Month: December 2020

Categories
Natural Language Processing

Natural Language Processing

Datalabeler_natural-language_processing

Natural Language Processing (NLP) is a branch of AI that helps machines understand natural language and enables interaction between machines and humans using the natural language. NLP helps the machines to read, understand and manipulating human language in a valuable way.

How NLP Works?

The first step in NLP depends on the type of application being developed. A voice-based system for instance involves the use of Hidden Markov Models (HMM)for converting words into text. HMM utilizes math models for interpreting natural language and converting it into text. The NLP system then processes this text further.

The next step involves understanding the context and language by dividing each part of a sentence into parts of speech. The algorithm that performs this step is trained on grammar rules. These algorithms use statistical Machine Learning to help NLP system to interpret the word context.

In scenarios like above where speech-to-text is involved, the NLP system avoids the first step using HMM and interprets the words based on grammar rules using algorithms.

NLP uses two methods mainly to interpret the human language; Semantic and Syntax analysis.

Syntax involves arrangement of words using grammar rules. This method enables the NLP system to use grammar rules and extract meaning from language.

Syntax Techniques

  • Parsing – checking sentences for grammar
  • Sentence breaking – placing boundaries around large texts
  • Word segmentation – divide larger texts into smaller fragments
  • Morphological segmentation – grouping of words
  • Stemming – Use inflection to convert words to its root forms

Extracting meaning from the text forms the crux of Semantic Analysis. The NLP system utilizes semantic analysis to understand the meaning and review the structure of a sentence for logically interpreting the human language.

Semantic Techniques

  • Sense disambiguation – using context to derive word meaning
  • Named Entity Recognition –  divides words into groups as per the category
  • Natural Language Generation –  extracts hidden semantics within words using a database

Technical Approaches for Developing NLP Systems

To develop an NLP system, two main technical approaches are used. They are Machine Learning and Rules-based methods

ML-based method uses algorithms that has the ability to interpret natural language based on previous encounters. In this method, text annotation services are used to train the ML algorithms on how to co-relate an input with its respective output. When you consider the previous example of Sentiment Analysis, an algorithm is specifically created for the automatic classification of reviews into positive, negative or neutral. The algorithms undergo training to accomplish the task by leveraging human labeled text data and to predict for unseen data without manual intervention.

Rules-based method applies linguistic rules to text. Each rule has a prediction and an antecedent. When performing sentiment analysis on product reviews for instance, it lists out the positive and negative words. Each review is analyzed to get the count of positive and negative words that in-turn helps to determine the sentiment of the overall text.

NLP Use Cases

Email Assistants

NLP has been used for everyday activities in some form or the other like auto-complete, grammar, spell-check and auto-correct. Email filters also use NLP to keep the spam emails away from the inbox.

Chatbots

NLP is utilized for training chatbots on specific behaviour and to enhance their performance before deployment. NLP algorithms enable chatbots to answer customer queries. They help the chatbots to interpret the meaning behind a query raised by customer and answer without human intervention in real-time.

Sentiment Analysis

Sentiment analysis is a common application of NLP that helps to determine the positive or negative polarity of a text. It empowers businesses to get customer views on their services or products. It is mainly used for categorizing product or company reviews and collect customers’ opinions from their social media posts or comments.

NLP requires the help of ML/DL algorithms to perform this task and also to perform back-end computation and data analytics for understanding huge data volumes.

About Data Labeler

Data Labeler specializes in providing best-in-class labeled datasets that help to power Machine Learning algorithms for Computer Vision projects. Contact us to get high-quality labeled datasets for AI applications.

Categories
Machine Learning

Top 5 Machine Learning Trends to Watch in 2021

Datalabeler_machine learning trends for 2021

Machine learning is going to revolutionize the industries in the coming years, in 2020 we have seen tremendous growth in the Machine learning and AI technologies. In 2021 machine learning will drive many business including medicine, health, E-commerce, agriculture and others. Here we are going to present you the machine learning trends for 2021 that will shape the industries in this year.

