Other Popular Tools for NLP
Beyond the libraries and frameworks used for building models, that we reviewed in previous chapter — there are many helpful tools that developers and researchers can use to work with text data. This chapter introduces other popular tools for NLP, especially cloud-based APIs, open-source datasets, and pretrained model platforms.
Cloud-Based NLP APIs
Cloud-based services offer easy-to-use NLP tools that do not require training your own models. These APIs allow developers to send text to a remote server and get back useful information. The main providers in this space are Google Cloud, Microsoft Azure, and Amazon Web Services (AWS).
Google Cloud Natural Language API helps developers analyze text in several ways. It supports features such as entity recognition (people, locations, organizations), sentiment analysis (positive or negative tone), syntax analysis (grammar structure), and content classification. For example, you can submit a customer review, and the API will tell you whether it is positive or negative, and which products or companies are mentioned. Google’s service supports many languages and is useful in multilingual applications.
Microsoft Azure Text Analytics offers similar capabilities. It detects sentiment, extracts key phrases, recognizes named entities, and can identify the language of the text. Azure is often chosen by organizations already using Microsoft products, since it integrates well with tools like Power BI and Microsoft Dynamics.
AWS Comprehend, part of Amazon Web Services, is another powerful NLP tool. Like the others, it can recognize entities, analyze sentiment, and extract key phrases. One feature that makes AWS Comprehend unique is topic modeling, which identifies common themes across a collection of documents. AWS also provides Comprehend Medical, a specialized tool for analyzing healthcare text such as doctor notes and medical reports. It connects easily with other AWS tools like S3, Lambda, and Redshift, making it suitable for large-scale, cloud-based workflows.
To summarize the main features of these three providers:
Google Cloud: Strong multilingual support, content classification.
Microsoft Azure: Easy Microsoft integration, key phrase extraction.
AWS Comprehend: Topic modeling, medical text support, deep AWS integration.
These APIs save time and resources, especially for teams that need fast results without building their own models. However, they may charge based on usage and can raise privacy concerns when sending sensitive text to external servers.
Open-Source NLP Datasets
To train and evaluate NLP models, high-quality datasets are essential. Over time, many open-source datasets have been developed by researchers and shared freely with the community. These datasets cover a wide range of NLP tasks, including sentiment analysis, translation, question answering, summarization, and entity recognition.
For sentiment analysis, the IMDB dataset is widely used. It contains movie reviews labeled as either positive or negative. Another important dataset is CoNLL-2003, which is used for named entity recognition. It includes English sentences where words are marked as names of people, organizations, or locations. For machine translation tasks, WMT datasets provide large collections of translated sentences in multiple language pairs.
Many datasets can be downloaded from platforms like Hugging Face Datasets, Kaggle, or academic websites. These datasets make it possible to compare new models against standard benchmarks and improve their performance.
Pretrained Models and Model Hubs
Another useful resource is the growing number of pretrained models. These are models already trained on large amounts of text and shared by the community or companies. Developers can use them as-is or fine-tune them on their own data.
One of the most popular platforms is the Hugging Face Model Hub. It offers thousands of models for tasks such as translation, summarization, classification, and question answering. These models are based on modern architectures like BERT, RoBERTa, and GPT.
Using pretrained models saves time, reduces the need for powerful hardware, and improves results —especially when the dataset is small or the timeline is short.
Bridging the Gap: Using ONNX and External Inference Services
While PHP cannot run machine learning models directly, developers can use ONNX (Open Neural Network Exchange) to convert and serve pre-trained NLP models using a language like Python, C++, or JavaScript. ONNX makes it possible to deploy models in a lightweight and portable format, allowing developers to expose inference as a web service.
For example, a BERT model trained in PyTorch can be converted to ONNX and then served using ONNX Runtime in a Python-based API. The PHP application can then call this API to perform tasks like sentiment analysis, entity recognition, or summarization.
This approach allows PHP to benefit from advanced models without needing native support for deep learning libraries. It also makes model deployment more efficient and language-agnostic. For example it can be used with TransformersPHP.
Summary
Today’s NLP tools go far beyond traditional code libraries. Cloud APIs like Google Cloud Natural Language, Microsoft Azure Text Analytics, and AWS Comprehend allow developers to analyze text with just a few lines of code. Open-source datasets provide the material needed to train and test new models. And model hubs such as Hugging Face make it easy to use high-quality pretrained models.
By combining these tools with core NLP skills, developers can build intelligent systems that understand human language in a fast, reliable, and scalable way.
Last updated