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Top Tools for Natural Language Processing

Ram Tavva– Ram Tavva, ITIL Expert Accredited Trainer and Director, ExcelR Solutions

What is Natural Language Processing ?

The field of Machine that assists us with understanding and potentially generating the human language is what we mean by Natural Language Processing. NLP applications and tools give us a superior comprehension of how the language may function in explicit circumstances. Besides, individuals likewise use it for various business purposes. Such proposals may incorporate information investigation, data analytics, User Interface advancement, and value proposition.

How Tools for Natural Language Processing Came into existence?

The shortfall of NLP tools blocked the advancement of technologies but in the latter part of the 90s, things started to change. Different custom content analytics and generative NLP programming started to show their latent capacity. Presently the market is overwhelmed with various natural language processing instruments and tools. All things considered, with such an assortment, it is hard to pick the best open-source NLP tool for our future undertaking.

In this article, we will take a gander at the most commonly used NLP tools, throw some light on their highlights, and their use cases.

Top 8 Tools for Natural Language Processing

1. Natural Language Toolkit (NLTK): One of the most powerful open-source programming software for building Python programs to deal with human semantics. Starting here, the NLTK library is a standard NLP device which is developed for research related and examination work. It furnishes its clients with a fundamental arrangement of devices for text-related tasks and so is considered a decent beginning stage for novices in Natural Language Processing.

The Natural Language Toolkit includes the following: Part of Speech(POS) tagging, text classification, Tokenization , Parsing, Entity Extraction, Stemming and finally, Semantic reasoning. Text corpora and lexical resources are mainly included in the NLTK interface. These include Open Multilingual Wordnet, Penn Treebank Corpus, Lin’s Dependency Thesaurus and Issue Report Corpus. Such innovation permits separating numerous experiences, including client exercises, feelings, and criticism.

Natural Language Toolkit is helpful for simple content investigation. But in cases where we need to handle huge amounts of data, NLTK would fail then. Why? Because implementation of NLTK requires massive resources.

2. GenSim: Widely used for semantic search, document analysis, and data exploration, GenSim is the ideal device for such things. It is a free open-source Python library for addressing reports as semantic vectors, as effectively (PC astute) and effortlessly (human-wise) it could really be expected to do. Gensim is intended to handle crude, unstructured computerized messages (plain content) utilizing individual AI calculations. It would assist us with exploring the different information bases and records.

The key GenSim includes word vectors. It considers the archives as successions of vectors and bunches. And afterward, GenSim groups them together. GenSim is additionally an asset saving with regards to managing a lot of information.

The principle GenSim use cases are: Semantic pursuit applications, Information examination, data analytics, Text generating applications such as chatbot, administration customization, text synopsis, and so forth.

If you are enjoying reading this blog on Tools for Natural Language Processing, then it’s an obvious case that you are a Machine Learning enthusiast. An endless domain of so many algorithms and techniques, research on which will never come to an end and its applications to solve real world problems is enormous. Among all such various techniques, regularization is one of the most important aspects. But yes, we won’t look into it here. Rather check L1 and L2 Regularization and enjoy some more learning.

3. Google Cloud: The platform which provides us with a few pre-prepared models to estimate content characterization, investigation, and element extraction, among all others is referred to as Google Cloud Natural Language API. Likewise, it offers AutoML Natural Language, which permits us to fabricate modified AI models. Being a vast component of the Google Cloud framework, it utilizes Google question noting and language understanding of the tech world.

The Natural Language Processing (NLP) based research performed at Google Cloud revolves around calculations that apply at scale, across dialects, and across areas. Our frameworks are utilized from numerous points of view across Google, affecting client experience in search, portable applications, promotions, interpretation and a lot more.

4. Stanford CoreNLP Library: An integrated framework, the Stanford NLP library is a multi-reason apparatus for text investigation fully written in Java. Like NLTK, Stanford CoreNLP gives a wide range of characteristic language preparation programs. Yet, on the off chance that if we need more, we can easily utilize custom modules.

The principal benefit of the Stanford NLP library is its wide versatility and scalability. Keeping aside NLTK, Stanford Core NLP is an ideal solution for handling a lot of information and performing complex tasks.

With its high versatility, Stanford CoreNLP is a phenomenal decision for many purposes such as sentiment analysis (web-based media, client care), conversational interfaces (chatbots), data scratching from open sources (web-based media, client produced reviews) and text handling, and generation(customer support, internet business). This device has the capacity to scrap out a wide range of data.

5. Apache OpenNLP : The machine learning based toolkit which is widely used for processing of natural language text processing is known as Apache OpenNLP. It is an open-source library for the individuals who lean toward practical implementations and openness. It too utilizes Java NLP libraries with Python decorators and that is why it is quite similar to Stanford CoreNLP.

While NLTK and Stanford CoreNLP are best in class libraries with huge loads of augmentations, OpenNLP is a straightforward yet valuable tool. Here, we can easily arrange OpenNLP in the manner in which we require and dispose of pointless highlights.

Apache OpenLP is the correct decision to implement in situations which include Sentence Detection, Named Entity Recognition, Tokenization and POS labeling.

We are free to utilize OpenNLP for a wide range of text information examination and assessment investigation tasks. It turns out to be an amazing tool for planning text corpora in generators and conversational interfaces.

6. TextBlob: A well defined Python library that fills in as an expansion of NLTK, permitting us to play out similar NLP errands in a considerably more natural and easy to use interface. It upholds complex investigation and procedure on printed information. For dictionary based methodologies, an estimation is characterized by its semantic direction and the power of each word in the sentence.

The extremity and subjectivity of a sentence is also returned by Textblob. Its expectation to absorb information is more simple than compared with other open-source libraries. So it’s a brilliant decision for novices, who need to handle NLP undertakings like text characterization, sentiment analysis, grammatical form labeling, POS tagging and a lot more advantageous ones to include.

7. IBM Watson: A complete set-up of AI administrations stored organizedly in the IBM Cloud is what we describe as IBM watson. Its main aim is to understand Human Language Processing. It uses deep learning techniques which permits us to distinguish and remove catchphrases, emotions, classifications, entities and a lot more to add to the list. It’s highly adaptable, and so can be used in various enterprises, from health sector to business, and has a store of archives to assist us in the beginning. The impact of IBM Watson in the field of NLP is that it empowers our workers to settle on more educated choices and save time with real time internet searching and text mining capacities that perform text extraction and investigate connections and examples covered in unstructured information.

8. SpaCy : Spacy is considered to be one of the most popular and recently developed open source NLP tools. It has inbuilt Python libraries which are simple to utilize, quick to access, and moreover provides the most accurate analysis of any NLP library available till date. In contrast to NLTK or CoreNLP, which show various calculations for each assignment, SpaCy keeps its menu short and presents the best accessible alternative for each job needing to be done.

This library is an incredible alternative in cases when we want to get our text ready  for profound learning, or for text extraction. The only disadvantage is that it’s just accessible in English. 

The Bottom Line

NLP, the branch of Machine Learning, has been the greatest innovation that powers all the chatbots, voice collaborators, prescient content, and other discourse/text applications that pervade our lives, has developed fundamentally over the last few couple of years. From the above reading we can conclude that a wide assortment of open source NLP tools are available to use for our next text or voice based NLP project.

In a broader sense, these tools are assisting organizations with getting bits of knowledge from unstructured content information like messages, online audits, web-based media posts, and then some more. There are numerous online apparatuses that make NLP available to our business, such as open-source and SaaS.

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