Natural Language Processing (almost) from Scratch by Ronan Collobert, Jason Weston, Leon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa.
We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.
In the introduction the authors remark:
The overwhelming majority of these state-of-the-art systems address a benchmark task by applying linear statistical models to ad-hoc features. In other words, the researchers themselves discover intermediate representations by engineering task-specific features. These features are often derived from the output of preexisting systems, leading to complex runtime dependencies. This approach is effective because researchers leverage a large body of linguistic knowledge. On the other hand, there is a great temptation to optimize the performance of a system for a specific benchmark. Although such performance improvements can be very useful in practice, they teach us little about the means to progress toward the broader goals of natural language understanding and the elusive goals of Artificial Intelligence.
I am not an AI enthusiast but I agree that pre-judging linguistic behavior (based on our own) in a data set will find no more (or less) linguistic behavior than our judgment allows. Reliance on the research of others just adds more opinions to our own. Have you ever wonder on what basis we accept the judgments of others?
A very deep and annotated dive into NLP approaches (the author’s and others) with pointers to implementations, data sets and literature.
In case you are interested, the source code is available at: SENNA (Semantic/syntactic Extraction using a Neural Network Architecture)