Do you ever wonder how Alexa understands what youâre saying?
Like when you say, "Alexa, turn on my reading lights." How does she take the sounds you're speaking, turn them into words, and reply so effortlessly?
"Sure. Iâve turned on the reading lights in your current room."
"Alexa, set a timer for 8 minutes. Letâs see if I actually learn how to create my own NLP app in just an 8-minute readâŠ"
"Sure. Iâve set a timer for 8 minutes."
If youâre looking to build an app that understands the human voice, youâve come to the right guide. Weâre BearPeak Technology Group: Engineers who help startups start up. If the contents of this article sound daunting, whether you aren't familiar with code or don't have the time to implement this yourself, we're here to help!
Since it's our job to make you an NLP ninja today, we're going to dive right in with terms you'll need to know:
Natural Language Processing (NLP) - Interactions between computers and human language. With it, machines can understand, interpret, and generate speech. This opens up a world of possibilities for applications, like:
Email filters
Predictive text
Translators (like Google Translate)
Smart assistants (like Appleâs Siri and Amazonâs Alexa)
If you're building your own app, your goal is to make one that correctly understands language and provides the correct response, right? Sounds simple enough, but as the Oxford English Dictionary has estimated, around 170,000 English words are in current use. That means infinite, or at least unlimited, sentence combinations!
But no worries- you won't have to teach your NLP nursery rhymes or the alphabet song on repeat. NLP Models have been designed to help your app understand English fast and efficiently.
Natural Language Processing Models - The backbones of NLPs. Models are algorithms that train with vast amounts of data to learn and understand patterns, rules, and relationships within a language. Some useful tasks include:
Sentiment Analysis / Opinion Mining - Identifying emotions; analyzing and categorizing opinions expressed in text. This tool is useful for understanding client feedback by analyzing product reviews, social media posts, or survey responses.
Named Entity Recognition (NER) - Identifying structured nouns; extracting information from text. This subtask locates and classifies entities like names, locations, time, and monetary values.
Machine Translation - Just what it sounds like: AI's ability to automatically translate text from one language to another.
Vector Semantics / Embeddings - Clustering words that relate. Word embedding categorizes words so the system can understand the relationships between them. After being trained with examples, models can learn to predict where new, unfamiliar words will go. We recommend Devopedia's lesson on Word Embedding for further reading.
Natural Language Toolkit (NLTK) - A popular Python library for NLP. It provides tools and resources for tasks such as:
Tokenization - Splitting a text document into smaller units (called tokens). Tokenizers can break down a big page of unstructured text data into readable words and sentences. They will consider punctuation, special characters like question marks and exclamation points, even tweets with hashtags, mentions, and URLs.
Stemming - Identifying words with a common denominator, or word stem. The words 'builders', 'building', and 'buildable' all have the same word stem: 'build'. This preprocessing step removes data redundancy and variations on the same word.
Part-of-Speech (POS) Tagging / Grammar Tagging - Employing tokenization, word tagging identifies parts of speech: CC for conjunctions of coordinating words, JJ for adjective, VBZ for verbs, and dozens more. Check out Educba's Introduction to NLTK POS Tag for the full list and installation process.
Syntactic Parsing - Structure is assigned to the text that has been tokenized and/or POS-tagged. This way, the model can understand relationships.
Once the model knows the ins and outs of a sentence, from its structure to its parts of speech, it can better understand more search queries.
Getting Started: Should You Build or Buy?
Now that we've defined Natural Language Processing, Models, and Toolkits, how can you start building your own with them?
You'll first need to decide whether your project can be completed using a pre-made platform, or if you need to build your own. By leveraging pre-made services, you can save time and effort. However, you may need more customization than a current service can offer. Let's expand on the benefits of each method:
Pros of Buying a Pre-Made System | Pros of Building Your Own |
- Quick & Efficient | - Fully Customizable |
- Extensively Pre-Trained Models | - Domain-Specific Knowledge Integration |
- Hands-Off Updates & Maintenance | - Data Privacy & Security |
- Less Expertise Required | - Excellent Learning Experience |
- Cheaper Price | - Fine-Tune Performance Optimization |
- Pre-Planned Scalable Infrastructure | - Long-Term Flexibility |
If the benefits in the left column matter more for your project, you can select a service and jump right in! If you're looking to build your own, don't worry: We have lots of ideas to help you build the best NLP model for your team, which we'll get to in a moment.
Choosing a Pre-Made System
With the abundance of NLP services available, it is crucial to choose the right one for your specific needs. When selecting an NLP service, consider factors such as the range of supported tasks, accuracy of the models, scalability of the service, and pricing chart. Some services offer free tiers and trial periods, providing flexibility before you make a commitment. Consider the ease of integration with your existing codebase and the availability of documentation and support. Choose an NLP service that aligns with your requirements and budget.
There are pre-made services available that offer ready-to-use NLP capabilities; these provide APIs that allow developers to integrate functionalities into their applications without the need for extensive coding or model training. Some popular options include:
For the remainder of this article, we'll assume the right column above suited your needs, so you're looking to build your own NLP Model. To get started with NLTK from scratch, you first need to install it using pip (the Python package manager). Once installed, you can import the NLTK library and start exploring its functionalities.
