Natural language processing is about building applications and assistance/services that can understand human languages. It is filed that interacts amidst computers and human. It is mainly used for text analysis that provides computers with a way to recognize the human language. (As discussing NLP, read about SqueexeBERT, an agile mobile NLP)
Moreover, NLP is the technology, that provides the potential to all the chatbots, voice assistants, predictive text and text applications, has unfolded in recent years. There is a wide variety of open-source NLP tools available. (Related blog:7 Natural Language Processing Techniques for Extracting Information)
With the help of NLP tools and techniques, most of the NLP task can be performed, a few examples of NLP tasks involve speech recognition, summarization, topic segmentation, understanding what the content is about or sentiment analysis etc.
This blog covers an introductory voyage to natural language toolkit(NLTK), an NLP tool for Python, its significant differences with another novel tool “spaCy”.
NLTK, a preeminent platform, that is used for developing Python programs for operating with human language data, as stated in the NLTK 3.5 document. It is a suite of open source program modules, tutorials and problem sets for presenting prepared computational linguistics courseware. NLTK incorporates symbolic and statistical Natural Language Processing and is assimilated to interpreted corpora for teachers and students especially.
Most significant features of NLTK includes;
It presents easy-to-implement interfaces across 50 corpora and linguistics sources, for example, WordNet, text processing libraries for classification, tokenization, and wrappers for industrial-strength NLP libraries.
NLTK is suitable for translators, educators, researchers, and industrial applications and accessible at Windows, Mac OS X, and Linux.
It attains a firsthand guide that introduces in computational linguistics and programming fundamentals for Python due to which it becomes proper fit for lexicographers who don’t have intense knowledge in programming. (Recommending blog: Introduction to Natural Language Processing: Text Cleaning & Preprocessing)
NLTK is an ultimate combination of three factors; first, it was intentionally designed as courseware and provides pedagogical objectives as primary status, second, its target audience comprises both linguists and computer specialists, and it is not only convenient but challenging also at various levels of early computational skill and thirdly, it deeply depends on an object-oriented composing language that supports swift prototyping and intelligent programming.
In accordance with the paper published, the following are the fundamental requirements of using NLTK;
Easy to implement: One of the main objectives behind using this toolkit is to enable users to focus on developing NLP components and system. The more time students must spend learning to use the toolkit, the less useful it is.
Consistency: The toolkit must apply compatible data structures and interfaces.
Extensibility: The toolkit easily adapts novel components, whether such components imitate or prolong the existing functionality and performance of toolkit. The toolkit should be arranged in a precise manner that appending new extensions would match into the toolkit’s existing infrastructure.
Documentation: There is a need to cite the toolkit, its data structure and its implementation delicately. The whole nomenclature must be picked out very sparingly and to be applied consistently.
Monotony: The toolkit should make up the ramification of producing NLP systems, and do not drop them. So, every class, determined by the tool, must be accessible for users that they could complete it by the moment ending rudimentary course in computational linguistics.
Modularity: To maintain interaction amid various components of the toolkit, it should be retained in a minimum, mild, and sharp interfaces. However, it should be plausible to finish different projects by tiny parts of the toolkit, without agonising about how to cooperate with the rest toolkit.
Comprehensiveness: The toolkit isn’t designed for rendering a wide set of tools. Certainly, various ways are offered through which users can elongate the toolkit.
Competence: NLTK doesn’t demand to be profoundly optimized concerning runtime performance. Nonetheless, it is capable enough that users can implement their NLP systems in order to perform real tasks
Ability: Its schemes and implementations are far superior to original yet vague ones.
Assignments: NLTK can be used to create assignments for students of various difficulties and scopes. After becoming familiar with the toolkit, users can make trivial changes or extensions in an existing module in NLTK. When developing a new module, NLTK gives few useful initiating points: pre-defined interfaces and data structures, and existing modules that apply the same interface.
Class demonstrations: NLTK offers graphical tools, that can be utilized in the class demonstrations, to assist in explaining elementary NLP concepts and algorithms. Such interactive tools are accepted to represent associated data structures and to bestow the step-by-step execution of algorithms.
Advanced Projects: NLTK presents users with an amenable framework for advanced projects. Standard projects include the development of totally new functionality for a priorly unsupported NLP task or the development of an entire system from existing and new modules.
NLTK reduces the tiresome infrastructure framework, typically linked with advanced projects by providing users with the basic data structures, tools, and interfaces of their need. This allows users to students to converge on the difficulties that intrigue them. The collaborative, open-source behaviour of the toolkit gives users a sense that their projects are essential contributions.
NLTK and spaCy are two of the popular NLP tools available in Python, one can design chatbots, automated summarizers, entity extraction systems with either of them. (Read a specified blog on What is spaCy in Natural Language Processing (NLP)?)
There is a substantial discrepancy separating them, some are following;
Plenty of algorithms are facilitated by NLTK in order to pick up for a specific problem, helpful of researchers, whereas spaCy sustains the appropriate algorithm for a specified problem within its toolkit and manage it up to date as the state of art revamps.
NLTK incorporates several languages, in opposite to that, spaCy have statistical models for seven languages including English, German, Spanish, French, Portuguese, Italian, and Dutch, It also braces “named entities” for multi-language.
Being a string processing library, it accounts strings as input and gives strings or lists of strings as output. In contrast to this, spaCy practises object-oriented approach, after defining text, spaCy yields document object in which words and sentences are objects in themselves. (here is another dose: Introduction to Text Analytics and Models in Natural Language Processing)
spaCy owns backing for word vectors whereas NLTK does not support.
As spaCy adopts novel and excellent algorithms, its performance is customarily good in comparison to NLTK.
NLTK tries to break the text into sentences, in contrary, spaCy builds a semantic tree for individual sentence as higher a potent approach, returns more information.
Hopefully, you have acquired a perception about the natural language toolkit, for concluding, we can say that NLTK gives an easy, extendible framework specially designed for assignments, outlines and class demonstrations as it is adequately documented, gentle to acquire and easy to implement.
With the help of NLTK, computational linguistics classes can cover a worthy experience in adopting and developing NLP components and systems. Being a blend of three factors, NLTK is a unique toolkit that is designed as courseware, goals audience comprises linguists and computer experts and depends on an object-oriented composing language.
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