Nlp-architect

Improvements
- Installation process refactored and supports `pip install nlp-architect` (much more simple)
- Moved `server` and `solutions` into main package
- Many Cross-doc coreference model fixes and additions
- Updates and fixed lots of documentation files

Demos
- Improved NLP Architect demos with a new design
- Renamed `nlp_architect demo` to `nlp_architect server`

New Solution
- **Topics and Trend Analysis** - extract topics and compare two temporal versions a corpus, highlighting hot and cold trends.
New models
- **Sparse GNMT** - A `Tensorflow` implementation of the GNMT model with sparsity and quantization operations integrated.
- **Semantic Relation Identification** - Extract semantic relation types of two words or phrases using external resources.
- **Sieve-based Cross Document Coreference** - A seive-based model for finding similar entities or events across different documents from the same domain.

Improvements
- **Reading comprehension** - added inference mode.
- **Sequential models** - updated NER, IE, Chunker models to use `tf.keras` and added CNN-character based feature extractors and improved accuracy of all models.
- **CRF Layer** - added native `Tensorflow` based CRF layer.
- **Word Sense Disambiguation** - model updated to use `tf.keras`.
- **Demo UI** - updated demo UI using AngularJS.
- **Installation** - improved installation process and added support for CPU/MKL/GPU backends for `Tensorflow`.
- **NLP Architect cmd** - added `nlp_architect` - a simple command initiator to handle maintenance tasks, see `nlp_architect -h` for the list of commands.
- Lots of bug fixes and refactoring.

New Solution
- **Term Set Expansion** - the task of expanding a given partial set of terms into a more complete set of terms that belong to the same semantic class. This solution demonstrates the usage of NLP Architect models (Word Chunker and NP2Vec) used in an application solution.
New models
- **Unsupervised Crosslingual Embeddings** model using a GAN to learn a mapping between languages - implemented in `Tensorflow`
- **Language Model (LM)** using **Temporal Convolution Network (TCN)** - implemented in `Tensorflow`
- **Supervised Sentiment Classification** - implemented in `Keras`
Model improvements
- **Reading comprehension** - refactored to use `Tensorflow`
- **End-to-end Memory Network for Goal Oriented Dialogue** - refactored to use `Tensorflow`
- **Word Chunker** - refactored to use `tf.keras` and use state-of-art model
- **NP semantic segmentation** - refactored to use `tf.keras`
- Updated `CONLL2000`, `Amazon_Review`, `PTB`, `Fasttext`, `Wikitext-103` and `Wikipedia-dump` dataset loaders.
New features
- REST server refactored to use `hug`, new streamlined the UI and improved documentation. See updated [documentation](http://nlp_architect.nervanasys.com/service.html) for further details.
- Noun Phrase annotator plug-in for `spaCy` pipeline
- [Publications](http://nlp_architect.nervanasys.com/publications.html) page with relevant material demonstrating the usage of NLP Architect
- [Tutorials](http://nlp_architect.nervanasys.com/tutorials.html) page with a collection of `Jupyter` notebook tutorials using NLP Architect models

The current version of NLP Architect includes these features that we found interesting from both research perspectives and practical applications:

- NLP core models that allow robust extraction of linguistic features for NLP workflow: for example, dependency parser (BIST) and NP chunker
- NLU modules that provide best in class performance: for example, intent extraction (IE), name entity recognition (NER)
- Modules that address semantic understanding: for example, colocations, most common word sense, NP embedding representation (e.g. NP2V)
- Components instrumental for conversational AI: for example, ChatBot applications, including dialog system, sequence chunking and IE
- End-to-end DL applications using new topologies: for example, Q&A, machine reading comprehension