Text analytics and natural language processing have made tremendous advances in the last few years. Unfortunately, there is a lot more to understanding natural language that TA/NLP.
I was reading a paper today about NLP pipelines for question answering that used machine learning to find what tools are good at what tasks and to configure a pipeline by selecting the best tool for a given task from each of the types of components in the pipeline. The paper has a long list of various components, so I checked a few out. Most of those of interest were available on the web so that they could be easily composed into pipelines without a lot of software setup. Looking at these I quickly tired in disappointment. Here are some of the reasons.
I am not surprised by these results. NLU is hard. But they are not particularly strong results either. I’m surprised that people find such results useful (if they do).