Every day businesses generate a mountain of data; it is an inevitable result of almost every business interaction—emails, websites, blog posts, whitepapers, internal documentation, reports…the list is endless. But this is not clutter. There are valuable insights to be mined from this mountain of data. But analyzing this unstructured data near overwhelming for humans to sort through, and it’s just as hard for computers to understand. Enter NLP. A subset of artificial intelligence, NLP leverages linguistics and computer science to make human language intelligible to machines. By enabling computers to automatically analyze massive sets of data, NLP can sort through massive data sets to unearth meaningful information in just seconds. NLP can conduct various text analyses, including sentiment analysis, topic classification, and more.
Best of all, you don’t need to build an NLP application from scratch. There are several NLP tools that are available through SaaS models and open-source libraries that can be implemented easily.
If you’re looking to hit the ground running, SaaS tools are for you. These tools are ready-to-use, mostly cloud-based solutions that need little to no code to be implemented.
Pre-trained NLP models on SaaS platforms are ideal for those looking for a code-free solution; Professional developers—anyone with the ability to code, really—and those who want a flexible, low-code, option to simplify their work can leverage the APIs provided by SaaS platforms.
For those users looking for full customization of their NLP tools, there are open-source libraries. These are free, flexible, and allow major customization of your NLP tools.
As open-source libraries are aimed at developers, they can be fairly complex to grasp and users will need experience in machine learning to build open-source NLP tools.
Fortunately, most of these frameworks have support communities, so you can count on help when and if you need it. Ready to get started with using NLP, here are some powerful online tools that can help.
1.BERT
Bert—an acronym for Bidirectional Encoder Representations from Transformers—is an open-source machine learning framework, designed to help computers understand the meaning of ambiguous language in text. Bert works by using the text surrounding the problematic text to establish context. BERT framework was pre-trained using Wikipedia content and can be further fine-tuned with Q&A datasets.
BERT is based on a deep learning model called Transformers wherein each output element is connected to an input element, and the weightage between them is dynamically calculated based on their connection. Unlike traditional language models that only read text input sequentially, viz left-to-right or right-to-left, BERT is designed to read in both directions at once. This is described as bi-directionality.
Using bidirectionality, BERT is pre-trained on Masked Language Modeling and Next Sentence Prediction.
Masked Language Model (MLM) is used to predict a masked word based on the hidden word’s context. Next Sentence Prediction can predict whether two given sentences have a logical, sequential connection or whether their relationship is simply random.
2.IBM Watson
IBM Watson is an offering by IBM cloud. It comprises a suite of AI services, which are stored in the IBM Cloud. One of its key features, Natural Language Understanding, identification, and extraction of keywords, categories, emotions, entities, and more.
IBM Watson is versatile. It can be tailored to the needs of different industries, from HLO to Fintech, and has a trove of documents to help you get started.
3.Google Cloud Natural Language API
Google Cloud Natural Language API uses pre-trained models to run sentiment analysis, content classification, entity extraction, etc. Google Cloud also enables the building of bespoke ML models using AutoML Natural Language. Google Cloud Natural Language API is an element of Google’s Cloud infrastructure. it offers several benefits, including
Using Entity analysis to identify and label fields within documents, such as emails, chats, social media posts, and interactions, and run sentiment analysis to map customer expectations. Users can also use the Speech-to-text API to extract insights from audio content, while Vision API brings optical character recognition for scanned documents
To get you started on your NLP journey, Google Cloud’s Natural Language AI even gives new customers USD300 in free credits to use on Natural Language, Plus 5,000 units for analyzing unstructured text every month, which is free, i.e. not adjusted against your credits.
4.Amazon Comprehend
Amazon Web Services too offers an NLP service, known as Amazon Comprehend. It is integrated with the Amazon Web Services infrastructure. This API can be used for NLP tasks such as sentiment analysis, topic modeling, entity recognition, and more.
There’s also a separate variant for the healthcare industry: Amazon Comprehend Medical. It allows you to perform advanced analysis of medical data using Machine Learning.
5.SpaCy
SpaCy is one of the latest open-source Natural Language Processing services using Python libraries. It’s exceedingly fast and easy to use. SpaCy is supported by detailed documentation and can handle large data volumes. It also boasts a series of pre-trained NLP models that make your job even easier.
SpaCy makes it easy to select the best algorithm for each task; it simply serves up the best available option. Bu keeping its menu short and displaying the best-fit option, it saves you the task of work of going through a large menu of algorithms every time you want to run a certain task.
SpaCy’s library is a great option to prepare text for deep learning and extraction tasks. But only in English…as it is currently only available in English.
6.NLTK (Natural Language Toolkit)
NLTK is a Python library and another leading tool used to build NLP models. NLTK has built a large and active community, and it also offers several tutorials for language processing and sample datasets. Users can also avail of several resources provided with the toolkit, including a comprehensive Language Processing and Python handbook.
NLTK’s library does have a longer learning curve but it’s considered an amazing sandbox to obtain hands-on NLP experience. It has a modular structure and provides tools to conduct NLP tasks like tokenization, tagging, stemming, parsing, and classification, among others.
- TextBlob
Like NLTK, TextBlog too is a Python library; in fact, it is built to work as an extension of NLTK. TextBlog lets you tackle NLP tasks like sentiment analysis, text classification, part-of-speech tagging, and more. It has an intuitive, user-friendly interface that makes it simpler for users to perform the same NLP tasks (as NLTK), making it an excellent choice for beginners.
- Stanford Core NLP
The prestigious Stanford University’s NLP community gets the credit for building and maintaining Stanford Core NLP. The tool is built using Java; which means users need to install JDK on their computers to use it. However, it provides APIs in most programming languages.
Stanford Core NLP toolkit enables users to perform a variety of NLP tasks, including tagging (parts of speech), tokenization, and named entity recognition, among others. It is built for scalability and speed, making it a good choice for complex tasks.
- Gensim
Gensim is a specialized Python library. It is mostly used to handle topic modeling tasks, for which it uses algorithms like Latent Dirichlet Allocation (LDA). Gensim excels at recognizing text similarities, texts indexing, and navigating different documents. It is fast, scalable, and can handle large volumes of data with