Increasing usage of Machine Learning

As per a research study, 77% of the devices that are in use presently utilize ML in some form or other. From virtual personal assistants like Siri, Alexa & Google to online transportation networks that estimate the price of the ride, email spam and malware filters, and social media platforms like Facebook that uses facial recognition to help recognize a friend instantly, Machine Learning has been leveraged by organizations and for day-to-day activities. The usage of ML will continue to increase in 2021.

Hyperautomation

Hyperautomation, a trend picked by Gartner refers to the possibility of automating each and every process within an organization. Being the next major phase of digital transformation, Hyperautomation can be used to automate even the legacy processes. Being one of the key components of Hyparautomation, ML helps to create automated business processes that can adapt and react to changing conditions and circumstances. With the current pandemic looking to continue into the next year, digital transformation powered by Hyperautomation seems to be the way forward for many businesses.

Intersection of ML and IoT

AI/ML and IoT need each other to flourish. ML algorithms require more data to learn, adapt and operate efficiently whereas IoT devices need to become more smart and secure. IoT devices provide the data required to train machines while integrating ML algorithms into IoT devices makes them smarter and more secure. We will continue to see the culmination of ML and IoT in many devices in 2021.

Reinforcement Learning

Reinforcement Learning is a technique that involves the use of deep learning algorithms that can learn from its own experiences. The machines perform on the basis of conditions set to perform a specific activity. Reinforcement Learning enables machines to find the best possible path it should take to achieve the ultimate objective.

Business Forecasting and Analysis

Whether you want to predict the trend in financial markets or forecast peak consumption in electricity during the day, time series is the best data science technique to leverage. Time Series Forecasting makes use of the best fitting model essential to predicting the future observation based on complex processing current and previous data.

Machine learning proved to be the most effective in capturing the patterns in the sequence of both structured and unstructured data and its further analysis for accurate predictions.  

About Data Labeler

At Data Labeler, we provide fully managed data labeling services and specialize in the production of high-volume and best-in-class training datasets for AI and ML initiatives. Reach out to us at sales@datalabeler.com for high-quality data labeling services.

Categories
Artificial Intelligence

AI for Online Content Moderation

Datalabeler_ai_online_content_moderation

More and more people are exploring various online platforms that allow for uploading user-generated content. Every day millions are uploading content either in the form of blogs, images, or videos to online platforms. Some are even making a living out of user-generated content and have made online platforms an integral part of their lives. But unlike the moderation rules associated with traditional offline media, the content on the internet is not subject to any editorial controls. This means users can post content that is cruel and insensitive to others especially children, pornographic content, or the ones that promote violence or terrorism.

There has arisen a need for moderation of content that goes live every other day over the internet. And the onus falls on the online platforms to review the content and flag and remove the inappropriate ones. These platforms are employing thousands of content moderators to vet any new content that is uploaded online. Globally, more than 100,000 people are moderating online content. Facebook for instance has employed 7500 moderators who moderate the content uploaded on their platform based on the rules set by the company.

What is the need for AI in Content Moderation?

The pace at which the user-generated content has been uploaded to the online platforms has made it difficult to identify and remove harmful content using the traditional human-based moderation. AI-based automation systems can assist humans in online content moderation and offer the scale and speed required to match the pace at which online content has been uploaded. This has been possible with the recent advancements in AI along with the availability of data and low-cost computational power needed to create new and improved algorithms.

AI-based moderation systems follow two approaches – content-based and context-based.

Content-based moderation systems can review text, image as well as videos. Named Entity Recognition, an important technique in Natural Language Processing is used for recognizing harmful content such as fake news, hate speech, harassment, etc. While sentiment analysis is used for classifying and labeling content based on the level of emotions involved. Semantic Segmentation, object detection – techniques of computer vision are used for analyzing images and videos.

Context-based moderation involves making the AI learn to understand the context or in simpler terms reading between the lines from various sources.

AI-Based Online Content Moderation Challenges

AI is aiding humans in online content moderation and helping to improve the pace at which content is moderated daily. But still, there are certain challenges that the machines have to overcome to perform efficiently and accurately in the long run.

There is a broad range of content that can be classified as harmful content ranging from child abuse content to spam, insensitive, violent and graphic content, extreme content, hate speech, and others. Some of these can be identified from the content alone while most of it requires the need to understand the context. A wide range of factors such as cultural, societal, political, and historical factors play a role in understanding the context and these contextual considerations vary as per the law of the land and what societies deem as acceptable. So, interpreting the context consistently is a challenge for AI-based systems.

