Natural Language Processing Archives

Isha
April 08, 2021
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What Natural Language Processing Can Do For Your Business?

“Google, call Mom” – how much are you into a habit of asking your phone or another gadget to do something for you, in plain language? If you answered every time or very often, you’d totally understand the importance of Natural Language Processing technology in our lives. The rise in demand for better, advanced means to perform is one of the primary causes for technology to evolve at such a pace. So much so that computers can now understand what humans speak in their native language! Of course, this sort of technology wasn’t achieved overnight. The demand for human-to-machine communication got programmers, coders, and a whole lot of tech specialists to bring out their best. As humans, we may be able to speak and write English or any other plain language, but for a computer these languages are alien. The machine language or code it understands is largely incomprehensible to most people. NLP or Natural Language Processing is a branch of artificial intelligence that deals with the interaction between computers and humans using natural language. The goal of NLP is to read, decipher, analyze, and make sense of the human language in a valuable manner. Almost any industry one can think of has implemented NLP in its operations, so much so that the common people are used to taking its help in their daily lives. The effect of NLP can be easily noted with the rise in demand for NLP consulting firms or those organizations that provide end-to-end NLP services. Why is Natural Language Processing important? Everything we express, through any medium of communication be it verbal or written, carries an enormous amount of information. The way we talk, tone of the conversation, selection of words, or anything that compiles our speech, adds a type of information that can be interpreted and its value, extracted. NLP helps computers communicate with humans in their native language. It also makes it possible for computers to read a text, hear speech and interpret while determining which parts of the speech are important. Moreover, as machines, they have the ability to analyze more language-based data than humans in a consistent manner, without getting fatigued, and in an unbiased way. Considering the staggering amount of data that are produced every day be it in the medical industry or social media, automation of language processing will always be critical to analyze speech and data efficiently. Which techniques are implemented for Natural Language Processing? Haven’t all of us come across that moment when Alexa or Google replies about not being able to understand what we communicated? Sometimes the computer or device may fail to understand well leading to obscure results. In order to minimize the frequency of such results, there are two main techniques used to accomplish NLP tasks. This refers to how words are arranged in a sentence to make the best grammatical sense. The Process of NLP uses syntactic analysis to assess how the natural language assigns with grammatical rules. A few syntactic techniques that are used are… – Morphological segmentation: Divides words into individual units called morphemes. – Lemmatization: Works at reducing a word to its original form and grouping all the different forms of the word, together. – Word segmentation: This involves dividing a large piece of continuous text into equal, and distinct units. – POS tagging: Identifies the part of speech for every word. – Sentence breaks: Places sentence boundaries on a large piece of text. – Stemming: Involves striking off an inflected word to its root form. – Coreference resolution: The task of finding all expressions that refer to the same entity in a text. Coreference resolution is a very important aspect of NLP when it comes to natural language understanding tasks such as document summarization, question answering, and information extraction. – Stopwords removal: Stopwords are the most commonly used words in any language. When analyzing text data and building NLP models, these stopwords do not add much value to the meaning of the document, like, ‘a’, ‘the’, ‘is’, ‘on’ etc. NLP helps in stopwords removal for a text classification task so that more focus can be given to other words. Semantics basically involves the meaning that is conveyed by a text. It is one of those problematic aspects of NLP that hasn’t been resolved yet. It requires computer algorithms to understand the meaning and interpretation of words while structuring the sentences. Here’s what helps in a semantic analysis… – NER: Named entity recognition is where parts of a text are determined, identified, and classified as pre-set groups. Examples of such groups are names of people, events, locations, and so on. – Word sense disambiguation: Involves giving meaning to a word based on context. – Natural language generation: uses databases to derive semantic intentions and translate them into human or native language. Firms are using NLP for business benefits in multiple ways, some of which are… How is Natural Language Processing used in different industries? Typically, Natural Language Processing works in a particular way. A human talks to the machine through the voice input, the machine captures the audio input, audio to text conversion happens, the text data is processed by the AI, data to audio is converted and the machine responds to the user by playing the audio file. While NLP is considered one of the most difficult things in computer science and engineering, it’s not the work, but the nature of human language that makes it difficult. NLP makes use of algorithms to identify and extract the natural language rules in such a way that the unstructured language data is translated into a form for the computers to understand. And while this technology has been around for some time, it’s a fascinating extension of AI and has enormously changed how we live in this age of digital transformation. Here are some of the areas which have been widely using natural language processing in their operations… A significant challenge for healthcare systems is to utilize their data to its full

Sulaksh More
February 22, 2020
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Incredible evolution journey of NLP models !!

