Natural language processing is the most promising field in data science and artificial intelligence that concerns in teaching computers how to elicit meaningful information from text.
“Artificial intelligence will reach human levels by around 2029. Follow that out further to, say, 2045, we will have multiplied the intelligence, the human biological machine intelligence of our civilization a billion-fold.” – Ray Kurzweil, American inventor and futurist
Through this blog discussion, you will learn the basics of Natural Language Processing(NLP), its usage and applications. After the brief introduction of NLP, you will see the next section emphasizing top 10 natural language processing (NLP) libraries, highlighting notion and features. Let’s start!!!!!
Natural language processing is best explained as “AI for speech and text.”
In simple words, NLP is the part of computer science and artificial intelligence that could aid in communicating between the computer (machine) and human by natural language. It lets a computer or machine to be read and understood by replicating the human natural language.
Being a core branch of data science, Natural Language Processing(NLP) is the method that deals in probing, understanding, and extorting out information from the text form of data. Therefore, by implementing NLP approach, we can adapt and decipher heavy chunks of text data and can operate several tasks addressed a broad range of applications such as automatic summarization, machine translation, etc. (Similar blog: 7 NLP Techniques for Extracting Information)
Below are some applications of Natural Language Processing;
ML chatbots or conversational agents
Speech and image recognition
“Much has been written about AI’s potential to reflect both the best and the worst of humanity. For example, we have seen AI providing conversation and comfort to the lonely; we have also seen AI engaging in racial discrimination. Yet the biggest harm that AI is likely to do to individuals in the short term is job displacement, as the amount of work we can automate with AI is vastly bigger than before. As leaders, it is incumbent on all of us to make sure we are building a world in which every individual has an opportunity to thrive.” – Andrew Ng
NLP is evolving day by day due to the generation of an extensive amount of textual data and also more unstructured data. However, some fundamental tasks of NLP are discussed below;
Tokenization: It is the process of splitting down the text into scantier, meaningful elements called tokens.
Word Stemming and Lemmatization: Stemming and Lemmatization are the approaches of deriving the word form to its base root. (Visit the blog: What is Stemming and Lemmatization in NLP?)
Part of speech (POS) Tagging: POS fundamentally is tagging in order to indicate a label to each and every word with a respective grammatical element.
Chunking: It is somewhat selecting up tiny pieces of information and assorting them into a higher one.
Stop Word Removal: Stop words are the simplistic word and are the parts of a grammatical structure of the sentence and this stop word removal guide in sentiment analysis.
Name entity recognition: It is the method of identifying entities such as name, location, etc that are often seen in unstructured data.
The primary goal of NLP libraries is to simplify text preprocessing, a good NLP library should able enough to properly transform free text sentences into structured features. Also, an NLP library must have an easy-to-obtain API that could be implemented to the newest and vastest algorithms aptly. (Related blog: Top 10 NLP Trends in 2021)
Let’s have a precise discussion over the topmost libraries in natural language processing;
NLTK is treated as the leading platform for developing Python programs in order to operate with human language data. NLTK has been termed as “an incredible tool for educating and serving in computational linguistics through Python,” and “an astonishing library to engage with natural language.”
It renders an easy-to-implement interface across more than 50 corpora and linguistics support like WordNet along with a sequence of text processing libraries for classification, tokenization, stemming, etc.
It gives a reasonable introduction and foundation to programming for language processing.
(More to know about NLTK in detail, read the blog: What is Natural Language Toolkit (NLTK) in NLP?)
Counted as the most advanced Natural Language Processing library in Python and Cython, spaCy is a stable, agile, and cost-effective open-source library which is written in Cython. spaCy comes along with imperative features, such as;
It appears with pre-trained statistical models and word vectors.
It promotes tokenization for numerous languages.
It emphasises state-of-the-art speed and accuracy, support convolutional neural network(CNN) models for tagging, translating.
It is deployed for named entity recognition and smooth deep learning integration.
(More to know about NLTK in detail, read the blog: What is spaCy in Natural Language Processing (NLP)?)
Developed and open-sourced by the Zalando Research team, Flair is the straightforward NLP library, its framework is designed over PyTorch. It is easy-to-deploy while equipping compelling features like stacking embeddings. It also gives access to its design “Flair embeddings.” In terms with Zalando Research team, they have delivered various pre-trained models for the following NLP assignments;
Name-Entity Recognition (NER): In order to recognize whether a word depicts a person, location, or names throughout the text.
Parts-of-Speech Tagging (PoS): Tagging of the words across a provided text in terms of “ which part of speech they belong to”.
