Nlp Architect Versions Save

A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks

5 years ago

New Solution

Topics and Trend Analysis - extract topics and compare two temporal versions a corpus, highlighting hot and cold trends.

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.

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.

5 years ago

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.

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

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.

REST server refactored to use hug, new streamlined the UI and improved documentation. See updated documentation for further details.
Noun Phrase annotator plug-in for spaCy pipeline
Publications page with relevant material demonstrating the usage of NLP Architect
Tutorials page with a collection of Jupyter notebook tutorials using NLP Architect models

6 years ago

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