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Natural Language Processing is the fastest-growing subset of AI that applies linguistics and computer science to make human language understandable to machines. There are new advancements every year. New tools of NLP are evolving and the old ones are being updated with more developed features.
Before going with the top 10 NLP tools services, it is important to mention that all the tools are either recently released or are upgraded with new features. The tools named below are free and open-source instruments.
Natural Language Toolkit, one of the leading tools for NLP, renders a whole set of programs and libraries to execute statistical and symbolic analysis in Python. This tool helps in separating a piece of text into smaller units (tokenization). Through this tool, you can recognize named entities and also can tag some text. It is the leading tool of NLP and is easy to use.
This tool is a successor of NLTK. It comes with pre-trained statistical models and word vectors. It is a library created for use in Python and Cython. It supports tokenization for 49+ languages.it enables to break the text into semantic segments like articles, words, punctuation. It can be used for named entity recognition (NER) with pre-trained classes, recognizing dependencies in sentences. It provides the fastest and most accurate syntactic analysis than any NLP library.
This tool is also applied in Python. It is a high-accuracy parser with models for 11 languages. It cracks the syntactic structure of sentences into nested sub phrases. This tool enables the easy extraction of information from syntactic constructs. The tool requires a piece of minimal knowledge and effort to start working with.
It is a new tool that was released recently by Open AI. It is quite a trend now. It is an autocompleting program and is used mainly for predicting text. The major advantage of using this tool is the sheer volume of data, it was pre-trained on (175 billion parameters). Using GPT-3, one can get outcomes that are closer to real human language.
It is a powerful tool for prototyping with good text processing capabilities. This tool is less effective for production if compared to SpaCy but it is largely used in research. Additionally, it has PyTorch, a very popular deep learning framework that enables customizing models more flexibly than SpaCy. It automates some of the tasks which are essential for almost every deep learning model. It provides a lot of modules like Seq2VecEncoder, Seq2SeqEncoder.
This tool was designed based on NLTK. For the probationer, it is the best option to understand the complexities of NLP and designing prototypes for their projects. The tool enables sentiment analysis, tokenization, translation, phrase extraction, part-of-speech tagging, lemmatization, classification, spelling correction, etc.
It is an easy-to-use, NLP tool that helps in obtaining valuable insights from the text data. The tool enables in performing text analysis such as sentiment analysis, topic classification, or keyword extraction, etc. The tool is used to train text analysis models to deliver accurate insights and once it is done then you can easily connect the models to your favorite apps like Excel. Google sheets through MonkeyLearn's APIs that are available in all major programming languages.
IBM Watson is a room of AI services stored in the IBM Cloud. One of its primary features is Natural Language Understanding, which enables you to recognize and extricate keywords, categories, emotions, entities, and more. It can be modified to different industries, from finance to healthcare. It has a store of documents that helps to get started.
This service is designed for information extraction and natural language processing. It has many algorithms that can be deployed irrespective of the size of the collection of linguistic data. As it is dependent on NumPy and SciPy (Python packages for scientific computing), the user needs to install these two packages before installing GenSim. The tool is extremely structured, and it has top-notch memory optimization and processing speed. It enables operating large text files even without loading the whole file in memory. Gensim doesn't require costly annotations or hand tagging of documents because it uses unsupervised models.
It is a strong, fast annotator for discretionary texts and is largely used in production. It is primarily Java-based but the creators of the tool provided an alternative for Python which has the same functionality. It is easy to retrieve functions that are corresponding to annotations and it stores documents and sentences as objects (Intuitive Syntax). It can grasp raw human language text as input and produce the base structures of words, parts of speech, whether they are names of companies, people, etc., decode dates, times, and numeric quantities. It also marks up the form of sentences in terms of phrases or word dependencies and stipulates the noun phrases referring to the same entities.
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