Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language, in particular how to program computers to process and analyze large amounts of natural language data.
This is series 2 of the introduction to key capabilities of NLP technologies. Here is the link to Series 1 of this article. With recent advances in Artificial intelligence technologies, computers have become very adept at reading, understanding, and interpreting human language. Here a few additional NLP capabilities that have made that possible.
Clustering in general refers to the grouping of similar data together. Text clustering is a technique used to group text or documents based on similarities in content. It can be used to group similar documents (such as news articles, tweets, and social media posts), analyze them, and discover important but hidden subjects. Text classification as we discussed before also puts objects in a group, but the major difference between clustering and classification is that classification is a supervised method whereas clustering in an unsupervised method. All objects/data within clustering are new and no the resultant groups are unknown. This method is heavily used to identify key topics, patterns in large data sets as the first step to classification.
Text Summarization refers to the technique of producing a concise summary of long pieces of text while preserving key information, content and overall meaning. There are many reasons and uses for a summary of a larger document. One example that might come readily to mind is to create a concise summary of a long news article, but there are many more cases of text summaries that we may come across every day. There are two different approaches that are used for text summarization: Extractive Summarization, Abstractive Summarization. Extractive Summarization identifies the important sentences or phrases from the original text and extracts only those from the text. Abstractive Summarization generates new sentences from the original text.
Relationship extraction refers to the technique of extracting semantic relationships from a text. Relationship Extraction products attributes and relations for entities in a sentence. For example: given the sentence “John was born in Fairfax, Virginia” a relation classifier aims at predicting the relation of “bornInCity”. Relation Extraction is the key component for building relation knowledge graphs, and it is of crucial significance to natural language processing applications such as structured search, sentiment analysis, question answering, and summarization.
These techniques combined with techniques discussed in series 1, can provide tools to create value from the deluge of unstructured data found within government agencies and other organizations. There is much to be learned from the potential of AI and, in particular, its ability to analyze masses of unstructured data
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Organizations often ask us, “How well is the AI model is doing?” Or “How do I measure its performance?”, we often respond with “Performance of the AI model is based on what the F1 score of the model is” and we will get a puzzled look on everyones face or asking “what is an F1 score?” So here I am going to attempt to explain F1 score in an easily understandable way:
Definition of F1 score:
F1 score (also F-score or F-measure) is a measure of a test’s accuracy. It considers both the precision (p) and the recall (r) of the test to compute the score (as per wikipedia)
Accuracy is how most people tend to think about it when it comes to measuring performance (Ex: How accurate is the model predicting etc.?). But accuracy is not a true measure of AI models performance. Accuracy only measures the number of correctly predicted values among the total predicted value. Although it is a good measure of performance it is not complete and does not work when the cost of false negatives is high. Ex: Lets assume we are using an AI model to predict cancer cells, after training, the model is fed with 100 samples that have cancer and the model identifies 90 samples as having cancer. That 90% accuracy, which sounds pretty high. But the cost of not identifying 10 samples is very costly. Therefore accuracy is not always the best measure.
So to explain it further lets consider this table:
True Positive is an outcome where the model correctly predicts the positive class. Ex: when cancer is present and the model predicts cancer.
False Positive is an outcome where the model incorrectly predicts the positive class. Ex: when cancer is not present and the model predicts cancer.
False Negative is an outcome where the model incorrectly predicts the negative class. Ex: when cancer is present and the model predicts no cancer.
True Negative is an outcome where the model correctly predicts the negative class. Ex: when cancer is not present and the model predicts no cancer.
As explained by the definition, the F1 score is a combination of Precision and Recall.
Precision is the number of True Positives divided by the number of True Positives and False Positives. Precision can be thought of as a measure of exactness. Therefore, low precision will indicate a large number of False Positives.
Recall is the number of True Positives divided by the number of True Positives and the number of False Negatives. Recall can be thought of as a measure of completeness. Therefore, low recall indicates a large number of False Negatives.
Now, F1 score is the harmonic mean of Precision and Recall and gives a much better measure of the model.
F1 Score = 2*((precision*recall)/(precision+recall)).
A good F1 score means that you have low false positives and low false negatives. Accuracy is used when the True Positives and True negatives are more important while F1-score is used when the False Negatives and False Positives are crucial
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Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language, in particular how to program computers to process and analyze large amounts of natural language data.
In my previous articles, I have addressed some specific topics on NLP like Text Classification, Natural Language Search, etc. Here I want to give a quick introduction to a few key technical capabilities of Natural Language Processing.With recent advances in Artificial intelligence technologies, computers have become very adept at reading, understanding and interpreting human language. Let’s look a few key capabilities of NLP. These are by no means a comprehensive list of all NLP capabilities.
