with a processing system; identifying content words in the text with the processing system; extracting one or more features from each of the content words with the processing system; predicting, with a processing system, whether each of the content words is being used figuratively in the text, the predicting being based on a prediction model and the extracted one or more features; and generating an evaluation score with the processing system for the text based on the predictions; wherein the one or more features include at least one of a topic model feature, a unigram feature, a part-of-speech feature, a concreteness feature, a concreteness difference feature, a literal context feature, a non-literal context feature, and an off-topic feature. 16. A system for generating a model for predicting whether a content word is being used figuratively, comprising: a processing system; and a memory; wherein the processing system is configured to execute steps comprising: accessing a plurality of training texts, each training text including content words, and each content word having a corresponding annotation indicating whether the content word is being used figuratively in the associated training text; accessing a plurality of topic models generated from a first corpus; for each content word in the plurality of training texts, assigning a plurality of topic-model feature scores to the content word, each topic-model feature score being associated with one of the plurality of topic models and determined based on: determining a first probability of the content word being used in a topic represented by the topic model; determining a second probability of the content word being used in a second corpus; and computing the topic-model feature score using the first probability and the second probability; and generating a prediction model for predicting whether a content word is being used figuratively, the generating being based on at least one of the plurality of topic-model feature scores and the annotations. 17. The system of claim 16, wherein the processing system is configured to execute steps comprising: accessing a text to be evaluated; identifying content words in the text; for each of the identified content words in the text, predicting whether the content word is being used figuratively in the text based on using the prediction model; and generating an evaluation score for the text based on the predictions. 18. The system of claim 16, wherein the first corpus and the second corpus are identical. 19. The system of claim 16, wherein the processing system is configured to execute steps comprising: for each content word in the plurality of training texts, determining a unigram feature score based on a frequency of the content word being used figuratively in a training corpus; wherein the generating of the prediction model is further based on the unigram feature scores. 20. The system of claim 16, wherein the processing system is configured to execute steps comprising: for each content word that is not an auxiliary word in the plurality of training texts, determining a part-of-speech feature score based on a part-of-speech classification of the content word; wherein the generating of the prediction model is further based on the part-of-speech feature scores. 21. The system of claim 16, wherein the processing system is configured to execute steps comprising: for each content word in the plurality of training texts, determining a concreteness feature score based on a predetermined concreteness rating assigned to the content word; wherein the generating of the prediction model is further based on the concreteness feature scores. 22. The system of claim 21, wherein determining a concreteness feature score includes: defining a plurality of bins, each bin being associated with a concreteness rating range; selecting one of the plurality of bins, the selected bin having an associated concreteness rating range that encompasses the predetermined concreteness rating assigned to the content word; determining the concreteness feature score based on the bin selected. 23. The system of claim 21, wherein determining a concreteness feature score includes: defining a plurality of bins, each bin being associated with a concreteness rating condition; selecting one or more of the plurality of bins, each selected bin's concreteness rating condition being satisfied by the predetermined concreteness rating assigned to the content word; determining the concreteness feature score based on the one or more bins selected. 24. The system of claim 16, wherein the processing system is configured to execute steps comprising: for each content word that is being used as an adjective or a verb in the plurality of training texts: if the content word is an adjective, identifying an associated word, the associated word being a noun modified by the adjective; if the content word is a verb, identifying an associated word, the associated word being a direct object of the verb; determining a first concreteness rating for the content word and a second concreteness rating for the associated word; determining a concreteness difference feature score based on the first concreteness rating and the second concreteness rating; wherein the generating of the prediction model is further based on the concreteness difference feature scores. 25. The system of claim 24, wherein a concreteness difference feature is used in the generating of the prediction model if the associated second concreteness rating satisfies a predetermined threshold. 26. The system of claim 16, wherein the processing system is configured to execute steps comprising: for each sentence in a corpus: comparing the sentence to one or more predetermined patterns for identifying hypernym-hyponym relationships; if the sentence matches any of the one or more predetermined patterns, identifying at least one hypernym in the sentence, at least one hyponym in the sentence, and at least one contextual content word in the sentence other than the hypernym and hyponym, the hyponym being a potential hyponym of the hypernym; and creating, in a data source, a record representing an observance of the at least one hypernym and the at least one contextual content word occurring in the same sentence; and creating, in a data source, a record representing an observance of the at least one hyponym and the at least one contextual content word occurring in the same sentence; for each content word in the plurality of training texts: identifying a sentence in which the content word occurs; identifying at least one other content word in the sentence; determining at least one frequency value based on using the data source, each frequency value representing a frequency of the content word and one of the at least one other content word occurring in a same sentence where the content word is being used as a hyponym or a hypernym; and determining a literal-context feature score based on the at least one frequency value; wherein the generating of the prediction model is further based on the literal-context feature scores. 