1. A method for trait prediction comprising: receiving a text sequence comprising words, each of the words including at least one character, some of the words comprising more than one character; generating a character embeddings for each of the characters of the text sequence, each character embedding being a multidimensional representation of a respective one of the characters; generating word representations for words of the text sequence with a trained character sequence model, based on the character embeddings, each word embedding being a multidimensional representation of a respective one of the words of the text sequence, the character sequence model comprising a first recurrent neural network that receives the character embeddings for a word of the text sequence and outputs the word representation for the word; generating a sequence representation for the text sequence with a trained word sequence model, based on the word representations; generating at least one trait prediction with a trained trait model, based on the sequence representation; and outputting the trait prediction or information based on the trait prediction, wherein at least one of the generating character embeddings, generating word representations, generating the sequence representation, and generating at least one trait prediction is performed with a processor.
1. A method for trait prediction comprising: receiving a text sequence comprising words, each of the words including at least one character, some of the words comprising more than one character; generating a character embeddings for each of the characters of the text sequence, each character embedding being a multidimensional representation of a respective one of the characters; generating word representations for words of the text sequence with a trained character sequence model, based on the character embeddings, each word embedding being a multidimensional representation of a respective one of the words of the text sequence, the character sequence model comprising a first recurrent neural network that receives the character embeddings for a word of the text sequence and outputs the word representation for the word; generating a sequence representation for the text sequence with a trained word sequence model, based on the word representations; generating at least one trait prediction with a trained trait model, based on the sequence representation; and outputting the trait prediction or information based on the trait prediction, wherein at least one of the generating character embeddings, generating word representations, generating the sequence representation, and generating at least one trait prediction is performed with a processor. 3. The method of claim 1 , wherein the first recurrent neural network comprises a bi-directional RNN which generates a hidden state for each character of the word in forward and backward directions.