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https://github.com/HKUST-KnowComp/ComHyper

Code for EMNLP'20 paper "When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models"
https://github.com/HKUST-KnowComp/ComHyper

conceptualization hypernymy-detection lexical-semantics semantic-relations

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Code for EMNLP'20 paper "When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models"

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README 

        
# ComHyper [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)



Code for EMNLP'20 paper "When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models" ([arXiv](https://arxiv.org/abs/2010.04941v1))



In a nutshell, ComHyper is the complementary framework for solving hypernymy detection tasks from the perspective of blind points of Hearst  pattern-based methods.  As shown in the left Figure,  long-tailed nouns cannot well covered by Hearst patterns and thus form non-negligible sparsity types.  For such cases, we propose to use supervised distributional models for complmenting pattern-based models shown in the right Figure. 






## Use ComHyper



### 1. Download Hearst pattern files and corpus.



First prepare the extracted Hearst pattern pairs such as `hearst_counts.txt.gz` from the repo [hypernymysuite](https://github.com/facebookresearch/hypernymysuite)  or `data-concept.zip` from Microsoft Concept Graph (Also known as [Probase](https://concept.research.microsoft.com/Home/Download)).  Specify the parameter `pattern_filename` in the `config` as the file location. 



```

wget https://github.com/facebookresearch/hypernymysuite/blob/master/hearst_counts.txt.gz

curl -L "https://concept.research.microsoft.com/Home/StartDownload" > data-concept.zip

```



Then extract the contexts for words from large-scale corpus such as Wiki + Gigaword or ukWac.  All the contexts for one word should be organized into one `txt` file and one line for one context.  



For those words appearing in the Hearst patterns (**IP words**),  organize their context files into the directory `context` in the `config`.  For **OOP words**,  organize their context files into the `context_oov`  in the `config`. 



### 2. Train and evaluate the ComHyper. 



For training the distributional models supervsied by the output of pattern-based models,  different context encoders are provided: 



```console

python train_word2score.py config/word.cfg

python train_context2score.py config/context.cfg

python train_bert2score.py config/bert.cfg

```



The same evaluation scripts work for all settings.  For reproducing the results, run: 



```console

python evaluation/evaluation_all_context.py ../config/context.cfg 

```



Note that we choose not to report the `BERT` encoder results in our orginial paper due to efficiency but release the relevant codes for incoroporating effective pre-trained contextualized encoders to further improve the performance. Welcome to PR or contact cyuaq # cse.ust.hk  ! 



## Citation



Please cite the following paper if you found our method helpful. Thanks !



```

@inproceedings{yu-etal-2020-hearst,

    title = "When Hearst Is Not Enough: Improving Hypernymy Detection from Corpus with Distributional Models",

    author = "Yu, Changlong and Han, Jialong and Wang, Peifeng and Song, Yangqiu and Zhang, Hongming and Ng, Wilfred and Shi, Shuming",

    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",

    month = "nov",

    year = "2020",

    address = "Online",

    publisher = "Association for Computational Linguistics",

    url = "https://www.aclweb.org/anthology/2020.emnlp-main.502",

    pages = "6208--6217",

}

```