📝EditorialMassively large pretrained models have become the norm in natural language processing (NLP). It seems that every other month, we achieve a new milestone in terms of the size of language models. And yet, we can’t stop writing about it because it’s so fascinating. When GPT-3 reached 175 billion parameters a few months ago, it seemed that we were close to the peak in size of language models. Since then, such models as Switch Transformer and the recently announced Wu Dao 2.0 have…… Read more...
Today’s virtual assistants and chatbots typically follow simple rules (if this then that) in order to respond to questions. Recent advances in statistical machine learning can add some flexibility by, for example, letting a machine find an answer to a question by searching through large amounts of text. However, both of these approaches can fall victim to the vast complexity and ambiguity of meaning often encoded in language.
Machines watch not what you say, but how you say things. NLP (Natural Language Processing) is not just about words but about context. Google used to match keywords and offer list of links.… Read more...
In this issue:we discuss Self-Supervised Learning for Language; we explore XLM-R, one of the most powerful SSL cross-lingual models ever built;we cover Facebook’s fastText, a library for representation learning in language tasks.Give a gift subscription💡 ML Concept of the Day: Self-Supervised Learning for Language Continuing our series about self-supervised learning (SSL), we would like to cover its applications in language. Without a doubt, natural language processing (NLP) has been…… Read more...
Content assists you in achieving your objectives and is regarded as the essential factor in many fields. Content can be many things like a thesis for students, any blog post to engage bloggers, or a marketing post to attract customers for digital businesses.
Unique and valuable content is what people want from you. Content that is unique and comprehensive is a core in any type of writing, be it an article, blog, or scholarly document. This will help you build your reputation while also preventing you from being accused of Plagiarism. The Internet is a primary means of gathering information and writing about anything you feel like.… Read more...
According to the SAS Institute:
“Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.”
Artificial intelligence includes the following elements:
Models of human behaviorModels of human thoughtSystems that behave intelligentlySystems that behave rationallyA set of specific applications that use techniques in machine learning, deep learning and others
In the larger picture of Data Science, artificial intelligence (AI) can encompass (among others):
Other Definitions of Artificial Intelligence Include:
“Strategy to make data analytics tools smarter.”… Read more...
By Harshit Tyagi, Data Science Instructor | Mentor | YouTuber
One of the very interesting and widely used applications of NLP is Named Entity Recognition(NER).
Getting insights from raw and unstructured data is of vital importance. Uploading a document and getting the important bits of information from it is called information retrieval.
Information retrieval has been a major task/challenge in NLP. And NER(or NEL — Named Entity Linking) is used in several domains(finance, drugs, e-commerce, etc.) for information retrieval purposes.
In this tutorial post, I’ll show you how you can leverage NEL to develop a custom stock market news feed that lists down the buzzing stocks on the internet.
Strong AI or General AI: machine display all person-like behavior. This would be a system that can do anything a human can (perhaps without purely physical things). This is fairly generic, and includes all kinds of tasks, such as planning, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (making art or poetry), etc. Its basically Sci-Fi.
Weak AI or Narrow AI. Confined to very narrow tasks. No meaning, just tasks. Is what´s around today in technology. Artifical personal assistants, bots, etc. They are not General AI, otherwise they would get tired of your orders.… Read more...
Click to learn more about author Ben Lorica.
Natural Language Processing (NLP) has been on the rise for several years, and for good reason. With the ability to identify new variants of COVID-19, improve customer service, and significantly refine search capabilities, use cases are expanding as the technology proliferates. While some verticals have adopted NLP faster than others, new global research shows that budgets are growing across industries, geographies, company size, and levels of expertise.
In its second year, John Snow Labs’ and Gradient Flow’s NLP Industry Survey shows investments in NLP have jumped from at least 10% to nearly doubling for a majority of technologists.… Read more...
If you worked on any natural language processing (NLP) tasks in the last three years, you have certainly noticed the widespread use of BERT, or similar large pretrained models, as a base to fine-tune on the task of interest to achieve outstanding results.
