https://github.com/thunlp/multird
Code and data of the AAAI-20 paper "Multi-channel Reverse Dictionary Model"
https://github.com/thunlp/multird
nlp reverse-dictionary
Last synced: about 1 year ago
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Code and data of the AAAI-20 paper "Multi-channel Reverse Dictionary Model"
- Host: GitHub
- URL: https://github.com/thunlp/multird
- Owner: thunlp
- License: mit
- Created: 2019-11-15T13:31:02.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-10-06T13:51:40.000Z (over 5 years ago)
- Last Synced: 2025-04-03T20:37:09.441Z (about 1 year ago)
- Topics: nlp, reverse-dictionary
- Language: Python
- Homepage: https://wantwords.thunlp.org/
- Size: 138 KB
- Stars: 110
- Watchers: 7
- Forks: 23
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MultiRD
Code and data of the AAAI-20 paper "**Multi-channel Reverse Dictionary Model**" [[pdf](https://arxiv.org/pdf/1912.08441.pdf)]
## Requirements
* Python 3.x
* Pytorch 1.x
* Other requirements: numpy, tqdm, nltk, gensim, thulac
## Quick Start
Download the code and data from [Google Drive](https://drive.google.com/drive/folders/1jeyPE8iGdGUSVJe_6Smr_NzoWfR52f4g?usp=sharing) or [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/ec29131d38fd4ca2a6ca/), where the code is the same as that here.
Unzip the data.zip (under English and Chinese paths respectively), and all files under `EnglishReverseDictionary` and `ChineseReverseDictionary` should be prepared as follows:
```
ReverseDictionary
|- EnglishReverseDictionary
| |- data
| | |- data_train.json
| | |- data_dev.json
| | |- data_test_500_rand1_seen.json
| | |- data_test_500_rand1_unseen.json
| | |- data_defi_c.json [definitions of the target words in 200 descriptions]
| | |- data_desc_c.json [testset of 200 descriptions]
| | |- vec_inuse.json [Only embeddings used in this model are included.]
| | |- lexname_all.txt
| | |- root_affix_freq.txt
| | |- sememes_all.txt
| | |- target_words.txt
| |- code
| |- main.py
| |- model.py
| |- data.py
| |- evaluate.py
| |- evaluate_result.py
| |- analyse_result.py
| |- result_analysis_En_1200.py
|- ChineseReverseDictionary
| |- data
| | |- Cx.json [x=1,2,3,4]
| | |- description_sense.json [train & dev dataset]
| | |- description_idio_locu.json [testset of Question]
| | |- description_byHand.json [testset of description]
| | |- hownet.json
| | |- sememe.json
| | |- word_cilinClass.json
| | |- word_index.json
| | |- word_vector.npy [Only embeddings used in this model are included.]
| |- code
| |- main.py
| |- model.py
| |- data.py
| |- evaluate.py
| |- evaluate_result.py
|- PrepareYourOwnDataset
|-
```
### Train English Model
Execute this command under code path：
```bash
python main.py -b [batch_size] -e [epoch_num] -g [gpu_num] -sd [random_seed] -f [freq_mor] -m [rsl, r, s, l, b] -v
```
In `-m [rsl, r, s, l, b]`,
- `-m r` indicates the use of Morpheme information including roots and affixes. You can filter morphemes by `-f`, usually 15~35;
- `-m s` means using the Sememe predictor;
- `-m l` means using WordNet lexnames, which is word category information (include Lexical name and POS tag information);
- `-m b` means not using any other information, just the basic BiLSTM model;
- `-m rsl` means to use all information which is our Multi-channel model;
`-e` is usually set to 10~20;
`-g` indicates which GPU to use;
`-v` means showing progess bar.
After training, you will get two new files, `xxx_label_list.json` and `xxx_pred_list.json`. "xxx" indicates the mode you set in `-m`, e.g., the `-m rsl` setting indicates that the file will be `rsl_label_list.json`.
#### Evaluation
Execute this command under code path:
```bash
python evaluate_result.py -m [mode]
```
Here, `mode` is the same as above.
Then you'll get `median rank`, ` accuracy@1/10/100` and `rank variance` results on 3 test sets including **seen**, **unseen** and **description**.
