More and more businesses rely on the processing of large amounts of natural language data in various formats (images or videos) on the web to develop more value-added services for their customers, depending on their business models.
They use natural language processing (NLP), a subfield of linguistics, data science, data science and artificial intelligence (AI) and machine learning (ML) concerned with the interactions between computers and human languages, to teach machines how to understand human languages and extract meaning.
NLP tools make it simple to handle tasks such as document classification, topic modeling, part-of-speech (POS) tagging, word vectors, and sentiment analysis. NLP is part of our lives for decades. In fact, we interact with NLP daily without even realizing it.
NLP technology is essential for scientific, economic, social, and cultural reasons, including understanding context and emotions from verbal and nonverbal communication that involves a nonlinguistic transmission of information through visual, auditory, tactile, and kinesthetic (physical) channels.
Below is a list of basic functions NLP is used to analyze language for its meaning.
- Text Classification (e.g. document categorization).
- Understanding how much time it takes to read a text.
- Finding words with the same meaning for search.
- Understanding how difficult it is to read is a text.
- Generating a summary of a text.
- Identifying the language of a text.
- Identifying entities (e.g., cities, people, locations) in a text.
- Finding similar documents.
- Text Generation.
- Translating a text.
This post will present a list of the most important Natural Language Processing (NLP) frameworks you need to know.
AllenNLP is an NLP research library, built on PyTorch, for developing state-of-the-art deep learning models on a wide variety of linguistic tasks. It makes it easy for researchers to design, evaluate, and build novel language understanding models quickly and easily. It provides a flexible data API that handles intelligent batching and padding, high-level abstractions for common operations in working with text, and a modular and extensible experiment framework that makes doing good science easy. A flexible framework for interpreting NLP models, AllenNLP is hyper-modular, lightweight, extensively tested, experiment friendly, and easy to extend. It can run reproducible experiments from a json specification with comprehensive logging.
2. Apache OpenNLP
The Apache OpenNLP library is a machine learning-based toolkit for the processing of natural language text. This open-source Java library supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, parsing, chunking, and coreference resolution. Usually, these tasks are required to build more advanced text processing services. OpenNLP also includes maximum entropy and perceptron based machine learning. The goal is to create a mature toolkit for the abovementioned tasks. An additional goal is providing a large number of pre-built models for a variety of languages, as well as the annotated text resources that those models are derived from.
3. Apache Tika
The Apache Tika is a content analysis toolkit used to parse the documents in PDF, Open Document, Excel, and many other well-known binary and text formats using a simple uniform API. It can detect and extract metadata and text from over a thousand different file types. All file types can be parsed via a single interface, making Tika useful for indexing the search engine, analyzing content, translating, and much more. There are several wrappers available for using Tika in another programming language, like Julia or Python.
BERT (Bidirectional Encoder Representations from Transformers) is a new method of pre-training language representations that achieves state-of-the-art results on a wide range of tasks related to NLP. BERT is conceptually simple and empirically powerful and can obtain new state-of-the-art results on eleven natural language processing tasks such as question answering and language inference, without substantial task-specific architecture modifications. It is designed to pre-train deep bidirectional representations from the unlabeled text by jointly conditioning on both left and right context in all layers.
5. Bling Fire
ERNIE (Enhanced Language Representation with Informative Entities) is a continual pre-training framework for language understanding in which multi-task learning incrementally builds up and learns pre-training tasks. It can take full advantage of lexical, syntactic, and knowledge information simultaneously. Within this framework, various custom tasks can be introduced incrementally at any time. For example, the tasks, including the prediction of named entities, recognition of discourse relationships, prediction of sentence order, are leveraged to enable the models to learn representations of languages. Based on the alignments between text and KGs, ERNIE integrates entity representations in the knowledge module into the semantic module’s underlying layers.
FastText is a library for efficient learning of word representations and sentence classification. It is on par with state-of-the-art deep learning classifiers in terms of accuracy. It can train on more than one billion words in less than ten minutes using a standard multicore CPU and classify nearly 500K sentences among 312K classes in less than a minute. Created by Facebook Opensource, FastText is available for all.
FLAIR is a simple, unified, and easy-to-use framework for state-of-the-art NLP, developed by Zalando Research. This NLP framework is designed to facilitate the training and distribution of sequence labeling, text classification, and language models. This powerful NLP library allows you to apply NLP models to text, such as named entity recognition (NER), part-of-speech tagging (PoS), sense disambiguation, and classification. It has simple interfaces that allow you to use and combine different word and document embeddings, including our proposed Flair embeddings87, BERT embeddings, and ELMo embeddings. This Pytorch NLP framework builds directly on Pytorch, making it easy to train your own models and experiment with new approaches using Flair embeddings and classes.
Gensim is a Python library for topic modeling, document indexing, and similarity retrieval with large corpora. It aims at processing raw, unstructured digital texts. The algorithms in gensim, such as Latent Semantic Analysis, Latent Dirichlet Allocation, or Random Projections, discover the semantic structure of documents, by examining word statistical co-occurrence patterns within a corpus of training documents. These algorithms are unsupervised, which means no human input is necessary – you only need a corpus of plain text documents. Once these statistical patterns are found, any plain text documents can be succinctly expressed in the new, semantic representation, and queried for topical similarity against other documents.