Building & Training Your Own NLP Model
Coupling Python with NLTK provides a powerful framework for building and training custom NLP models. You'll first need to gather and preprocess a large dataset. Here's a general process you can follow:
1. Consider the Scope and Purpose - What specific problem will your app solve? Does it only need to answer frequently asked questions about an air fryer? Will it provide in-depth analysis of the user's fitness tracker data? Will it narrate e-books out loud, therefore needing to comprehend tone and context? Clarifying the needs of your final product will help you identify the types of data you'll need to collect.
2. Identify Data Sources - Think about where you'd find the most relevant text for your app. It may come from pre-organized datasets, web scraping, APIs, social media platforms, forums, blogs, or academic resources.
3. Collect Data - Now that you've identified your sources, it's time to collect! Make sure to comply with data usage and privacy regulations, as well as obtain any necessary permissions if you're working with user-generated content or sensitive information.
4. Clean and Pre-Process - Raw text data contains noise, inconsistencies, and irrelevant information. This step involves tasks that standardize the text and tokenize it into individual words or sentences. You can refer to the tools listed earlier to better understand what each tool in NLTK will do.
5. Label & Annotate - Prepare the data with accurate labels and annotations that the model will learn from. Depending on your resources, you can either manually annotate the data or use existing labeled datasets. There are several annotation tools available that can facilitate the process, such as Prodigy, Labelbox, and Doccano. Implement a system for quality control, and keep in mind that annotation might require iterations.
6. Data Splitting - Divide your annotated dataset into training, validation, and test sets. This helps evaluate the model's performance on unseen data. Divide your labeled dataset into separate subsets that serve distinct purposes during the model development and evaluation phases. The primary goal of data splitting is to assess the model's performance on unseen data, which helps avoid overfitting and provides a realistic estimate of how well the model will generalize to new inputs.
7. Data Augmentation - Improve your model's performance by artificially increasing the diversity and quantity of training data. The idea is that the model learns to be more robust and generalizes better by seeing a wider range of variations in the data. Create new, slightly modified examples from the original data while preserving the original labels. Augmentation techniques could include synonym replacement, random insertion/deletion of words in the text, text masking, random swapping, or sentence shuffling.
* A personal favorite of ours is back translation: Translate the text to another language, then back into the original language.
8. Data Versioning and Documentation - Keep track of the different versions of your datasets as they evolve over time. This is important because as you apply data augmentation or make changes to your dataset, you want to be able to recreate the exact conditions under which your model was trained. This can be more easily achieved by using a version control system like Git, clearly labeling your datasets, saving preprocessing scripts, and backing up your original data.
Take the time to properly document your data too: Create README files for each version, document metadata, include code snippets, and include any licensing restrictions on the data so others know how to use it.
Once your final dataset is ready, you can use machine learning algorithms to train your model on the data and evaluate its performance.
Machine Learning in NLP
As previously mentioned, machine learning plays a crucial role, enabling NLP model training on large datasets so they can learn patterns and make predictions. You could train models to perform tools like text classification, sentiment analysis, and named entity recognition. If you're building your own NLP model, you may want to check out some popular machine learning algorithms:
By understanding the basics of machine learning and its application in NLP, the models you create will be more powerful and accurate.
Deploying Your NLP App
It's essential to follow best practices in software development: modular design, version control, automated testing, etc. Consider scalability, security, and performance optimization. By following a systematic approach and leveraging the right tools and frameworks, you'll deploy a robust and efficient NLP application.
Alternative Approach: SpaCy
While NLTK is a powerful NLP library, there is another popular library called SpaCy that is gaining traction in the NLP community.
SpaCy is known for its speed and efficiency, making it a preferred choice for large-scale NLP applications. It provides pre-trained models for tasks such as part-of-speech tagging, named entity recognition, and dependency parsing. SpaCy also offers a user-friendly interface and supports multiple programming languages, including Python and Java.
Alternative Approach: Java
While Python is widely used for NLP development, Java also offers powerful libraries and frameworks for natural language processing. One such library is Stanford NLP, which provides a wide range of NLP tools and models.
Stanford NLP supports tasks such as tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis, as well as pre-trained models that can be easily integrated into Java applications. Consider exploring Java's capabilities so you can best leverage strengths and choose the right tool for your project.
Cloud-Based NLP Solutions
These offer a convenient and scalable way to integrate NLP functionalities into your applications. Cloud-based solutions provide APIs that can be easily accessed and integrated into your code. You can offload the computational burden of NLP tasks to the cloud, allowing your application to scale seamlessly. They also provide features like language detection, entity linking, and text summarization, expanding the capabilities of your NLP applications.
Conclusion
Today, we've explored the world of NLP models and how to utilize them for a powerful application. Here's an outline of everything you learned:
- Important terms including NLP, NLP models, and NLTK tools.
- Tasks like sentiment analysis and vector semantics.
- The benefits of buying a pre-made system vs. the pros to making your own.
- 8 guideline steps to make your own NLP model.
- How NLTK provides powerful tools and resources for NLP development.
- How machine learning plays a crucial role in training NLP models.
- The alternative approaches of using Java and SpaCy for development and the availability of NLP services and platforms.
- Finally, we discussed the importance of choosing the right NLP service for your project and what best practices to follow when building and deploying NLP apps.
Armed with this knowledge, you're ready to create your own powerful NLP app!
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