You can evaluate model performance with prior knowledge:
```bash
python analyse_result.py
python result_analysis_En_1200.py -m [mode]
```
### Train Chinese Model
Execute this command under code path：
```bash
python main.py -b [batch_size] -e [epoch_num] -g [gpu_num] -sd [random_seed] -u/-s -m [CPsc, C, P, s, c, b] -v
```
Different from English model training, we use `-u` or `-s` to represent **Unseen** or **Seen** test mode. In fact, there is no need to use the test mode on the Seen Definition test set.
In `-m [CPsc, C, P, s, c, b]`
- `-m C` means using Cilin word category information and we use 4 word classes in Cilin;
- `-m P` means using POS predictor;
- `-m s` means using Sememe predictor;
- `-m c` indicates the use of Morpheme predictor where morphemes are Chinese characters;
- `-m b` means not using any other information, just the basic BiLSTM model;
- `-m CPsc` means to use all information as our Multi-channel model.
`-e` , `-g` and `-v` are the same as those in English model training.
#### Evaluation
```bash
python evaluate_result.py -m [mode]
```
Here, the `mode` is the prefix of `xxx_label_list.json`.
Then you'll get `median rank`, ` accuracy@1/10/100` and `rank variance` results on 4 test sets including **seen**, **unseen**, **Description** and **Question**.
You can evaluate model performance with prior knowledge:
```bash
python result_analysis_Ch.py -m [mode]
```
## Prepare Your Own Data
Here is some code for reference. The data format is shown below, and you can build your own data set.
```
ReverseDictionary
|- EnglishReverseDictionary
|- ChineseReverseDictionary
|- PrepareYourOwnDataset
|- proc_allFeatures.py
|- get_wordnet_lexname.py
|- get_wordnet_500sample.py
|- process_googleVec_checkAllData.py
|- readHowNet_to_word_sememe.py
|- wordnik_get_defi.py
|- check_root_affix.py
```
### Data Formats
It is json format in data_xxx.json files.
```
{
"word": "fatalism",
"lexnames": [
"noun.cognition"
],
"root_affix": [
"fatal",
"ism"
],
"sememes": [
"knowledge",
"believe",
"experience",
"Fate"
],
"definitions": "the doctrine that all events are predetermined by fate and are therefore unalterable"
}
```
Word embeddings are in `vec_inuse.json` which contains all target words and words in definitions. Only used words are included. The format is `{word: [vector]}`, ....
`lexname_all.txt` contains all 45 lexnames from WordNet.
`sememes_all.txt` contains 1400 sememes from HowNet.
Morphemes (root and affix) are in `root_affix_freq.txt`, which contains morphemes and their numbers, separated by spaces.
### Download and Process Data
In English experiments, we use the Description dataset from [(Hill et al. 2016)](https://arxiv.org/pdf/1504.00548.pdf).
Word embeddings are from [GoogleNews-vectors-negative300](https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing).
Sememes can be obtained using [OpenHowNet](https://github.com/thunlp/OpenHowNet).
Lexnames are from WordNet which you can get them easily by NLTK.
We get morphemes by [Morfessor tool](https://morfessor.readthedocs.io/en/latest/). The used dataset is from [morpho.aalto.fi](http://morpho.aalto.fi/events/morphochallenge2010/datasets.shtml). You should train mofessor model first, and then use it to process the target words to get the corresponding roots and affixes.
```bash
morfessor-train --encoding=ISO_8859-15 --traindata-list --logfile=log.log -s model.bin -d ones wordlist-2010.eng
morfessor-segment -l ../morfessor_data/model.bin target_words.txt -o word_root_affix.txt
```
Unfortunately, the morphemes obtained by this method are not accurate. It is recommended that you use the standard root-affix dictionary.
## Cite
If you use any code or data, please cite this paper
```
@article{zhang2019multi
title={Multi-channel Reverse Dictionary Model},
author={Zhang, Lei and Qi, Fanchao and Liu, Zhiyuan and Wang, Yasheng and Liu, Qun and Sun, Maosong},
journal={arXiv preprint arXiv:1912.08441},
year={2019}
}
```
## Contact
You can visit our [online reverse dictionary website](https://wantwords.thunlp.org/), where we have optimized our methods and datasets. Github [WantWords](https://github.com/thunlp/WantWords). You can post issues if you have any questions.