There have been ground breaking changes in the field of AI, where many a new algorithms and models are being introduced and worked up. The pace that we have been moving at, is sure to bring many more rapid developments in various industries with AI as major change maker. Here we are going to talk about the incredible evolution journey of NLP Models. While Google’s BERT and Transformer did set some amazing records, Facebook’s RoBERTa, which is based on BERT, surpassed many previous records and then Microsoft’s MT-DNN exceeds the benchmarks set by Google’s BERT almost of nine NLP tasks out of eleven. How Google’s Bert did: Google implemented two major strategies with BERT – Mask Language Model, where an amount of words was masked, kept hidden in the input and the BERT was to predict the hidden word. Refer the below example for better understanding. Referring to the above example, the masked words were kept hidden as part of training and model was to anticipate the words. For BERT, it was essential to understand the context of the sentence based on the unmasked words and predict the masked one, failing to produce expected words like Red and Good, would be a result of failed training techniques. Moving ahead, Next Sentence Prediction (NSP) was the second technique used with BERT, where BERT learned to establish a relationship between various sentences. Major task for NSP was to choose next sentence, based on the context of current sentence, to make proper pairs of sentences. Referring to the above sentences, BERT would have to choose the Sentence3 in order to complete the Sentence1 as its successor. Choosing Sentence2 instead of Sentece3 would result in failed training of the model. Both of the above techniques were used in training the BERT. Real Life examples of BERT can be seen with Gmail app, where replies are being suggested according to the mail, or when you start typing a simple sentence, further words to complete the sentence can be seen in Light Grey Fond. How were past models improved: Everything we have today is better than yesterday, but the tomorrow will demand many improvisations, “There’s always some room for Improvement”. When Google broke the records with BERT, it was exceptional but then Facebook decided to implement the same model of BERT, but with a slight change. The changes here were improved methods for training, with massively added amounts of data and a whole lot more of computation. What Facebook did was, simple carry forward the Language Masking Strategy of BERT but decided to replace the Next Sentence Prediction Strategy with Dynamic Masking. Masking: Static vs Dynamic: When Google fed its model, BERT, with massive amount of data with masked words, it was Static Masking, has it happened only at time of insertion. But what Facebook did was, tried to avoid masking same word multiple times and so training data was repeated 10 times and every next time, the masked word would be different, meaning the sentence would be same but the words masked would be different and this made RoBERTa quite exceptional. What’s with lesser parameters: Parameters, a very important part of training data, has to be accurate and useful for the model and must be in vast amount for the model to learn every possible scenario. NVIDIA, exceeded every past record for maximum parameters when they trained world’s largest Language Model named as Megatron, which is based on Google’s Transformer, with 8.3 BILLION Parameters. Amazingly, NVIDIA trained the model in 53 minutes and happily made it accessible for other major players like Microsoft and Facebook, to play with its State-of-the-Art Futuristic Language Understanding Model. BUT BUT BUT, what DistilBERT did was, stood up with almost matching results as BERT but with using almost half the number of parameters. DistilBERT meaning Distillated-BERT, released by Hugging Face uses only 66 million parameters while BERT base uses 110 million parameters. Along with Toyota Technological Institute, Google released a Lite version of BERT, ALBERT. While BERT xLarge uses 1.27 billion parameters, ALBERT xLarge uses only of 59 million parameters, now that’s reducing parameters to almost half. Smaller and Lighter compared to BERT, ALBERT might be BERT’s successor. Sharing Parameters is one of the most impressive strategy implemented with ALBERT, which works on hidden level of model. With Parameters Sharing, loss of accuracy happens but also reduces the number of parameters required. Google Again: Google, with Toyota brought out ALBERT, and as described above, it uses less parameters and implements a strategy to convert the words into numeric one-hot vector, which are later passed into an embedding space. But it is essential for embedding space to have same dimension as of the hidden layer and this was surpassed when ALBERT team factorised the embedding, meaning the earlier created word vectors were first projected into smaller dimension space and then pushed into higher one with the same dimension. What could be Next? Next is probably to harness the Language Intelligence that made English Language quite interesting for machines to understand and learn, to be implemented with various languages. Every language has its own roots and varies a lot with multiple factors, but possibility is to train next languages just as it was done with English Language. Many improvements are being made with added computation or by increasing data, but one factor that will be considered to be ground breaking in the field of AI is when models are efficiently being trained and improved with smaller amount of data and less computation. To talk to our NLP expert on how to use the NLP Models for your business contact us

Sulaksh More
February 06, 2020
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The Past, Present and the Future of Natural Language Processing?

