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The goal of NLP (Natural Language Processing), a branch of artificial intelligence, is to comprehend the semantics and implications of natural human languages. It focuses on collecting useful information from the text and using that information to train data models. Text mining, text classification, text analysis, sentiment analysis, word sequencing, speech recognition and synthesis, machine translation, and dialogue systems are only a few of the major NLP tasks. Today, thanks to the development of usable NLP Libraries, NLP is finding applications in a wide range of industries. NLP is becoming an essential component of Deep Learning research. Developing chatbots, patent research & analysis, voice/speech recognition, patient data processing, and searching picture content, among other NLP use cases, requires extracting meaningful information from free text. The primary goal of NLP Libraries is to make text preprocessing easier. Decent NLP Libraries should be able to transform free text phrases into structured characteristics (such as cost per hour) that can be readily fed into Machine Learning or Deep Learning pipelines. In addition, an NLP Library should have an easy-to-learn API and be able to rapidly apply the latest and best algorithms and models. Even though there are various NLP Libraries built for specialized NLP applications, we'll talk about the functions of the best Python NLP Libraries in this article.
NLTK is a popular Python framework for creating programs that interact with human language data. It provides a hands-on introduction to language processing programming. For phrase recognition, tokenization, lemmatization, stemming, parsing, chunking, and POS tagging, NLTK includes several text processing packages. Over 50 corpora and lexical resources are accessible through NLTK's user-friendly interfaces. The program comes with all of the necessary features for nearly any type of Natural Language Processing work that can be done with Python.
Gensim is one of the popular Python NLP Libraries for "topic modeling, document indexing, and similarity retrieval with huge corpora," according to the developers. Gensim's methods are memory-independent in terms of corpus size, thus it can handle input bigger than RAM. Gensim provides for efficient multicore implementations of common algorithms such as online Latent Semantic Analysis (LSA/LSI/SVD), Latent Dirichlet Allocation (LDA), Random Projections (RP), Hierarchical Dirichlet Process (HDP), or word2vec Deep Learning, thanks to its simple interfaces. Gensim comes with a lot of documentation and lessons for Jupyter Notebook. For scientific computing, NumPy and SciPy are essential. As a result, before installing Gensim, you must first install these two Python programs.
Stanford CoreNLP is a collection of tools for human language technology. Its goal is to make using linguistic analysis tools on a piece of text simple and efficient. In only a few lines of code, CoreNLP can extract all types of text characteristics (including named-entity recognition, part-of-speech tagging, and so on). Because CoreNLP is built in Java, it necessitates the installation of Java on your device. It does, however, provide programming interfaces for several well-known computer languages, including Python. The parser, sentiment analysis, bootstrapped pattern learning, part-of-speech (POS) tagger, named entity recognizer (NER), and coreference resolution system, to mention a few, are all included in the program. Aside from English, CoreNLP supports four more languages: Arabic, Chinese, German, French, and Spanish.
SpaCy is a Python-based open-source Natural Language Processing toolkit. It is built specifically for commercial use, allowing you to create applications that process and comprehend large amounts of text. SpaCy can help Deep Learning by preprocessing text. It may be used to create systems that interpret the natural language or extract information. SpaCy comes with statistical models and word vectors that have been pre-trained. Over 49 languages may be tokenized with it. SpaCy offers cutting-edge speed, parsing, named entity identification, tagging models based on convolutional neural networks, and Deep Learning integration.
TextBlob is a text processing module written in Python 2 and 3. It focuses on offering familiar interfaces for typical text-processing processes. TextBlob objects may be thought of as Natural Language Processing-trained Python strings. Part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, language translation, word inflection, parsing, n-grams, and WordNet integration are all possible using TextBlob's API.
The pattern is a Python program that may be used for text processing, web mining, Natural Language Processing, Machine Learning, and network analysis. It includes data mining tools (Google, Twitter, Wikipedia API, a web crawler, and an HTML DOM parser), NLP (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), ML (vector space model, clustering, SVM), and network analysis tools (graph centrality and visualization). Pattern may be an effective tool for both scientists and non-scientists. It features a basic and easy-to-understand syntax, with function names and arguments that are self-explanatory. The pattern provides a quick development framework for web developers, as well as a useful learning environment for students.
PyNLPl is a Python package for Natural Language Processing that is pronounced "pineapple." It includes a set of Python modules designed specifically for Natural Language Processing applications. PyNLPl has a large library for working with FoLiA XML, which is one of its most prominent features (Format for Linguistic Annotation). PyNLPl is divided into many modules and packages, each of which is helpful for both basic and complex NLP tasks. While PyNLPl may be used for basic NLP activities such as n-gram extraction and frequency lists, as well as the creation of a rudimentary language model, it also supports more sophisticated data types and algorithms for advanced NLP tasks.
We can observe that, while most of the NLP Libraries can execute comparable NLP tasks, each offers distinct features/approaches for certain NLP applications after receiving a thorough overview of their functions. The use of these NLP Libraries in Python is primarily determined by the NLP challenge at hand.