Pretrained models allow one to achieve high accuracy on the downstream task with relatively low data and training time. With their massive pretraining they have already learnt much about the statistical structure of natural language and need to learn how to answer for the specific task. However, due to their massive size, most people do not have the needed resources to train one of them and have to rely on the publicly existing models.
Once these types of data have been cleaned, they do more than show organized data sets. They reveal unlimited possibilities, and AI analytics can reveal these possibilities faster and more efficiently than ever before.
Data scientists have always been expected to curate data into ‘aha’ moments and tell stories that can reach a wider business audience. But what is the cost of this curation?
The real signal is in the noise
Tidy data doesn’t help that much.
Every aggregation and pivot performed on datasets reduces the total amount of information available to analyze. That clever NLP topic mining on free text fields was no doubt very useful, but the raw text is more interesting.
A primer on the different levels of explainability and how each can be used across the ML lifecycle
In the last decade, significant technological progress has been driven rapidly by numerous advances in applications of machine learning. Novel ML techniques have revolutionized industries by cracking historically elusive problems in computer vision, natural language processing, robotics, and many others. Today it’s not hyperbolic to say that ML has changed how we work, how we shop, and how we play.
While many models have increased in performance, delivering state-of-the-art results on popular datasets and challenges, models have also increased in complexity. In particular, the ability to introspect and understand why a model made a particular prediction has become more and more difficult.… Read more...
This series of articles focuses on Deep Learning algorithms, which have been getting a lot of attention in the last few years, as many of its applications take center stage in our day-to-day life. From self-driving cars to voice assistants, face recognition or the ability to transcribe speech into text.
These applications are just the tip of the iceberg. A long path of research and incremental applications has been paved since the early 1940’s. The improvements and widespread applications we’re seeing today are the culmination of the hardware and data availability catching up with computational demands of these complex methods.
We are living in an age where we simply need to speak to the VA (voice assistant) and command to get things done for us. This is where NLP or Natural language processing with AI comes into the picture. As the subset of machine learning and an AI component, “NLP was first implemented in around 1952 as per the Hodgkin-Hexley model”. While, it was Alan Turing in 1950, who first recognized that a ‘thinking machine’ should be able to interpret and understand conversations in the language…… Read more...
What if I want to know how words have changed over time? For example, I may want to quantify the ways certain words (such as “mask” or “lockdown”) were used before the COVID-19 pandemic and how they evolved through the pandemic. Detecting how and when word usage changed over time can be useful from a linguistic and cultural standpoint as well as from a policy perspective (i.e., did the way certain words are use change after an event or a policy implementation?).
End-to-end Machine Learning pipeline for Named Entity Recognition in emails with basic implementation
- This project was inspired by a problem I had a chance to solve in my professional career, however, the problem presented here is different and this article does not contain any code and/or solutions used in the product.
- The solution presented here is a simplified one. Further steps required for making it closer to reliable production-ready service are discussed at the end of the article.
- The given material assumes the reader is familiar with the basics of Machine Learning and Software Engineering but is curious to know how one can make them work together.
What is keyword extraction?
Keyword extraction is the retrieval of keywords or key phrases from text documents. They are selected among phrases in the text document and characterise the document’s topic. In this article, I summarise the most commonly used methods that automatically extract keywords.
Methods that automatically extract keywords from the documents use heuristics to select the most used and significant words or phrases from the text document. I classify keyword extraction methods in the field named natural language processing, which is an important field in machine learning and artificial intelligence.
Keyword extractors are used to extract words (keywords) or groups of two or more words that create a phrase (key phrases).
Too lazy to scrape nlp data yourself? In this post, I’ll show you a quick way to scrape NLP datasets using Youtube and Python.
Continue reading: https://hackernoon.com/how-to-scrape-nlp-datasets-from-youtube?source=rss
… Read more...
We are going to build a simple sentiment analysis application. This app will get a sentence as user input, and return with a prediction of whether this sentence is positive, negative, or neutral.
Here’s how the end product will look:
You can use any Python IDE to build and deploy this app. I suggest using Atom, Visual Studio Code, or Eclipse. You won’t be able to use a Jupyter Notebook to build the web application. Jupyter Notebooks are built primarily for data analysis, and it isn’t feasible to run a web server with Jupyter.
