ROOT13: Spotting Hypernyms, Co-Hyponyms and Randoms
Authors: Enrico Santus, Alessandro Lenci, Tin-Shing Chiu, Qin Lu, Chu-Ren Huang
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate it with a 10-fold cross validation on 9,600 pairs, equally distributed among the three classes and involving several Parts-Of Speech (i.e. adjectives, nouns and verbs). When all the classes are present, ROOT13 achieves an F1 score of 88.3%, against a baseline of 57.6% (vector cosine). |
| Researcher Affiliation | Academia | * The Hong Kong Polytechnic University, Hong Kong esantus@gmail.com, cstschiu@comp.polyu.edu.hk, {qin.lu, churen.huang}@polyu.edu.hk University of Pisa, Italy alessandro.lenci@ling.unipi.it |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found in the paper. |
| Open Source Code | Yes | More info: https://github.com/esantus/. |
| Open Datasets | Yes | We have used 9,600 pairs, randomly extracted from three datasets: Lenci/Benotto (Santus et al., 2014b), BLESS (Baroni and Lenci, 2011) and EVALution (Santus et al., 2015). |
| Dataset Splits | Yes | We evaluate it with a 10-fold cross validation on 9,600 pairs, equally distributed among the three classes and involving several Parts-Of Speech (i.e. adjectives, nouns and verbs). |
| Hardware Specification | No | No specific hardware details (like CPU/GPU models, memory, or specific computer specifications) used for running the experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions 'Weka' for the Random Forest algorithm but does not specify its version number or any other software dependencies with version details. |
| Experiment Setup | Yes | ROOT13 uses the Random Forest algorithm implemented in Weka (Breiman, 2001), with the default settings. ... All the features are normalized in the range 0-1: ... a window-based Vector Space Model (VSM), built on a combination of uk Wa C and Wa Ckypedia corpora (around 2.7 billion words) and recording word co-occurrences within the 5 nearest content words to the left and right of each target. |