Goto

Collaborating Authors

The Essential NLP Guide for data scientists (codes for top 10 NLP tasks)

#artificialintelligence

Technologies that can make a coherent summary take into account variables such as length, writing style and syntax.Automatic data summarization is part of machine learning and data mining. The main idea of summarization is to find a subset of data which contains the information of the entire set. Such techniques are widely used in industry today. Search engines are an example; others include summarization of documents, image collections and videos. Document summarization tries to create a representative summary or abstract of the entire document, by finding the most informative sentences, while in image summarization the system finds the most representative and important (i.e.


Natural Language Processing Advancements By Deep Learning: A Survey

arXiv.org Artificial Intelligence

Natural Language Processing (NLP) helps empower intelligent machines by enhancing a better understanding of the human language for linguistic-based human-computer communication. Recent developments in computational power and the advent of large amounts of linguistic data have heightened the need and demand for automating semantic analysis using data-driven approaches. The utilization of data-driven strategies is pervasive now due to the significant improvements demonstrated through the usage of deep learning methods in areas such as Computer Vision, Automatic Speech Recognition, and in particular, NLP. This survey categorizes and addresses the different aspects and applications of NLP that have benefited from deep learning. It covers core NLP tasks and applications and describes how deep learning methods and models advance these areas. We further analyze and compare different approaches and state-of-the-art models.


Bangla Natural Language Processing: A Comprehensive Review of Classical, Machine Learning, and Deep Learning Based Methods

arXiv.org Artificial Intelligence

The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it easy to use the Bangla language in the online and technical domains. There are some review papers to understand the past, previous, and future Bangla Natural Language Processing (BNLP) trends. The studies are mainly concentrated on the specific domains of BNLP, such as sentiment analysis, speech recognition, optical character recognition, and text summarization. There is an apparent scarcity of resources that contain a comprehensive study of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough review of 71 BNLP research papers and categorize them into 11 categories, namely Information Extraction, Machine Translation, Named Entity Recognition, Parsing, Parts of Speech Tagging, Question Answering System, Sentiment Analysis, Spam and Fake Detection, Text Summarization, Word Sense Disambiguation, and Speech Processing and Recognition. We study articles published between 1999 to 2021, and 50% of the papers were published after 2015. We discuss Classical, Machine Learning and Deep Learning approaches with different datasets while addressing the limitations and current and future trends of the BNLP.


Introduction to NLP – Towards Data Science

#artificialintelligence

Natural language processing (NLP) is an area of computer science and artificial intelligence that is concerned with the interaction between computers and humans in natural language. It is the driving force behind things like virtual assistants, speech recognition, sentiment analysis, automatic text summarization, machine translation and much more. In this post, you will learn the basics of natural language processing, dive into some of its techniques and also learn how NLP benefited from the recent advances in Deep Learning. Natural Language Processing (NLP) is the intersection of Computer Science, Linguistics and Machine Learning that is concerned with the communication between computers and humans in natural language. NLP is all about enabling computers to understand and generate human language. Applications of NLP techniques are Voice Assistants like Alexa and Siri but also things like Machine Translation and text-filtering.


What is Natural Language Processing?

#artificialintelligence

It's easy to understand the importance of NLP given the number of applications for it--question-and-answer (Q&A) systems, translation of text from one language to another, automatic summarization (of long texts into short summaries), grammar analysis and recommendation, sentiment analysis, and much more. This technology is even more important today given the massive amount of unstructured data generated daily in the context of news, social media, scientific and technical papers, and the variety of other sources in our connected world. Today, when we ask Alexa or Siri a question, we don't think about the complexity involved in recognizing speech, understanding the meaning of the question, and ultimately providing a response. Recent advances in state-of-the-art NLP models, BERT, and BERT's lighter successor ALBERT from Google is setting new benchmarks in the industry and allowing researchers to increase training speed of the models. In the mid-1950s, IBM sparked tremendous excitement for language understanding through what was called the Georgetown experiment, a joint development project between IBM and Georgetown University.