Sabrina Mielke, Suzanna Sia, Ayush Dalmia
We lifted 2 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| adalmia96/Cluster-Analysis | — | 2 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| Agglo_model | Ran | adalmia96/Cluster-Analysis/code/clustering.py pointer only (licence: NONE) · get_code("7200461c5e224001") |
| find_words_for_cluster | Ran | adalmia96/Cluster-Analysis/code/clustering.py pointer only (licence: NONE) · get_code("9de0b7bf06d142cf") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Topic models are a useful analysis tool to uncover the underlying themes within document collections. The dominant approach is to use probabilistic topic models that posit a generative story, but in this paper we propose an alternative way to obtain topics: clustering pretrained word embeddings while incorporating document information for weighted clustering and reranking top words. We provide benchmarks for the combination of different word embeddings and clustering algorithms, and analyse their performance under dimensionality reduction with PCA. The best performing combination for our approach performs as well as classical topic models, but with lower runtime and computational complexity.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2004.14914")
get_code_for_paper("2004.14914")
have("2004.14914")
Connect an agent — have() is free.