Named Entity Recognition (NER):
NER is one of the first steps towards information extraction from large unstructured data. NER seeks to locate and extract named entities that are present in a text into pre-defined categories like persons, countries, organizations etc. This helps with answering many questions such as:
– How many mentions of an organization is in this article?
– Were there any specific products mentioned in a customer review?
This technology will enable organizations to extract individual entities from documents, social media, knowledge base etc. The better defined and trained the ontologies are, the more efficient the outcome will be.
Topic Modeling is a type of statistical modeling for discovering abstract topics from a large document set. It is frequently used to discover hidden semantic structures in a textual body. It is different from traditional classification in that, it is an unsupervised method of extract main topics. This technique is used in the initial exploring phase to find what the common topics are in the data. Once you discover the topics, you can use language in those topics to create categories. One of the popular methods used for Topic Modeling is Latent Dirichlet Allocation (LDA). LDA builds a topic per document model and words per topic model, modeled as Dirichlet distributions. You can read more about LDA here: http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf
Text classification (a.k.a text categorization or text tagging) is the task of assigning a set of predefined categories to free-text. This is a supervised training methodology as opposed to Topic Modeling above. I have written in detail about text classification here:
Information Extraction is used to automatically find meaningful information in unstructured text. Information extraction (IE) distills structured data or knowledge from the unstructured text by identifying references to named entities as well as stated relationships between such entities. IE systems can be used to directly extricate abstract knowledge from a text corpus or to extract concrete data from a set of documents which can then be further analyzed with traditional data-mining techniques to discover more general patterns.
Sentiment analysis is the automated process of understanding an opinion about a given subject from written or spoken language. Sentiment analysis decodes the meaning behind human language, allowing organizations to analyze and interpret comments on social media platforms, documents, news articles, websites, and other venues for public comment.
Within government agencies and organizations, there is a deluge of unstructured data both in analog and digital form. NLP can provide the needed tools to move the needle forward in providing better visibility and knowledge into unstructured data. NLP can be utilized in many ways. To name a few: Analyze public data like Social Media, reviews, comments, etc., Get visibility into the organizational knowledge base, provide predictive capabilities, enhance citizen services, etc. There is much to be learned from the potential of AI and, in particular, its ability to analyze masses of unstructured data. It is time now for agencies and organizations to take action to harness the power of NLP to stay ahead.
NLS: Natural Language Search is a search using everyday spoken language, such as English. Using this type of search, you can ask a database a question or type in a descriptive sentence that describes your question.
Though asking questions in a more natural way (ex: What is the population of England as of 2018? Or who was the 44th president of America?) has only recently come into its own in the field, natural language search engines have been around almost as long as web search.
Remember Ask Jeeves? The 1996 search engine encouraged its users to phrase their search queries in the form of a question, to be “answered” by a virtual suited butler. Ask Jeeves was actually ahead of its time in this regard when other search engines like Google and Yahoo were having greater success with keyword-based search. In 2010, Ask Jeeves finally bowed to the pressure from its competition and outsourced its search technology. Ironically, had Ask Jeeves been founded about fifteen years later, it most likely would have been at the cutting edge of natural language search, ahead of the very search engines that squeezed it out.
Today the advent of smart speakers and mobile phones has brought voice-based search and conversational search to the forefront. Advances in NLP (Natural Language Processing) technology has made this possible not just for search giants like Google and Microsoft but also for enterprises to search their internal knowledge base and domain data utilizing artificial intelligence (AI).
Anyone who has used an enterprise application will be familiar with multiple criteria search boxes (like below).
These searches are cumbersome and perform search on structured data stored in the database. As more and more data are now being stored in NOSQL databases and in unstructured text across documents and folders, the need for search across these data sources becomes essential. A simple Boolean search (a simple search for keywords) does not provide the extensive search capabilities necessary to review complex relationships between topics, issues, new terms, and languages. It’s 2019 – searches need to, and can, go beyond simple keyword matching.
An effective search will need to include indexed data that is extracted from a knowledge base using AI technologies like NER (Named Entity Recognition), OpenIE (Information Extraction), key phrase extraction, Text classification, STS (Semantic Text Similarity) and Text Clustering. The data extracted from the above processes will need to be classified, indexed and stored using a multi-label classification technology. This classification methodology will populate the database with knowledge, links, and relations between all data sources. Data, along with structured data stored through transactional operations, will then be used to train the AI models in understanding entities, relations, and common phrases. And that’s where natural language search comes in. NLS provides an efficient way to search data stored in structured or unstructured formats (like scanned pdf documents etc.) thereby providing a comprehensive search across all data.