27. The system of claim 16, wherein the processing system is configured to execute steps comprising: for each sentence in a corpus: comparing the sentence to one or more predetermined patterns for identifying similes; if the sentence matches any of the one or more predetermined patterns, identifying at least one simile source word in the sentence and at least one contextual content word in the sentence other than the simile source word; and creating, in a data source, a record representing an observance of the at least one simile source word and the at least one contextual content word occurring in the same sentence; for each content word in the plurality of training texts: identifying a sentence in which the content word occurs; identifying at least one other content word in the sentence; determining at least one frequency value based on using the data source, each frequency value representing a frequency of the content word and one of the at least one other content word occurring in a same sentence where the content word is being used as a simile source word; and determining a non-literal-context feature score based on the at least one frequency value; wherein the generating of the prediction model is further based on the non-literal-context feature scores. 28. The system of claim 16, wherein the processing system is configured to execute steps comprising: accessing a third corpus, the third corpus including texts associated with a plurality of predetermined topics; for each of the plurality of predetermined topics, generating a plurality of subtopic models based the texts associated with that predetermined topic; for each content word in the plurality of training texts: identifying one of the plurality of predetermined topics related to the training text in which the content word occurs; for each of the plurality of subtopic models associated with the identified predetermined topic: determining a third probability of the content word being used in a subtopic represented by the subtopic model; determining a fourth probability of the content word being used in the texts associated with the identified predetermined topic; and computing a subtopic-model score using the third probability and the fourth probability; assigning an off-topic feature score to the content word based on the computed subtopic-model scores; wherein the generating of the prediction model is further based on the off-topic feature scores. 29. The system of claim 16, wherein the generating of the prediction model is based on a model training algorithm that penalizes mistaken predictions of figurative words more than mistaken predictions of non-figurative words. 30. A system for scoring a text based on at least predicted figurative word usage in the constructed texts, comprising: a processing system; and a memory; wherein the processing system is configured to execute steps comprising: accessing a text to be evaluated; identifying content words in the text; extracting one or more features from each of the content words; predicting whether each of the content words is being used figuratively in the text, the predicting being based on a prediction model and the extracted one or more features; and generating an evaluation score for the text based on the predictions; wherein the one or more features include at least one of a topic model feature, a unigram feature, a part-of-speech feature, a concreteness feature, a concreteness difference feature, a literal context feature, a non-literal context feature, and an off-topic feature. 31. A non-transitory computer-readable medium for generating a model for predicting whether a content word is being used figuratively, comprising instructions which when executed cause a processing system to carry out steps comprising: accessing a plurality of training texts, each training text including content words, and each content word having a corresponding annotation indicating whether the content word is being used figuratively in the associated training text; accessing a plurality of topic models generated from a first corpus; for each content word in the plurality of training texts, assigning a plurality of topic-model feature scores to the content word, each topic-model feature score being associated with one of the plurality of topic models and determined based on: determining a first probability of the content word being used in a topic represented by the topic model; determining a second probability of the content word being used in a second corpus; and computing the topic-model feature score using the first probability and the second probability; and generating a prediction model for predicting whether a content word is being used figuratively, the generating being based on at least one of the plurality of topic-model feature scores and the annotations. 32. A non-transitory computer-readable medium for scoring a text based on at least predicted figurative word usage in the constructed texts, comprising instructions which when executed cause a processing system to carry out steps comprising: accessing a text to be evaluated; identifying content words in the text; extracting one or more features from each of the content words; predicting whether each of the content words is being used figuratively in the text, the predicting being based on a prediction model and the extracted one or more features; and generating an evaluation score for the text based on the predictions; wherein the one or more features include at least one of a topic model feature, a unigram feature, a part-of-speech feature, a concreteness feature, a concreteness difference feature, a literal context feature, a non-literal context feature, and an off-topic feature.