Sunday, May 28, 2023
  • Natural language processing libraries or platforms allow machines to understand, interpret, and synthesize human language.
  • NLP can assist in bridging language barriers, improving accessibility for people with disabilities, and advancing research in linguistics, psychology, and social sciences.
  • The best Natural language processing libraries include NLTK, SpaCy and Gensim

Natural language processing (NLP) is significant because it allows machines to understand, interpret, and synthesize human language, which is the primary mode of human communication.

Using NLP, machines can analyze and make sense of vast amounts of unstructured textual data. This boosts their ability to aid people in various jobs, such as customer support, content generation, and decision-making.

Furthermore, NLP can assist in bridging language barriers, improving accessibility for people with disabilities, and advancing research in linguistics, psychology, and social sciences.

Below, we detail five NLP libraries that can be used for various purposes.

National Language Toolkit (NLTK)

Owing to its large ecosystem of Natural Language Processing modules and tools, Python extensively finds usage as one of the most popular programming languages for NLP. Python’s popularity in data science and machine learning has made it a popular choice for many NLP applications. NLTK’s ease of use and rich documentation further contribute to its popularity.

NLTK is a popular Python NLP library. It supports NLP machine learning for tokenization, stemming, tagging, and parsing. NLTK is excellent for beginners and is utilized in many academic NLP courses.

Tokenization is the process of separating a document into more manageable chunks, such as single words, phrases, or sentences. It seeks to arrange the text in order to facilitate programmatic analysis and manipulation. Tokenization is a common pre-processing step in Natural Language Processing applications such as text categorization and sentiment analysis.

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Through the process of stemming, words are formed from their base or root form. For example, “run” is the source of the words “running,” “runner,” and “run.” Tagging is the process of identifying each word’s part of speech (POS) within a text, such as a noun, verb, adjective, and so on. POS tagging is an important step in many NLP applications, such as text analysis and machine translation, where knowing the grammatical structure of a sentence is key.


SpaCy is a Python NLP library that is fast and efficient. It is user-friendly and includes tools for entity recognition, part-of-speech tagging, dependency parsing, and more. Because of its speed and precision, SpaCy is frequently utilized in the business.

Dependency parsing evaluates a phrase’s grammatical structure by establishing word relationships. It considers syntactic and semantic dependencies, a technique in natural language processing. It then generates a parse tree that captures these relationships. This technique helps in analyzing the grammatical structure of a text and understanding the relationships between words in a sentence.


Gensim is an open-source library for topic modelling, document similarity analysis, and other natural language processing (NLP) activities. The toolkit includes tools for methods like latent Dirichlet allocation (LDA) and word2vec, which generate word embeddings.

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LDA is a probabilistic topic modelling approach that finds the underlying themes in a series of documents. The neural network-based model Word2vec learns to map words to vectors, enabling semantic analysis and comparisons of word similarity.

Using Blockchain and Natural Language Processing libraries together

Natural Language Processing libraries and blockchains are two independent technologies that can be used in a variety of ways. For example, NLP methods can evaluate and comprehend text-based content on blockchain platforms, such as smart contracts and transaction records.

Natural Language Processing libraries can provide natural language interfaces for blockchain applications, enabling users to communicate with the system in normal language. The usage of blockchain to safeguard and certify NLP-based products, such as chatbots or sentiment analysis tools, can ensure the integrity and privacy of user data.


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