Search engine, personal assistant, machine translation, machine reading, intelligent question answering, chat robot, knowledge map, semantic search, machine reading, public opinion monitoring analysis, recommendation system, text keyword extraction, automatic text summarization and so on all need natural language processing technology.
Anyway, NLP is particularly hot now. Learning suggestions, you need to learn ML (machine learning), DL (deep learning) and RL (reinforcement learning) first. You can study one or two excellent open source projects. These open source projects can be found on github. There are many people on GitHub, and there are also many good open source projects. For example:
Word representation learning algorithm considering words
GitHub - Leonard-Xu/CWE
Network representation learning
Text-enhanced network representation learning algorithm
Github-Albert Yang 33/Tadw: IJCAI 2015 The code of the paper "Learning the Network Representation of Rich Text Information"
Cross-language word representation learning algorithm
Learning cross-language word embedding through matrix decomposition
Topic-enhanced word representation learning algorithm
Github-largeymfs/topic _ word _ embedding: demonstration code of topic word embedding.
Interpretable word representation learning algorithm
GitHub-SkTim/OIWE: On-line Explanatory Word Embedding
China's natural language processing tool: Harbin Institute of Technology LTP:/
It is suggested to study some latest classic papers, such as ACL, EMNLP, COLING, CCL, etc.
I recommend several books about learning natural language processing, first of all, Statistical Methods by Li Hang, as well as machine learning and Python natural language processing.