Natural Language Processing (NLP) is one of the most interesting and fast-growing subset of Artificial Intelligence & this realm is developing very rapidly. Every year or even within months there are new advancements are coming in existence. New tools are appearing, and existing ones are being updated with more progressive features. Natural Language Processing uses linguistics & computer technology to make human language easy to understand for machines. Today, in our blog post we have decided to mention some of the best tools of NLP subset of AI.
NLTK (Natural Language Toolkit)
Natural Language Toolkit is one of the leading tools in NLP that renders a collection of programs and libraries to perform statistical analysis & calculation. This tool helps in to break down piece of information into smaller units or tokenization and it helps to identify named entities & can also tag some text. This leading NLP tool is very easy to use.
This library is specifically designed for the use in Python and Cython languages which is a successor of NLTK that comes with pre-trained statistical models and word vectors and it also supports tokenization in over 49 languages. This library is one of the best in terms of working with tokenization which allows you to break the text or piece of information into semantic segments like words, articles, punctuation. These segments can be illustrated later as vectors, so you can compare them.
Berkeley Neural Parser
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.
GPT-3 is a new instrument released recently by Open AI. It is quite trendy and at the same time truly powerful. It is used mainly for predicting text, so it is an auto completing program. Upload several examples of needed text, and GPT-3 will generate something similar and entirely unique.
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.
Text Blob is a python library developed on the basis of NLTK which is very best option for beginners to understand the complexities of Natural Language Processing NLP systems. This tool enables sentiment analysis, tokenization, translation, phrase extraction, part-of-speech tagging, lemmatization, classification, spelling correction.
BERT means Bidirectional Encoder Representations by Transformers which is a pre trained model from Google, designed to better understand what people are looking for. Unlike the old context less approaches like word2vec or GloVe, BERT takes into account the surrounding words, which can obviously affect the meaning of the word itself.
Core NLP 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. 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.
Gen Sim is a python library, specifically designed for extraction of information and Natural Language Processing and has multiple algorithms to deploy irrespective of the size of linguistic data. This library is dependent of NumPy and SciPy, both are python packages for scientific computing analysis. This library is also extremely efficient, and it has top-notch memory optimization and processing speed.
This industry domain is growing very fast and its opportunities will be in demand for a long time.