Role of humans in training AI on content moderation

Since context plays an important role in moderating the content online, the role of training the AI-based system to read between the lines has fallen on humans. The rise of human data labelers has aided in the development of AI-based automated content moderation systems. Humans curate and organize the data as part of the data labeling process. They will first comb through the data and label what is appropriate and flag that’s inappropriate content. This helps to train the machines to recognize harmful content and process and moderate billions of user-generated content on online platforms. For AI to moderate content effectively, a mix of human data labelers and moderators is the need of the hour.

This is where data labeling companies like Data Labeler come into the picture. With 1000+ human data labelers working around the clock, we provide the labeled data to help train your AI-based systems for content & context-based moderation. Our team of data labelers will label the data as per your set specific guidelines and objectives to meet your company’s standards and policies. Contact us now for high-quality training datasets required for developing contextually aware AI-based moderation systems.

Categories
Deep Learning

Attention Mechanism in Deep Learning

Datalabeler_deeplearning_mechanism

The introduction of Attention Mechanism has revolutionized the way we work with deep learning models. It is one of the most valuable developments that has given rise to many recent advancements in Natural Language Processing like the Transformer model and Google’s BERT. In this blog, we will explore the concepts behind Attention, its type, and applications in Transformers.

What is Attention?

Attention generally refers to the process of selectively focusing on a specific thing or a topic while ignoring all others. The Attention Mechanism in Deep Learning is also based on a similar concept where it selectively focuses on certain factors during data processing while ignoring the remaining factors. It is the main component of a network’s architecture that helps to manage and measure the quantity of interdependence between the input and output elements and within the input elements.

Why Attention is better than the standard sequence-to-sequence model?

The drawback of the seq2seq models was its inability to process long input sequences accurately. This is due to its limitation of considering only the last state of the encoder RNN as the context vector for the decoder. Attention mechanism was introduced as a solution to overcome this problem. During the process of decoding, it retains and uses all the hidden states of the encoder RNN and maps the output of the decoder to all the hidden states of the input sequence.

Types of Attention Models

The attention models can be categorized into two major types: Bahdanau Attention and Luong Attention. The major differences between these models lie in their computations and architecture while the underlying principles remain the same.

Bahdanau Attention

This model is also called as an Additive model and was proposed by Dzmitry Bahdanau in one of his papers that was aimed at improving the seq2seq model in Machine Learning tasks. It attempted to align the decoder with the right input sentences and then implementing the Attention mechanism.

Here’s how the attention mechanism was implemented in Bahdanau’s paper:

  1. The encoder creates hidden states for each element of the input sequence
  2. Alignment scores are calculated between each of the encoder’s hidden states and the previous decoder hidden state
  3. The alignment scores of each encoder hidden state are combined and converted into a single vector post which it is softmaxed
  4. A context vector is created by multiplying the encoder hidden states and their alignment scores
  5. The new output is produced by concatenating the context vector with the previous decoder output and fed along with the previous decoder hidden state into the decoder RNN for a particular time step
  6. The steps from 2 to 5 repeat itself for each of the decoder’s time step until the output is beyond the specified max length or a token is generated.

Luong Attention

This type is also called Multiplicative Attention and was built on top of the Bahdanau Attention. It was proposed by Thang Luong. The main differences between the two lie in their ability to calculate the alignment scores and the stage at which the Attention mechanism is introduced in the decoder.

Here’s how the attention mechanism was implemented in Luong’s paper:

  1. The encoder creates hidden states for each element of the input sequence
  2. A new hidden state is created for a particular time step by passing the previous decoder output along with its hidden state
  3. Alignment scores are calculated using the encoder hidden states and the newly created decoder hidden state
  4. A single vector is created by combining alignment scores for each encoder hidden state which is then softmaxed
  5. A context vector is generated by multiplying the encoder hidden states and their alignment scores
  6. The new output is produced when the decoder hidden state created in step 2 is concatenated with the context vector
  7. The steps from 2 to 6 repeat itself for each of the decoder’s time step until the output is beyond the specified max length or a token is generated.

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