Welcome to your guide on Open-SESAME, a frame-semantic parser designed to automatically detect FrameNet frames and their respective frame-elements from sentences. In this article, we will walk you through the installation process, data preprocessing, training, and testing your models. Let’s get started!

Installation

To begin using Open-SESAME, you’ll need to ensure your environment is set up correctly. This project requires Python version 3.7.9 and the DyNet library, along with some handy tools from NLTK. Here’s how to get everything you need:

• Clone the repository:

sh$ git clone https://github.com/swabhs/open-sesame.git
sh$ cd open-sesame

• Install required packages:

sh$ pip install dynet==2.0.3
sh$ pip install nltk==3.5
sh$ python -m nltk.downloader averaged_perceptron_tagger wordnet

Data Preprocessing

Next, let’s jump into data preprocessing. This project is configured to handle XML data based on FrameNet. Here are the steps to prepare your data:

1. Create a directory for your data, $DATA, and place your extracted FrameNet version 1.7 data inside $DATA/fndata-1.7.
2. Optionally, download the pretrained GloVe word embeddings and extract them under $DATA/embeddings_glove.
3. If you’d like to modify configurations, edit configurations/global_config.json to suit your needs.
4. Preprocess the data using:

sh$ python -m sesame.preprocess

Training Your Model

The training process involves three key steps: target identification, frame identification, and argument identification. Each step is trained independently. Here’s how to do it:

To train a model, execute the following command:

sh$ python -m sesame.$MODEL --mode train --model_name $MODEL_NAME

Testing Your Model

After training, you’ll want to test your model. The testing process requires executing different models in a specified order:

• Target Identification
• Frame Identification
• Argument (Frame-Element) Identification

To test under a given model, use:

sh$ python -m sesame.$MODEL --mode test --model_name $MODEL_NAME

The output will be available in a CoNLL 2009-like format for ease of interpretation.

Understanding the Code: An Analogy

Imagine you are a chef trying to prepare a delicious multi-course meal (the various stages of semantic parsing). To make this meal, you need specific ingredients (data), which you gather from a well-organized pantry (your data structure in the repository). You would follow a recipe (your code) that tells you how to mix these ingredients step by step (the separate training stages for identifying targets, frames, and arguments). Each step builds upon a foundation laid by the previous one, ensuring everything comes together smoothly and effectively at the end. Just as a meal needs to be completed in the right order and with the right proportions, training your model requires structured steps to achieve optimal performance.

Troubleshooting

If you encounter issues during installation or running the models, here are a few troubleshooting tips:

• Ensure all dependencies are correctly installed using the specified versions.
• Double-check your data paths and configurations in global_config.json.
• Keep an eye on the log files produced during preprocessing to identify any data issues.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.