We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| abefrandsen/syntactic-rand-walk | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| chunker | Ran | abefrandsen/syntactic-rand-walk/train_from_triplecounts.py pointer only (licence: NONE) · get_code("ec7977b6ec96dce1") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Word embedding is a powerful tool in natural language processing. In this paper we consider the problem of word embedding composition \--- given vector representations of two words, compute a vector for the entire phrase. We give a generative model that can capture specific syntactic relations between words. Under our model, we prove that the correlations between three words (measured by their PMI) form a tensor that has an approximate low rank Tucker decomposition. The result of the Tucker decomposition gives the word embeddings as well as a core tensor, which can be used to produce better compositions of the word embeddings. We also complement our theoretical results with experiments that verify our assumptions, and demonstrate the effectiveness of the new composition method.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1902.00613")
get_code_for_paper("1902.00613")
have("1902.00613")
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