zjunlp/SPEECH
[ACL 2023] SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres
This project helps you automatically identify and categorize real-world events mentioned in documents, and then discover the relationships between them. For example, it can find all mentions of "product launch" and then determine if one launch caused another, or if they happened simultaneously. It takes unstructured text documents as input and outputs structured information about events and their temporal, causal, or sub-event relationships. This is useful for researchers or analysts working with large volumes of text data who need to extract and understand complex event structures.
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Use this if you need to automatically extract and understand the complex relationships between events mentioned across multiple documents, such as identifying causes and effects, or sequences of events.
Not ideal if you're looking for a simple keyword extraction tool or if your primary need is to classify the sentiment of text rather than the structure of events.
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Language
Python
License
MIT
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Last pushed
Dec 22, 2023
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