A device may receive a first command, included in a set of commands, to set a configuration parameter associated with performing feature extraction. The device may receive a second command, included in the set of commands, to set a corresponding value for the configuration parameter. The configuration parameter and the corresponding value may correspond to a particular feature metric that is to be extracted. The device may configure, based on the configuration parameter and the corresponding value, feature extraction for a corpus of documents. The device may perform, based on configuring feature extraction for the corpus, feature extraction on the corpus to determine the particular feature metric. The device may generate a feature vector based on performing the feature extraction. The feature vector may include the particular feature metric. The feature vector may include a feature identifier identifying the particular feature metric. The device may provide the feature vector.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A device, comprising: one or more processors to: receive a first command, included in a set of commands, to set a configuration parameter associated with performing feature extraction; receive a second command, included in the set of commands, to set a corresponding value for the configuration parameter, the configuration parameter and the corresponding value corresponding to a particular feature metric that is to be extracted; configure, based on the configuration parameter and the corresponding value, feature extraction for a corpus of documents; perform, based on configuring feature extraction for the corpus, feature extraction on the corpus to determine the particular feature metric; generate a feature vector based on performing the feature extraction, the feature vector including the particular feature metric, the feature vector including a feature identifier identifying the particular feature metric; and provide the feature vector. 2. The device of claim 1, where the corpus is a first corpus, the feature extraction is a first feature extraction, and the feature vector is a first feature vector; and where one or more processors are further to: perform a second feature extraction on a second corpus of documents based on one or more commands of the set of commands; generate a second feature vector based on performing the second feature extraction; provide the first feature vector to a first recipient device operating a first machine learning application; and provide the second feature vector to a second recipient device operating a second machine learning application, the first machine learning application being different from the second machine learning application. 3. The device of claim 1, where the configuration parameter relates to at least one of: a linguistic type of feature, a semantic type of feature, or a statistical type of feature. 4. The device of claim 1, where the one or more processors are further to: determine a set of possible corresponding values for the configuration parameter based on receiving the configuration parameter, the set of possible corresponding values including the corresponding value; provide, via a user interface, information identifying one or more commands, of the set of commands, associated with setting the set of possible corresponding values, the one or more commands including the second command; and where the one or more processors, when receiving the second command, are to: receive a selection of the second command via the user interface based on providing the information identifying the one or more commands. 5. The device of claim 1, where the one or more processors when generating the feature vector are to: generate a header entry including the feature identifier; and generate a set of rows associated with the header entry, the set of rows including a set of values for the particular feature metric, each value, of the set of values, corresponding to a syntactic unit of a set of syntactic units of the corpus. 6. The device of claim 1, where the one or more processors, when providing the feature vector, are to: provide the feature vector to a recipient device to cause the recipient device to perform a machine learning or natural language processing technique on the corpus based on the particular feature metric, the recipient device being caused to identify the particular feature metric based on the feature identifier. 7. The device of claim 1, where the one or more processors, when providing the feature vector, are to: provide the feature vector to permit text analytics to be performed on the corpus for at least one of: a medical text analysis context, a legal text analysis context, a software text analysis context, a sentiment mining context, a social media text analysis context, a customer intelligence context, a web page ranking context, or a fraud analytics context. 8. The device of claim 1, where the feature identifier includes the first command, the second command, and a logical operator identifying a relationship between the first command and the second command. 9. A non-transitory computer-readable medium storing instructions, the instructions comprising: one or more instructions that, when executed by one or more processors, cause the one or more processors to: provide a user interface, the user interface including one or more user interface elements identifying a set of commands of a feature extraction language; receive, via the user interface, a selection of one or more commands of the feature extraction language, the one or more commands identifying a set of feature metrics; perform, based on receiving the selection of the one or more commands of the feature extraction language, feature extraction on a document to determine a set of values for the set of feature metrics; and provide a feature vector to cause a machine learning process to be performed on the document based on the set of values for the set of feature metrics, the feature vector including the set of values for the set of feature metrics, the feature vector including information identifying the set of feature metrics. 10. The computer-readable medium of claim 9, where the set of feature metrics includes a particular feature metric associated with at least one of: a syntactic feature, an orthographic feature, a context based feature, a dependency feature, a lexical feature, a latent feature, an ontological feature, a distribution feature, a correlation feature, an information specificity feature, a latent semantic association feature, a central themes and topics feature, or a clustering feature. 11. The computer-readable medium of claim 9, where the one or more user interface elements include at least one of: a drop-down list, a drop-down menu, or a pop-up window. 12. The computer-readable medium of claim 9, where the one or more instructions, when executed by the one or more processors, are further to: parse the one or more commands to determine a set of expressions for feature extraction; and where the one or more instructions, that cause the one or more processors to perform feature extraction, cause the one or more processors to: determine the set of values for the set of feature metrics, the set of values being results of evaluating the set of expressions for one or more syntactic units of the document. 13. The computer-readable medium of claim 9, where the one or more instructions, when executed by the one or more processors, cause the one or more processors to: identify another document, perform feature extraction on the other document based on another one or more commands; generate another feature vector based on performing the other feature extraction; and provide the other feature vector to cause the machine learning process to be performed on the other document. 14. The computer-readable medium of claim 9, where the machine learning process is a text analytics process. 15. The computer-readable medium of claim 9, where the information identifying the set of feature metrics includes information identifying an expression associated with the set of feature metrics. 16. A method, comprising: determining, by a device, a first one or more feature extraction parameters, of a set of feature extraction parameters, and a first one or more corresponding values for the first one or more feature extraction parameters; performing, by the device, a first feature extraction on a first document to generate a first one or more feature metrics based on the first one or more feature extraction parameters and the first one or more corresponding values; providing, by the device and to a first recipient device, a first feature vector including information identifying the first one or more feature metrics; determining, by the device, a second one or more feature extraction parameters, of the set of feature extraction parameters, and a second one or more corresponding values for the second one or more feature extraction parameters; performing, by the device, a second feature extraction on a second document to generate a second one or more feature metrics based on the second one or more feature extraction parameters and the second one or more corresponding values; and providing, by the device and to a second recipient device, a second feature vector including information identifying the second one or more feature metrics. 17. The method of claim 16, further comprising: providing a user interface including information identifying the set of feature extraction parameters and a set of potential values for the set of feature extraction parameters; receiving, via the user interface, a selection of the first one or more feature extraction parameters and the first one or more corresponding values based on providing the user interface; and where determining the first one or more feature extraction parameters and the first one or more corresponding values comprises: determining the first one or more feature extraction parameters and the first one or more corresponding values based on receiving the selection. 18. The method of claim 16, further comprising: determining one or more expressions based on the first one or more feature extraction parameters and the first one or more corresponding values; and where performing the first feature extraction comprises: evaluating the one or more expressions to determine the first one or more feature metrics. 19. The method of claim 18, where providing the first feature vector comprises: providing information identifying the one or more expressions. 20. The method of claim 16, further comprising: providing the first feature vector to a third recipient device, the third recipient device being different from the first recipient device, the third recipient device being different from the second recipient device.