You can think about it like this: Take a query from an engineer like “Show me Oil Leaks on Main Engine for TAO class in the last 2 years.” The ships that belong to a specific class are stored in a structured database where specific oil leak failures are in repair text as unstructured information. Using AI models to extract this information and index it will provide the ability to answer this query with much more accuracy and greater speed – digging through documents is not going to be the best use of your time. In this case, the query will be analyzed by the model to identify entities that fit the query and dynamically build a solution query to retrieve information from the appropriate data store.
Artificial intelligence has enabled the possibility of implementing NLS within enterprises. This will increase the efficiency and effectiveness of search while reducing the time to perform searches.
Microservice is a software development technique for developing an application as a suite of small, independently deployable services built around specific business capabilities. Microservices is the idea of breaking down big, monolithic application into a collection of smaller, independent applications.
Why should machine learning models be deployed as microservices?
This is an empirical era for machine learning as successful as deep learning has been, our level of understanding of why it works so well is still lacking. Machine learning engineers need to explore and experiment with different models before they settle on a model that works for their specific use case. Once a model is developed there are inherent advantages to deploying machine learning models in a container and serving it as microservices.
Here are a few reasons to why it makes sense to deploy AI models as microservices:
- Microservices are smaller and are easier to understand as opposed to large monolithic application. Microservices are focused around business functions and so it makes it simpler to deploy a single specific function without worrying about all the other business functions.
- Each service can be deployed independently of each other. This also allows for independent scaling of each service as opposed to the entire application. This is a much efficient way of using computing capabilities and will achieve a balance of computing resource allocation. Microservices deployed in a container architecture allows for further efficiency in scaling.
- Because each service is focused on a specific business function, it makes it easier for development resource(s) to understand a small set of function rather than the entire application.
- Making the model as a service provides the ability to expose the services to both internal and external applications without having to move the code. The ability to access data using well-defined interfaces. Containers have mechanisms built in for external and distributed data access, so you can leverage common data-oriented interfaces that support many data models.
- Each team also has the luxury of choosing whatever languages and tools they want for their job without affecting anyone else. It eliminates vendor or technology lock-in. By deploying Machine Learning models as Microservices with API endpoints, the data scientists and AI programmers can write models in whatever framework- Tensorflow, PyTorch or Keras, without worrying about the technology stack compatibility.
- Microservices allow for deployment of new versions in parallel and independent of other services. Developers can work in parallel and get changes to production independently and faster. Enables the continuous delivery and deployment of large, complex Machine Learning applications. With production ready frameworks like Tensorflow Serving, the management of versions of a model become very easy.
- Deploy to any environment local, private or public cloud. If there are data privacy concerns on deploying AI models on the cloud, creating individual models as containers allow for deployment of AI models in the local environment.
- In most AI projects, there will be several AI models that will be developed to do specific functions (ex: A model to do Named Entity Recognition, Model to do Information Extraction etc.). Microservices allows for these models to be independently developed, updated and deployed.
Now let’s talk about some technologies that help with deploying models as microservices. Here we want to focus on two prominent technologies that allow for this to happen.
Docker helps you create and deploy microservices within containers. It’s an open source collection of tools that help you build and run any app, anywhere. Here is a great resource on Docker Basics. There are plenty of resources out on the internet for getting started with Docker as well.
When it comes to deploying microservices as containers, another aspect that should be kept in mind is the management of individual containers. If you want to run multiple containers across multiple machines – which you’ll need to do if you’re using microservices, you will need to manage these efficiently. To start the right containers at the right time, make them talk to each other, handle storage and memory considerations, and deal with failed containers or hardware. Doing all of this manually would be a nightmare and hence having a tool like Kubernetes is critical. Kubernetes is an open source container orchestration platform, allowing large numbers of containers to work together in harmony, reducing operational burden.
When used together, both Docker and Kubernetes are great tools for developing a modern AI cloud architecture.
As a follow up to my earlier LinkedIn Post of Google’s BERT model on NLP, I am writing this to explain further about BERT and the results of our experiment.
In a recent blog post, Google announced they have open-sourced BERT, their state-of-the-art training technique for natural language processing (NLP) applications. The paper released (https://arxiv.org/abs/1810.04805) along with the blog is receiving accolades from across the machine learning community. This is because BERT broke several records for how well models can handle language-based tasks and more accurately NLP tasks.
