Extracting Meaning from Natural Language

Building Conversational AI with Snips NLU: Extracting Meaning from Natural Language

Are you interested in building chatbots or voice assistants? Snips NLU, an open source Python library, could be your go-to tool for extracting structured information from natural language sentences. In this article, we will explore the ins and outs of Snips NLU and how it can revolutionize the way you develop conversational AI systems.

Snips NLU is built upon the concept of Natural Language Understanding (NLU). It enables you to translate user’s words into a machine-readable description of their intent and extract relevant parameters. With a properly trained Snips NLU engine, you can accurately detect user intents and take appropriate actions or responses.

To illustrate the power of Snips NLU, let’s consider an example. Imagine a user asking, “What will be the weather in Paris at 9pm?” Snips NLU can extract the intent “searchWeatherForecast” and the slots “paris” and “2018-02-08 20:00:00 +00:00”. Not only does Snips NLU extract entities, but it also resolves them, providing useful information in a convenient ISO format.

Getting started with Snips NLU is straightforward. First, ensure you meet the system requirements, including Python 2.7 or Python >= 3.5 and sufficient RAM. Then, install Snips NLU using the pip package manager. You can choose to install pre-built binaries for Mac, Linux, and Windows, or install from source distribution.

To use Snips NLU, you need to download language resources specific to the language you intend to process. A simple command can fetch the necessary resources for your chosen language, ensuring accurate and efficient processing.

Once you have the library set up, you can start utilizing the Snips NLU API. Snips NLU provides a command line interface, which is the easiest way to test and interact with the library. Train your NLU engine using sample datasets, and then parse sentences interactively to see how well it understands the user’s intent and extracts relevant slots.

To give you a taste of what Snips NLU can do, here’s a sample code snippet:

“`python
from future import unicode_literals, print_function
import io
import json
from snips_nlu import SnipsNLUEngine
from snips_nlu.default_configs import CONFIG_EN

with io.open(“sample_datasets/lights_dataset.json”) as f:
sample_dataset = json.load(f)

nlu_engine = SnipsNLUEngine(config=CONFIG_EN)
nlu_engine = nlu_engine.fit(sample_dataset)
text = “Please turn the light on in the kitchen”
parsing = nlu_engine.parse(text)
print(parsing[“intent”][“intentName”]) # Output: ‘turnLightOn’
“`

Snips NLU also provides several sample datasets that you can use to train your NLU engine. The datasets cover a range of scenarios, such as controlling lights, preparing beverages, and booking flights.

To evaluate the performance of Snips NLU, benchmark studies have been conducted comparing it with other popular NLU engines, including Dialogflow, Luis.ai, IBM Watson, and Rasa NLU. The results show that Snips NLU delivers competitive intent classification and slot filling accuracy, making it a reliable choice for building conversational AI systems.

To get the most out of Snips NLU, refer to its comprehensive documentation. It provides a step-by-step guide on setting up and using the library effectively. Additionally, join the Snips NLU forum to ask questions, share experiences, and get feedback from the community.

In conclusion, Snips NLU provides the necessary tools for building powerful and accurate conversational AI systems. With its easy-to-use API, extensive language support, and strong performance, Snips NLU is a reliable choice for extracting meaning from natural language.

Give Snips NLU a try and witness the transformation it can bring to your conversational AI projects.

References:
– Snips NLU Documentation: Link
– Snips NLU GitHub Repository: Link
– Snips NLU Forum: Link
– Snips NLU Benchmark Results: Link