Tag Archives: natural language processing

Contextualized Lexical Embeddings: The Key to Accurate and Efficient NLP

12 Friday May 2023

Posted by in AI, ChatGPT

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The field of Natural Language Processing (NLP) has made remarkable progress in recent years, enabling machines to interpret, comprehend, and manipulate human language. One of the most promising developments in this field is the emergence of contextualized lexical embeddings.

Traditional word embeddings represent words as fixed vectors, ignoring the surrounding context. However, this approach falls short since words can have multiple meanings depending on the context. For example, the word “crane” can refer to a bird, a machine used for lifting heavy objects, or Origami.

Contextualized lexical embeddings overcome this limitation by considering the surrounding words and phrases when representing a particular word as a vector. This allows machines to understand language in context, leading to more accurate and efficient NLP solutions.

Creating contextualized lexical embeddings involves training deep learning models on large amounts of text data. By doing so, the same word can have different vector representations based on context. Take sentiment analysis, for instance, which involves determining the emotional tone of a piece of text. By analyzing the surrounding context, a machine learning model can better understand the sentiment behind a particular piece of text. This is particularly vital for social media platforms and customer service interactions, where sentiment analysis can help companies measure customer satisfaction.

For example, consider a customer review for a product: “I love Apple products, but their customer service is terrible.” Without contextualized embeddings, an NLP model may struggle to classify the overall sentiment of the sentence. However, with contextualized embeddings, the model can take into account the context of each word and accurately determine the overall sentiment of the sentence to be negative towards the customer service and positive towards the product.

Another example of sentiment analysis is a review for a smartphone: “I’m really impressed with this new smartphone. It has great features and is easy to use.” Using contextualized lexical embeddings, the model can understand the context and determine that the sentiment is not just positive, but also includes a sense of enthusiasm. This is valuable information for companies looking to improve their product offerings.

Although the use of contextualized lexical embeddings is still in its early stages, it has already shown great promise in improving NLP solutions. As we continue to refine and improve these techniques, we can expect even more impressive advancements in the field of NLP. By incorporating contextual information into word embeddings, we can improve the accuracy and efficiency of NLP solutions, paving the way for new possibilities in AI and machine learning applications.

ChatGPT 101: Code Generation

25 Tuesday Apr 2023

Posted by in ChatGPT, Tutorial

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Welcome back to the ChatGPT 101 series! In the last post, I introduced readers to OpenAI’s language model called ChatGPT, which uses machine learning algorithms to generate human-like text. The post highlights the potential applications of ChatGPT in code generation and translation, including generating code based on a prompt and translating code between programming languages. In this post, we’ll focus on using ChatGPT for code generation.

Code Generation with ChatGPT:

ChatGPT can generate code in a variety of programming languages, including Python, JavaScript, and PowerShell. Let’s take a look at an example of using ChatGPT to generate a PowerShell script to connect to a remote server and troubleshoot a TCP/IP routing issue, all using natural language.

Prompt:
Create a PowerShell script that will connect to a remote server named [TARGET-1] and then diagnose and troubleshoot any TCP/IP routing issues that may exist between [TARGET-1] and another server named [TARGET-2].

Response:

$targetServer = "TARGET-1"
$username = "domain\username"
$password = "password"

$securePassword = ConvertTo-SecureString $password -AsPlainText -Force
$cred = New-Object System.Management.Automation.PSCredential ($username, $securePassword)

Enter-PSSession -ComputerName $targetServer -Credential $cred

$targetServerIP = Resolve-DnsName -Name $targetServer | Select-Object -ExpandProperty IPAddress
$target2ServerIP = Resolve-DnsName -Name "TARGET-2" | Select-Object -ExpandProperty IPAddress

Test-NetConnection -ComputerName $target2ServerIP -TraceRoute -InformationLevel Detailed

ChatGPT is smart enough to include in the script a prompt for the credentials needed to connect to [TARGET-1], then establishes a PowerShell session with that remote server. It then uses the Resolve-DnsName cmdlet to resolve the IP addresses of both [TARGET-1] and [TARGET-2]. Finally, it runs the Test-NetConnection cmdlet with the -TraceRoute parameter to diagnose any TCP/IP routing issues between [TARGET-1] and [TARGET-2].

Conclusion:

We’ve seen how ChatGPT’s language model can be leveraged to generate complex and useful PowerShell scripts, such as the one we created to connect to a remote server and troubleshoot a TCP/IP routing issue. What’s impressive is not just the syntax and logic of the code itself, but also the ability of the model to understand the context of the problem and prompt for missing information, like the credentials required for remote access. This shows the power of natural language processing and machine learning, and how it can augment the work of system administrators, enabling them to focus on higher-level tasks and delegate some of the more mundane and time-consuming ones to AI-powered tools like ChatGPT.

So if you’re a sysadmin looking for ways to streamline your workflow and automate repetitive tasks, don’t hesitate to give ChatGPT a try. You may be surprised by the quality and efficiency of the code it generates, and the intelligence it demonstrates in understanding your needs and requirements.

In the next post, we will cover how to use ChatGPT for code translation, such as rewriting PowerBuilder code into a .NET app. Stay tuned!