I think you want to understand comp linguistics viewpoint: parsers, PoS taggin, dependency analysis, syntax trees;
and the machine learning perspective: embeddings in, say, 100-200 dimensional space (word2vec, glove) and topic modelling/LDA, and latent semantic analysis from the 90's. Then you can read about inputting embedding datasets into LSTM, GRU, content addressable memory/attention mechanisms etc that are being furiously introduced (you can scan the ICLR submissions and http://aclweb.org/anthology/.
and the machine learning perspective: embeddings in, say, 100-200 dimensional space (word2vec, glove) and topic modelling/LDA, and latent semantic analysis from the 90's. Then you can read about inputting embedding datasets into LSTM, GRU, content addressable memory/attention mechanisms etc that are being furiously introduced (you can scan the ICLR submissions and http://aclweb.org/anthology/.
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The Jurafsky/Martin draft 3rd ed is a good starting point, they've got about 2/3 of chapters drafted: https://web.stanford.edu/~jurafsky/slp3/ as well as the Stanford, Oxford, etc courses on NLP and comp linguistics, and Klein's https://people.eecs.berkeley.edu/~klein/cs288/fa14/ , Collins: http://www.cs.columbia.edu/~cs4705/ and other courses at MIT, CMU, UIUC etc
Also, try out the various standard benchmark datasets and tasks: https://arxiv.org/abs/1702.01923
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Last time i checked, this SoA page wasn't up to date and not very well summarized but will give you lots of project ideas: http://www.aclweb.org/aclwiki/index.php?title=State_of_the_a...