From Rules to Neural Graphs: Scalable Structured Prediction for Patent Prior Art Search
Patent search requires processing documents routinely exceeding tens of thousands of tokens. Most neural retrieval approaches operate on truncated inputs, limiting their effectiveness. Graph-based retrieval addresses this by representing each patent as a structured invention graph, but constructing these graphs relies on brittle rule-based parsers. We present the neural parser, which adapts biaffine attention from dependency parsing to predict invention graphs directly from patent text. Our local biaffine attention restricts pairwise scoring to a sliding window, reducing complexity from $O(n^2)$ to $O(n \cdot w)$. Since local and global scoring share the same weights, the model trains on ...
| Authors | Nikolai Zenovkin, Sebastian Björkqvist |
|---|---|
| Primary category | cs.IR |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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