Making our machines understand the language has made significant changes in the field of machine learning and has improvised the various Natural Language Processing models. But on the contrary, it was quite difficult for machines to understand the underlying meaning of a sentence and how it has its importance in a bunch of sentences, until Google published BERT. Let’s consider the following statements: Sushant was my friend. He was a good coder but lacked the idea of optimized code. When in need, he has always helped me. Humanly, this sentence has a clear meaning but quite difficult to understand for a computer. Natural Language Processing (NLP) has been a major player for training machines to understand and evaluate the meaning. But every Natural language processing (NLP) module, at some point lacked the ability to completely comprehend the underlying meaning of the sentences. In the above sample statement, every highlighted word points towards the person “Sushant”, for a model trained to find and evaluate the specific keywords in a sentence would fail to connect the dots here. Models are particularly trained to understand and evaluate the meaning of the words in one-after-one manner, which made the above mentioned sample quite out of scope. Now the need was, of something, that did not just understand the later part of the word but also the prior. Not just to connect the meaning with next word but to compare the meaning with last word too. Transformer by Google: The Transformer by Google, based on Novel Neural Network Architecture follows a self-attention mechanism and did surpassed recurrent and convolutional models for English language. Along with translating English to German and English to French, Transformer requires competitively less computation. Transformer performs small tasks over a sequence and applies self-attention method, which establishes a relationship between differently positioned words in a sentence. In the sample statement about Sushant, it is important to understand the normal word ‘he’ refers to Sushant himself and this establishes the ‘he-him-his’ relationship to the mentioned person in a single step. And then Google Introduces BERT: Until BERT by Google came in to picture, understanding the conversational queries was quite difficult. BERT stands for Bidirectional Encoder Representations and is a big leap in the field of Language Understanding. The word Bidirectional itself means functioning in two directions. It was amazing to see BERT exceed all previous models and become the unsupervised pre-training natural language processing. In practice, BERT was fed with word sequences with 15% of words masked, kept hidden. The aim was to train the model to predict, value of the masked words based on the words provided in the sequence, unmasked words. This method, known as Masked Language Modelling performs to anticipate the masked, hidden words out of sentence, based on context. One of the finest application of such improvised models are seen with search engines, to find particular meaning of the sentence and to provide matching results, greatly helps in filtering the required information. There was time when Google used to rely on keywords, specifically added in blog post or website content, but with BERT, Google steps ahead and will now interpret words, NOT JUST KEYWORDS. Google search has been implementing BERT, as improvised software for better user experience. But with advanced software we need to implement hardware with similar capacities and this is where latest Cloud TPU, Tensor Processing Unit by Google, comes in picture. While enhancing the user experience, Google’s BERT will affect your SEO content too. Currently, these changes are being made with English Language Search for Google U.S. But with aim to provide better result over the globe, Google will be implementing teachings of One Language to others, from English to rest. Consider the following sentences: That flower is a rose. That noise made him rose from his seat. If the machine is trained to understand and interpret the meaning of the sentence with one-by-one method, the word “rose” would be a point of conflict. On the contrary, with latest developments and thanks to google for open sourcing the BERT, the meaning of the word rose will now vary according to the context. The aim is not to interpret, how the flower is ‘rising’ or how the noise is making him into a ‘rose’, a flower. XLNET and ERNIE: Similar to Generative Pre-trained Transformer aka GPT and GPT-2, XLNET is BERT like Autoregressive language model, which predicts to next word based on context word’s backward and forward intent. Outperforming BERT and XLNET, Baidu has open sourced ERNIE Another Pre-Training Optimized Method for NLP by Facebook: Improvising what Google’s BERT offered, Facebook advanced with RoBERTa and DeBERTa NLP models. Using Bert’s Language Masking Strategy and structure, Facebook’s RoBERTa offered an improvised understanding for systems to anticipate the portion of text which was deliberately kept under surface. Implemented using PyTorch, FB’s RoBERTa focuses on improving a few key hyperparameters in BERT. Various Public News articles along with unannotated Natural language processing data sets were used in training RoBERTa. DeBERTa, Decoder-based Pre-Training is a dynamic NLP model based on BERT structure. It utilizes a dynamic masking pattern of pre-training and a larger model size than BERT and RoBERTa which allows it to better apprehend the context and meaning of the text. Overall, it ensures a more robust representation of input content. GPT 3 GPT3 or Generative Pretrained Transformer is a trending NLP model developed by OpenAI is an advanced language model for natural language processing. It functions upon the massive amount of text data containing 175 billion parameters trained upon the Common Crawl dataset. It performs a wide array of tasks such as language translation, question answering, summarization, and very viable human-like text generation as well. And then Microsoft Jumped in: Moving ahead, Microsoft’s MT-DNN, which stands for Multi-Task Deep Neural Network, transcends the BERT by Google. Microsoft’s NLP model is built on 2015’s proposed model but implements BERT’s Network architecture. Implementing Multitask Learning (MTL) along with Language Model Pretrainig of BERT, Microsoft has exceeded previous records. Achieving

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