We spend a lot of time via chat, email, websites, and social media. But we don’t always pause to consider the massive amounts of text data we produce every second. Customer behavior and feedback are invaluable sources of insights for businesses. In this blog, we discuss the best NLP tools.
Yet, most businesses are still struggling to find the best way to analyze this data. It’s unstructured data, which is difficult for computers to understand. Processing large amounts of information become time-consuming, repetitive.
What exactly are NLP Tools?
Natural Language is how humans communicate with one another. You are communicating through text as you read this article. That deals with the interpretation and manipulation of human speech. In a variety of ways, we use NLP, from statistical and machine learning approaches.
Best Natural Language Processing (NLP) Tools.
Natural Language Toolkit (NLTK) is a leading NLP instrument. It is a collection of libraries and programs for performing analysis in Python. It is also one of the earliest projects, which is an interesting fact. The first version was from the University of Pennsylvania in 2001.
This tool is the replacement for NLTK. A pre-trained statistical as well as word vector. Divide the text into semantic segments, such as articles, words, and punctuation. It recognizes dependencies in sentences using named entity recognition (NER) with pre-trained classes. It provides the most accurate and fastest syntactic analysis of any NLP library.
MonkeyLearn, an NLP-powered platform that can help you gain valuable insights from text data. You can create a customized machine learning model for your business.
Once you have trained your models to provide accurate insights, you can connect your text models to your favorite apps using MonkeyLearn APIs.
Aylien is a SaaS API that analyzes large amounts of text-based data. It uses NLP tools as text summarization, article extraction, entity extraction.
IBM Watson is a collection of artificial intelligence (AI) services in the IBM Cloud. Natural Language Understanding is one of its key features. It can tailor various industries from healthcare to finance.
05.Google Cloud Platform
It includes AutoML Natural Language, which creates personalized machine learning models.
It makes use of Google question-answering and language understanding technology.
06.Berkeley Neural Parser.
It is a high-accuracy parser written in Python that has models for 11 languages. It divides sentence syntactic structure into nested substructures.
Amazon Comprehend is a service that integrates into the Amazon Web Services infrastructure. This API is used for NLP tasks like sentiment analysis, topic modeling. In addition, there is a variant for those who work in healthcare: Amazon Comprehend Medical.
This service is for the extraction of information from natural language. It has many algorithms that can be used regardless of the size of the linguistic data collection. Because it relies on NumPy and SciPy, the user must first install these two packages. It allows you to work with large text files without having to load the entire file into memory.
It is a powerful and fast annotator for discretionary texts that are in production. It is Java-based. It provides a Python alternative with the same functionality. It is simple to retrieve annotation-related functions. It can take raw human language text as input and generate the base structures.
Structures are words, speech, names of companies, people. It also decodes dates, times, and numeric quantities. It specifies the noun phrases that refer to the same entities. Finally, it marks up the form of a sentence of terms. CoreNLP is one of the best NLP tools to use.