Chainer
Chainer, developed by the Japanese company Preferred Networks founded in 2014, is a powerful, flexible, and intuitive Python-based framework for neural networks that adopts a “define-by-run” scheme [1]. It stores the history of computation instead of programming logic. Chainer supports CUDA computation and multi-GPU. The framework released under the MIT License
and is already applied for sentiment analysis, machine translation,
speech recognition, question answering, and so on using different types
of neural networks like convolutional networks, recurrent networks, and
sequence to sequence models [2].
Deeplearning4j
Deeplearning4j
is a deep learning Java programming library, but it also has a Python
API, Keras that will be described below. Distributed CPUs and GPUs,
parallel training via iterative reduce, and micro-service architecture
adaptation are its main features [3]. Vector space modeling
enables the tool to solve text-mining problems. Parts of speech (PoS)
tagging, dependency parsing, and word2vec for creating word embedding
are discussed in the documentation.
Deepnl
Deepnl is another neural network Python library especially created for natural language processing by Giuseppe Attardi.
It provides tools for part-of-speech tagging, named entity recognition,
semantic role labeling (using convolutional neural networks [4]), and word embedding creation [5].
Dynet
Dynet is a tool developed by Carnegie Mellon University and many others. It supports C++ and Python languages, runs on either CPU or GPU [6]. Dynet is based on the dynamic declaration of network structure [7].
This tool was used for creating outstanding systems for NLP problems
including syntactic parsing, machine translation, morphological
inflection, and many others.
Keras
Keras
is a high-level neural-network based Python API that runs on CPU or
GPU. It supports convolutional and recurrent networks and may run on top
of TensorFlow, CNTK, or Theano. The main focus is to enable users fast experimentation [8]. There are many examples of Keras usage in the comparative table: classification, text generation and summarization, tagging, parsing, machine translation, speech recognition, and others.
Nlpnet
Erick Rocha Fonseca’s nlpnet
is also a Python library for NLP tasks based on neural networks.
Convolutional networks enable users to perform part-of-speech tagging,
semantic role labeling, and dependency parsing [9]. Most of the architecture is language independent [10].
OpenNMT
OpenNMT is a Python machine translation tool that works under the MIT license and relies on the PyTorch
library. The system demonstrates efficiency and state-of-the-art
translation accuracy and is used by many translation providers [11]. It also incorporates text summarization, speech recognition, and image-to-text conversion blocks [12].
PyTorch
PyTorch
is a fast and flexible neural network framework with an imperative
paradigm. It builds neural networks on a tape-based autograd system and
provides tensor computation with strong GPU acceleration [13].
Recurrent neural networks are mostly used in PyTorch for machine
translation, classification, text generation, tagging, and other NLP
tasks.
SpaCy
Developers called spaCy
the fastest system in the world. They also affirm that their tool is
the best way to prepare text for deep learning. Spacy works excellent
with well-known Python libraries like gensim, Keras, TensorFlow, and
scikit-learn. Matthew Honnibal, the author of the library, says that spaCy’s mission is to make cutting-edge NLP practical and commonly available [14].
Text classification, named entity recognition, part of speech tagging,
dependency parsing, and other examples are presented in the comparative
table.
Stanford’s CoreNLP
Stanford’s CoreNLP
is a flexible, fast, and modern grammatical analysis tool that provides
APIs for most common programming languages including Python. It also
has an ability to run as a simple web service. As mentioned on the official website,
the framework has a part-of-speech (POS) tagger, named entity
recognizer (NER), parser, coreference resolution system, sentiment
analysis, bootstrapped pattern learning, and open information extraction
tools [15].
Tensorflow
The Google Brain Team developed TensorFlow
and released it in 2015 for research purposes. Now many companies like
Airbus, Intel, IBM, Twitter and others use TensorFlow at production
scale. The system architecture is flexible, so it is possible to perform
computations on CPUs or GPUs. The main concept is flow graphs usage.
Nodes of the graph reflect mathematical operations, while the edges
represent multidimensional data arrays (tensors) communicated between
them [16]. One of the most known of TensorFlow’s NLP application is Google Translate. Other applications are text classification and summarization, speech recognition, tagging, and so on.
TFLearn
As
Tensorflow is a low-level API, many high-level APIs were created to run
on top of it to make the user experience faster and more
understandable. TFLearn
is one of these tools that runs on CPU and GPU. It has a special graph
visualization tool with details about weights, gradients, activations,
and so on [17].
The library is already used for sentiment analysis, text generation,
and named entity recognition. It lets users work with convolutional
neural networks and recurrent neural networks (LSTM).
Theano
Theano is a numerical computation Python library that enables users to create their own machine learning models [18].
Many frameworks like Keras are built on top of Theano. There are tools
for machine translation, speech recognition, word embedding, and text
classification. Look at Theano’s applications in the table.
Summary
In
this paper, we described neural network supporting Python tools for
natural language processing. These tools are Chainer, Deeplearning4j,
Deepnl, Dynet, Keras, Nlpnet, OpenNMT, PyTorch, SpaCy, Stanford’s
CoreNLP, TensorFlow, TFLearn, and Theano. A table lets readers easily
compare the frameworks discussed above.
Posts