Once you have a programming IDE set up, you will need to have the following libraries installed: Pandas, Nltk, and Flask.
One of the main core features of this package is the capability to tokenize Myanmar language text. At the time of this writing, it supports:
- Syllable-level tokenization (Burmese, Karen, Shan, Mon)
- Word-level tokenization (Burmese)
This tokenization is based on regular expression (regex). It supports Burmese, Karen, Shan and Mon languages. Call it as follows:
It will return a list of tokens (tokenized words).
On the other hand, word-level tokenization supports only Burmese. It is based on conditional random field (CRF) prediction. Call the tokenize function as usual and specify the
form parameter to
The output is slightly different from the syllable label depending on the input text.
NLP Text Preprocessing Methods
Deep Learning, particularly Natural Language Processing (NLP), has been gathering a huge interest nowadays. Some time ago, there was an NLP competition on Kaggle called Quora Question insincerity challenge. The competition is a text classification problem and it becomes easier to understand after working through the competition, as well as by going through the invaluable kernels put up by the Kaggle experts.
So, first let’s start with explaining a little more about the text classification problem in the competition.
Text classification is a common task in natural language processing, which transforms a sequence of a text of indefinite length into a category of text.
This guide goes through how we can use Natural Language Processing (NLP) and K-means in Python to automatically cluster unlabelled product names to quickly understand what kinds of products are in a data set.
This article is Part 2 and will cover: K-means Clustering, Assessing Cluster Quality and Finetuning.
If you haven’t already, please read Part 1 which covers: Preprocessing and Vectorisation.
Now that we have our word matrices, let’s get clustering.
This is the sexy part: clustering our word matrices.
K-means clustering allocates data points into discrete groups based on their similarity or proximity to each other.
gensim Python library makes it ridiculously simple to create an LDA topic model. The only bit of prep work we have to do is create a dictionary and corpus.
A dictionary is a mapping of word ids to words. To create our dictionary, we can create a built in
gensim.corpora.Dictionary object. From there, the
filter_extremes() method is essential in order to ensure that we get a desirable frequency and representation of tokens in our dictionary.
id2word = corpora.Dictionary(data_preprocessed)
id2word.filter_extremes(no_below=15, no_above=0.4, keep_n=80000)
filter_extremes() method takes 3 parameters. Let’s break down what those mean:
- filter out tokens that appear in less than 15 documents
- filter out tokens that appear in more than 40% of documents
- after the above two steps, keep only the first 80,000 most frequent tokens
A corpus is essentially a mapping of word ids to word frequencies.
In this blog, I have tried summarizing the paper Deep Natural Language Processing for LinkedIn Search Systems as per my understanding. Please feel free to comment your thoughts on the same!
This paper introduces a comprehensive study of applying deep Natural Language Processing techniques to five representative tasks for building efficient and robust search engines. Apart from this, the paper also tries to find out answers to 3 important questions that will help build and scale such systems in production environments, around latency, robustness, and effectiveness.
So without further ado, let’s dig into the search engine components.
Whether it is in computer vision, natural language processing or image generation, deep neural networks yield the state of the art. However, their cost in term of computational power, memory or energy consumption can be prohibitive, making some of them downright unaffordable for most limited hardware. Yet, many domains would benefit from neural networks, hence the need to reduce their cost while maintaining their performance.
That is the whole point of neural networks compression. This field counts multiple families of methods, such as quantization , factorization , distillation  or, and this will be the focus of this post, pruning.
Find out a name’s likely gender using Natural Language Processing in Tensorflow, Plotly Dash, and Heroku.
Choosing a name for your child is one of the most stressful decisions you’ll have to make as a new parent. Especially for a data-driven guy like me, having to decide on a name without any prior data about my child’s character and preferences is a nightmare come true!
Since my first name starts with “Marie,” I’ve gone through countless experiences of people addressing me as “Miss” over emails and text only to be disappointed to realize that I’m actually a guy when we finally meet or talk 😜.