10. Microsoft Icecaps
Microsoft Icecaps is an open-source toolkit for building conversational neural systems. Within a flexible paradigm, Icecaps provides a range of tools from recent conversation modeling and general NLP literature that enables complex multi-task learning setups. This conversation modeling toolkit was developed on top of TensorFlow functionality to bring together these desirable characteristics. Users can build agents with induced personalities, generate diverse responses, ground those responses in external knowledge, and avoid particular phrases.
jiant, an open-source toolkit for conducting multi-task and transfer learning experiments on English NLU tasks. jiant enables modular and configuration-driven experimentation with state-of-the-art models and implements a broad set of tasks for probing, transfer learning, and multi-task training experiments. jiant implements over 50 NLU tasks, including all GLUE and SuperGLUE benchmark tasks. This software can be used for evaluating and analyzing natural language understanding systems. jiant allows users to run a variety of experiments using state-of-the-art models via an easy to use configuration-driven interface.
NeuralCoref is a pipeline extension for spaCy 2.0, which uses a neural network to annotate and solve coreference clusters. NeuralCoref is ready for production, integrated into the NLP pipeline of spaCy, and can be easily extended to new training datasets. This coreference resolution module is based on the superfast spaCy parser and uses the model of neural net scoring as described by Kevin Clark and Christopher D. Manning in Deep Reinforcement Learning for Mention-Ranking Coreference Models.
13. NLP Architect
NLP Architect is an open-source Python library to explore topologies and techniques for natural language processing and understanding of natural languages. It is meant to be a platform for future collaboration and research. Key features include Core NLP models that are useful in many NLP applications, Novel NLU models featuring novel topologies and techniques, optimized NLP / NLU models featuring various optimization algorithms on neural NLP / NLU models, model-oriented design, and essential tools for working with NLP models – pre-processing text/string, IO, data manipulation, metrics, embedding.
14. NLTK (Natural Language Toolkit)
NLTK is a leading platform for creating Python programs that work with data in the human language. It provides easy-to-use interfaces for more than 50 corpora and lexical resources, along with a suite of text processing libraries for tokenization, classification, stemming, tagging, parsing, and semance reasoning, wrappers for NLP libraries of industrial strength. NLTK operates on all Python-supported platforms, including Windows, OS X, Linux, and Unix.
Pattern is a web mining module for the Python programming language. It bundles tools for data retrieval (Google, Twitter, Wikipedia API, web spider, HTML DOM parser), text analysis (rule-based shallow parser, WordNet interface, syntactical + semantical n-gram search algorithm, tf-idf + cosine similarity + LSA metrics) and data visualization (graph networks).
Rant is an all-purpose, procedural text engine that includes a variety of features to handle everything from the most basic tasks of string generation to advanced dialog generation, code templating, and automatic formatting. The features include recursive, weighted branching with multiple selection modes, queryable dictionaries, automatic capitalization, rhyming, indefinite English articles, and verbalization of multi-lingual numbers, printing to multiple separate outputs, modifier probability for pattern elements, loops, conditional statements, and subroutines, fully-functional object model, etc.
SpaCy is a free, open-source, advanced natural language processing ( NLP) library in Python. It is specifically designed for use in production and helps you build applications that process and “understand” large volumes of text. It can be used to construct systems for information extraction or natural language understanding or to pre-process text for deep learning. The features include non-destructive tokenization, named entity recognition, support for 26+ languages, 13 statistical models for eight languages, pre-trained word vectors, easy deep learning integration, part-of-speech tagging, labeled dependency parsing, syntax-driven sentence segmentation, built-in visualizers for syntax and NER, convenient string-to-hash mapping, etc.
18. Stanford CoreNLP
The Stanford CoreNLP Natural Language Processing Toolkit is an extensible pipeline that provides core natural language analysis. It can give the basic forms of words, their parts of speech, whether they are names of companies, individuals, etc., normalize dates, times and numerical quantities, mark the sentence structure in terms of syntactic dependencies and phrases, indicate which noun phrases refer to the same entities, indicate feelings, extract particular or open-class relationships between entities, obtain quotes. This integrated NLP toolkit with a broad range of grammatical analysis tools is a fast, robust annotator for arbitrary texts, used in production. It has a modern, regularly updated package, with the overall highest quality text analytics.
Texar-PyTorch is a toolkit that aims to support a wide range of machine learning tasks, in particular the processing of natural languages and the generation of text. Texar offers an easy-to-use library of ML modules and functionalities to compose whatever models and algorithms. The tool is designed for fast prototyping and experimentation, for both researchers and practitioners. Texar-PyTorch integrates many of TensorFlow’s best features into PyTorch, delivering superior to native PyTorch modules that are highly usable and customizable. Texar-PyTorch combines many useful functions and features TensorFlow and PyTorch in. It is highly customizable, providing a different level of abstraction API to facilitate rich novice and experienced users.
20. TextBlob: Simplified Text Processing
TextBlob is a Python library for processing textual data. It provides a simple API for diving into common NLP tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, tokenization (splitting text into words and sentences), detection powered by Google Translate, word and phrase frequencies, parsing, n-grams, word inflection (pluralization and singularization) and lemmatization, WordNet integration, etc.
Thinc is a lightweight deep learning library powering spaCy. It offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow, and MXNet. It features a battle-tested linear model designed for large sparse learning problems and a flexible neural network model under development for spaCy v2.0. You can use Thinc as an interface layer, standalone toolkit, or a flexible way to develop new models. It is designed to be easy to install, efficient for CPU usage, and optimized for NLP and deep learning with text – in particular, hierarchically structured input and variable-length sequences. Thinc is a practical toolkit for implementing models that follow the “Embed, encode, attend, predict” architecture.
Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) is a novel architecture that aims to solve sequence-to-sequence tasks while handling long-range dependencies with ease. It provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet) for NLU and NLG with over 32+ pre-trained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch. It is as easy to use as pytorch-transformers and as powerful and concise as Keras. Researchers can share trained models instead of always retraining, reducing compute time, and production costs.