I don’t really know anything about this. See instead, perhaps design grammars, semantics, iterated function systems and my obsolete research proposal in this area, grammatical inference, information retreival.
What is NLP
Sebastian Ruder, Recent history of NLP a.k.a how natural language processing turned into a deep learning thing too
Peter Norvig on Chomsky and statistical versus explanatory models of natural language syntax. Full of sick burns.
BlingFire Fire Tokenizer is a tokenizer designed for fast-speed and quality tokenization of Natural Language text. It mostly follows the tokenization logic of NLTK, except hyphenated words are split and a few errors are fixed.
This looks like it is also good for non-NLP tokenization tasks.
spaCy excels at large-scale information extraction tasks. It’s written from the ground up in carefully memory-managed Cython. Independent research has confirmed that spaCy is the fastest in the world. If your application needs to process entire web dumps, spaCy is the library you want to be using. […]
spaCy is the best way to prepare text for deep learning. It interoperates seamlessly with TensorFlow, PyTorch, scikit-learn, Gensim and the rest of Python’s awesome AI ecosystem. With spaCy, you can easily construct linguistically sophisticated statistical models for a variety of NLP problems.
Like other deep learning frameworks, there is some basic NLP support in pytorch; see pytorch.text.
NLTK is a classic python teaching library for rolling your own language processing.
The Natural Language Processing for JVM languages (NLP4J) project provides:
NLP tools readily available for research in various disciplines. Frameworks for fast development of efficient and robust NLP components. API for manipulating computational structures in NLP (e.g., dependency graph). The project is initiated and currently led by the Emory NLP research group with many helps [sic] from the community.
MALLET is another big java NLP workbenchey thing
IMS Open Corpus Workbench (CWB)…
is a collection of open-source tools for managing and querying large text corpora (ranging from 10 million to 2 billion words) with linguistic annotations.
I’m uncertain how actively maintained this is.
The Hidden Markov Model Toolkit (HTK) is a portable toolkit for building and manipulating hidden Markov models. HTK is primarily used for speech recognition research although it has been used for numerous other applications including research into speech synthesis, character recognition and DNA sequencing. HTK is in use at hundreds of sites worldwide.
There are many more, but I am stopping with the links having found the bits and pieces I need for my purposes.
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