Here are a few highlights that make BERT unique and powerful:
- BERT stands for Bidirectional Encoder Representations from Transformers. As the name suggests, it uses Bidirectional encoder that allows it to access context from both past and future directions, and unsupervised, meaning it can ingest data that’s neither classified nor labeled. This is unique because previous models looked at a text sequence either from left to right or combined left-to-right and right-to-left training. This method is opposed to conventional NLP models such as word2vec and GloVe, which generate a single, context-free word embedding (a mathematical representation of a word) for each word in their vocabularies.
- BERT uses Google Transformer, an open source neural network architecture based on a self-attention mechanism that’s optimized for NLP. The transformer method has been gaining popularity due to its training efficiency and superior performance in capturing long-distance dependencies compared to a recurrent neural network (RNN) architecture. The transformer uses attention (https://bit.ly/2AzmocB) to boost the speed with which these models can be trained.As opposed to directional models, which read the text input sequentially (left-to-right or right-to-left), the Transformer encoder reads the entire sequence of words at once. This characteristic allows the model to learn the context of a word based on all of its surroundings (left and right of the word).
- In the pre-training process, researchers used a masking approach to prevent words that’s being predicted to indirectly “see itself” in a multi-layer model. A certain percentage (10-15%) of the input tokens were masked to train the deep bidirectional representation. This method is referred to as a Masked Language Model (MLM).
- BERT builds upon recent work in pre-training contextual representations — including Semi-supervised Sequence Learning, Generative Pre-Training, ELMo, and ULMFit. BERT is pre-trained on 40 epochs over a 3.3 billion word corpus, including BooksCorpus (800 million words) and English Wikipedia (2.5 billion words). BERT has 24 Transformer blocks, 1024 hidden layers, and 340M parameters. The model runs on cloud TPUs (https://cloud.google.com/tpu/docs/tpus) for training which enables quick experimentation, debug and to tweak the model
- It enables developers to train a “state-of-the-art” NLP model in 30 minutes on a single Cloud TPU (tensor processing unit, Google’s cloud-hosted accelerator hardware) or a few hours on a single graphics processing unit.
These are just a few highlights on what makes BERT the best NLP model so far.
To evaluate the performance of BERT, we compared BERT to IBM Watson based NER. The test was performed against the same set of annotated large unstructured documents. The model created using BERT and IBM Watson was applied to the annotated large unstructured documents. Below table shows the results we achieved:
Based on our comparison and what we have seen so far, it is fairly clear that BERT is a breakthrough and a milestone in the use of Machine Learning for Natural Language Processing.
Deep Learning is a subset of machine learning that allows machines to do tasks that typically require human like intelligence. The inspiration for deep learning comes from neuroscience, if you look at the architecture of Deep Learning Neural Networks, they are connected in a fundamental way that mirrors the brain. Deep-learning networks are distinguished from the more commonplace neural networks by their depth; that is, the number of node layers through which data passes in a multistep process.
Earlier versions of neural networks were shallow, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as “deep” learning. So deep as strictly defined means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer’s output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
Let’s take a simple example of recognizing hand written numbers from 1 – 10. If 10 people wrote the numbers, the numbers will look very different from each person. For a human brain, it is fairly easy to identify these numbers. For a traditional machine it is impossible to detect and hence Neural Networks are used to mimic the way, neurons in the brain interact. These multiple hidden layers allow a computer to determine the nature of a handwritten digit by providing a way for the neural network to build a rough hierarchy of different features that make up the handwritten digit.
For instance, if the input is an array of values representing the individual pixels in the image of the handwritten figure, the next layer might combine these pixels into lines and shapes, the next layer combines those shapes into distinct features like the loops in an 8 or upper triangle in a 4, and so on. By building a picture of these features, neural networks can determine with a very high level of accuracy the number that corresponds to a handwritten digit. Additionally, the model will learn which links between neurons are critical in making successful predictions during training. Over the course of several training cycles, and with the help of occasional manual tuning, the network will continue to learn and generate better predictions until it reaches desired accuracy.
Thus, Deep learning allows machines to solve complex problems even when using a data set that is very diverse, unstructured and inter-connected. Deep learning networks excel at dealing with vast amount of disparate data. In fact, the larger the amount of data the more efficient Deep learning becomes and the more deep learning algorithms learn, the better they perform.
Few additional links on this topic:
MIT Technology Review: https://www.technologyreview.com/s/513696/deep-learning/
Cambridge Univerisity paper: https://bit.ly/2Fbbrlr