Text Classification: Segragating text on the basis of some defined standards(labels), and
Training Custom Models: For developing your own customized models/systems.
Top 10 Natural Language Processing libraries with Python
Gensim is an open-source python library that is used for unsupervised topic modelling, document indexing and similarity retrieval with large corpora. For natural language processing methods that practice advanced statistical machine learning.
It is broadly adopted while operating with word embeddings such as Word2Vec and Doc2Vec, and
It is considered suitable for performing topic modelling relevant tasks.
Gensim is a highly recommended choice for topic modelling and document similarity comparison.
It proffers scalable statistical semantics and semantic structure analysis.
It supports huge-level processing speed and embraces the potential to manage extensive chunks of data.
The salient highlights of this library include (a) all algorithms are memory-independent in the context of the corpus size, instinctive interfaces, effective multicore implementations of conventional algorithms, shared computing, etc.
Developed on the jostle of NLTK, TextBlob is considered as an identical extension that interprets many of NLTK’s functions. Being a Python library, TextBlob processes textual data more efficiently.
It gives an easy-to-access interface that is understandable for various tasks involving sentiment analysis, PoS tagging, and noun phrase extraction.
It is scalable and recommended tool for NLP novices.
It gives simple API for segmenting into common NLP tasks such as part-of-speech tagging, classification, translation, WordNet integration, word inflexion, etc.
Stanford CoreNLP incorporates an array of human language technology tools with the aim to make simple and productive use of linguistics analytics tools to a piece of text. With CoreNLP, an individual can extract all sorts of text attributes in some lines of codes only.
It blends many of Stanford’s NLP tools involving part-of-speech(POS) tagging, named entity recognizer(NER), the parser, sentiment analysis, bootstrapped pattern learning, and information extraction tools. However, these tools deploy rule-based, probabilistic machine learning and deep learning algorithms/elements.
Its scalability makes it an excellent NLP tool for information retrieval, chatbots training, and text processing and generation.
The Pattern is a text processing, web mining, natural language processing, machine learning and network analysis module for Python programming language. It can be considered as a very potent tool for the both scientific and non-scientific congregation.
It has various tools for data mining, natural language processing tasks like part-of-speech tagging, n-gram search, sentiment analysis etc, machine learning problems like vector spaces, clustering, SVM, and network analysis by graph centrality and visualization.
It has very easy and straightforward syntax format,i.e., the function names and the parameters which are selected in a manner that the instructions are self-descriptive.
It renders a fast developed framework for web-developers and deep worthwhile learning environment for students.
Articulated as “Pineapple”, PyNLPI is the Python library in NLP that includes several customized modules required for most of the common NLP tasks.
It can be used for some of the fundamental tasks such as extraction of n-grams and frequency lists, and to develop an easy language model.
One most promising feature of PyNLPI is the extensive library for working with FoLiA XML (format for Linguistics Annotation).
It is broken down into distinct models and packages, each of them is beneficial for standard and advanced NLP tasks.
This is the handiest NLP library that renders developers a broad width of algorithms in order to build machine learning models. The potential of this library is the automated classes methods. In addition to that, the scikit-learn pursues enhanced documentation that assists developers in making most of its features.
It gives numerous functions for implementing the bag-of-words methods for making features to deal with text classification problems. However, it doesn’t deploy neural networks for text preprocessing.
Polyglot is a Python NLP library, a perfect library for the specific sorts of applications that deals with large collections of languages, i.e. it supports extensive multilingual applications. It shows up with comprehensive documentation, deciphering the entry process for anymore.
Its features consist of tokenization, language detection, named entity recognition(NER), part of speech tagging, word embeddings, sentiment analysis, etc.
Since it supports multiple languages, it is a feasible choice where localization is considered as a significant role.
For advanced textual data analytics, NLP libraries and tools mark themselves as imperative, many data experts, researchers and business professionalists greatly employ natural language processing libraries in order to elicit worthwhile information from text data analysis. However, such analysis includes examining customer feedback, automated support systems, enhancing recommendation algorithms and regulating social media.
“It seems probable that once the machine thinking method had started, it would not take long to outstrip our feeble powers… They would be able to converse with each other to sharpen their wits. At some stage, therefore, we should have to expect the machines to take control.” ― Alan Turing
Through this blog, you have come to understand that a roomy array of NLP libraries and services are available, and knowing their brilliant features is the crucial key to acquiring intelligent outcomes. While some of the libraries are perfect for small-scale projects, others are suitable for personalities operating on heavy-scale data, everything depends on the project.
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